# Regression Analysis

Chapter 4

Regression Analysis

Data analysts use regressions to determine if relationships exists between variables……

In a simple linear regression we test whether measured values of the dependent variable (on the Y axis) vary with provided independent variable (on the X axis)

Does an increase in advertising (X) coincide to an increase in sales (Y)….?

Can more training hours (X) leads to decreased scrap (Y)….?

Will more spending on HR benefits (X) prompt an increase in employee retention (Y)…?

We use sample data (x,y) and sample y intercept

X

Y

X

Y

X

Y

When performing regressions there are 3 rules we must follow:

(Rule 1) Do not predict values far beyond the data we are working with

In the example below we see a linear relationship between X and Y.

What is the predicted Y value at X=12?

In this case the relationship changed (from linear to curvilinear) when x exceeded 6.

Conclusion: we can only apply extrapolate values near the test range

When performing regressions there are 3 rules we must follow:

(Rule 2) Data deviations from the predicted line are assumed to be random

The data points ( ) are randomly scattered around the regression line. Meaning there is not an underlying influence on Y values other than the X values we are considering

When performing regressions there are 3 rules we must follow:

(Rule 3) Variables X and Y are normally distributed

Y

X

Regression line

How do we determine if our data is normally distributed?

To test data for skewsness we use the formula =SKEW(). If SKEW value is between -1 (negative skew) and +1 (positive skew) we can say the data is normal in X

To test data for kurtosis we use the formula =KURT(). If the KURT values are between -1 (flat) and +1 (peaked) we can say the data is normal in Y

In this example the data X and Y are normally distributed because SKEW and KURT values are all between +1 and -1.

 X Y 8 200 2 230 7 220 3 210 7 240 6 200 4 210 9 230 6 216 SKEW -0.41576 0.268996 KURTOSIS -0.86776 -0.99992

Now that we know the rules of regression lets try one…

We start by enabling Excel Add-ins

In Excel 2010 and later go to File > Options

22

1. Click this

2. Click this

23

3. Check these

4. Click this.

5. Click “Data”. Now you should be able to see these.

24

1. On Data tab

2. Select Data Analysis

3. Select Regression

4. Click OK

5. Click to select D3:D10

6. Click to select C3:C10

7. Click as 1st row of X & Y are labels

8. Click to make plot

What does all this mean???

Start by looking at Significance F. If F is < .05, there is < 5% chance of incorrectly accepting a regression exists. In other words, there is >95% chance of a regression existing. At F < .05 we accept the regression.

Next we look at R square (i.e. r2)

The F <5% means a regression exists and r2 = 0.8 that it is strong; we can now look to coefficients to find x slope and y intercept of the regression line

Are the regression coefficients significant?

The P values of y intercept (.0029) and slope (.006) are less than .05. So….

There is < 5% chance of incorrectly accepting these coefficients. In other words, there is >95% chance of a regression existing with these coefficients.

Let’s try another…

Determine if a relationship exists between how much Triple A Construction Co. sells and how much it pays in payroll.

The X and Y data are normally distributed so we can test for a regression

1. On Data tab

2. Select Data Analysis

3. Select regression

4. Click OK

5. Click to select D8:D14

6. Click to select E8:E14

7. Click as 1st row of X & Y are labels

We look at the Significance F

With a correlation coefficient (r) of .69, the regression is moderate.

With an intercept p value of .3, we cannot accept this value at 95% confidence. We need to consider standard error.

What does standard error mean?

The Standard Errors are errors associated with regression coefficients. Think of it standard deviation of coefficients.

Is it possible when we collected sales and payroll numbers, there were external factors we didn’t control that affected results (such as years service, or employee performance ratings, or economy strength)?

From the “residual plots” we can see

Residual error is on the vertical axis. The independent variable on the horizontal axis.

Since the points in this example are randomly scattered around the horizontal axis (sum approximately to 0), we can reject external factors and accept a single variable linear regression.

Payroll (X) Residual Plot

3 4 6 4 2 5 0.25 1 -0.5 -2 0 1.25Payroll (X)

Residuals

A multiple regression model allows us to predict an output value Y using multiple independent variables X1, X2 ….

1. On Data tab

2. Select Data Analysis

3. Select regression

4. Click OK

5. Click to select B4:B18

6. Click to select C4:D18

7. Click as 1st row of X1, X2 & Y are labels

We look at Significance F

The r2=0.67 tells us the linear regression explains 67% of the variance in the dependent variable (i.e. house selling price). So, we have a moderately strong model.

Since the p-values for square feet (.0013) and age (.0039) are both below .05, square feet and age can both be used to predict price

A non-significant P value (>.05) would have told us the variable does not have predictive capability in the presence of the other; so we would have removed it and refit the model without it.

P values shouldn’t be used to eliminate more than one variable at a time

Why? Because a variable that doesn’t have predictive capability in the presence of other variables may have predictive capability when some of those variables are removed from the model.

What do t values tell us?

In multiple linear regression, the absolute size of the coefficient for each independent variable gives you the size of the effect that variable is having on your dependent variable, and the sign on the coefficient (positive or negative) gives you the direction of the effect.

In our case square feet (t=4.26) has a bigger effect on house price than age (t=3.64)

As additional variables are added to a multiple regression equation, R² increases even when the new variables have no real predictive capability.

When variables are added and adjusted R² doesn’t increase the new variables do not improve predictive capability.

Is it possible when we collected house price, house age and square footage, there were external factors we didn’t control that affected price (such as school district, builder, or taxes)?

From the “residual plots” we can see

The points are randomly dispersed around the horizontal axis for both square feet and age; we can reject external factors are impacting our age and square feet multiple regression with house price

Square feet Residual Plot

1926 2069 1720 1396 1706 1847 1950 2323 2285 3752 2300 2525 3800 1740 -49066.406014619977 -2345.7129556209984 -10239.616517664399 -29322.435475499602 14171.227106612627 27584.813911759818 5185.8704494942504 3537.3054888200131 10607.696428610972 -26587.137991958123 4760.9159575959784 3002.8722506209742 17802.96365005814 30907.643711790413Square feet

Residuals

Age Residual Plot

30 40 30 15 32 38 27 30 26 35 18 17 40 12 -49066.406014619977 -2345.7129556209984 -10239.616517664399 -29322.435475499602 14171.227106612627 27584.813911759818 5185.8704494942504 3537.3054888200131 10607.696428610972 -26587.137991958123 4760.9159575959784 3002.8722506209742 17802.96365005814 30907.643711790413Age

Residuals

When we do linear regressions, there are certain assumptions we make….

Sample sizes are large enough (>30) the t distributions approximates normal distributions

Correlation does not equal causality

An action or occurrence can cause another (such as smoking causes lung cancer), or it can correlate with another (such as smoking is correlated with high alcohol consumption). If one action causes another, then they are most certainly correlated. But just because two things occur together does not mean that one caused the other, even if it seems to make sense.

SUMMARY OUTPUT

Regression Statistics

Multiple R0.894909611

R Square0.800863211

Standard Error12.43238858

Observations7

ANOVA

dfSSMSFSignificance F

Regression13108.0357143108.03571420.108370.006493257

Residual5772.8214286154.5642857

Total63880.857143

CoefficientsStandard Errort StatP-valueLower 95%Upper 95%Lower 95.0%Upper 95.0%

Intercept56.7142857110.50728615.3976150610.00294829.7044469283.7241245129.7044469283.72412451

Time Period10.535714292.3495005984.4842356260.0064934.49613072516.575297854.49613072516.57529785

## Sheet1

 Time Period Electrical Demand 2001 1 74 2002 2 79 2003 3 80 2004 4 90 2005 5 105 2005 6 142 2007 7 122 SUMMARY OUTPUT Regression Statistics Multiple R 0.8949096107 R Square 0.8008632114 Adjusted R Square 0.7610358536 Standard Error 12.4323885764 Observations 7 ANOVA df SS MS F Significance F Regression 1 3108.0357142857 3108.0357142857 20.1083691483 0.0064932569 Residual 5 772.8214285714 154.5642857143 Total 6 3880.8571428571 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 56.7142857143 10.5072861018 5.3976150611 0.0029479517 29.7044469192 83.7241245093 29.7044469192 83.7241245093 Time Period 10.5357142857 2.3495005983 4.4842356259 0.0064932569 4.496130725 16.5752978464 4.496130725 16.5752978464 RESIDUAL OUTPUT Observation Predicted Electrical Demand Residuals 1 67.25 6.75 2 77.7857142857 1.2142857143 3 88.3214285714 -8.3214285714 4 98.8571428571 -8.8571428571 5 109.3928571429 -4.3928571429 6 119.9285714286 22.0714285714 7 130.4642857143 -8.4642857143

Time Period Line Fit Plot

Electrical Demand 1 2 3 4 5 6 7 74 79 80 90 105 142 122 Predicted Electrical Demand 1 2 3 4 5 6 7 67.25 77.785714285714292 88.321428571428584 98.857142857142861 109.39285714285714 119.92857142857143 130.46428571428572Time Period

Electrical Demand

## Sheet1

 Time Period Electrical Demand 2001 1 74 2002 2 79 2003 3 80 2004 4 90 2005 5 105 2005 6 142 2007 7 122 SUMMARY OUTPUT Regression Statistics Multiple R 0.8949096107 R Square 0.8008632114 Adjusted R Square 0.7610358536 Standard Error 12.4323885764 Observations 7 ANOVA df SS MS F Significance F Regression 1 3108.0357142857 3108.0357142857 20.1083691483 0.0064932569 Residual 5 772.8214285714 154.5642857143 Total 6 3880.8571428571 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 56.7142857143 10.5072861018 5.3976150611 0.0029479517 29.7044469192 83.7241245093 29.7044469192 83.7241245093 Time Period 10.5357142857 2.3495005983 4.4842356259 0.0064932569 4.496130725 16.5752978464 4.496130725 16.5752978464 RESIDUAL OUTPUT Observation Predicted Electrical Demand Residuals 1 67.25 6.75 2 77.7857142857 1.2142857143 3 88.3214285714 -8.3214285714 4 98.8571428571 -8.8571428571 5 109.3928571429 -4.3928571429 6 119.9285714286 22.0714285714 7 130.4642857143 -8.4642857143

Time Period Line Fit Plot

Electrical Demand 1 2 3 4 5 6 7 74 79 80 90 105 142 122 Predicted Electrical Demand 1 2 3 4 5 6 7 67.25 77.785714285714292 88.321428571428584 98.857142857142861 109.39285714285714 119.92857142857143 130.46428571428572Time Period

Electrical Demand

## Sheet1

 Time Period Electrical Demand 2001 1 74 2002 2 79 2003 3 80 2004 4 90 2005 5 105 2005 6 142 2007 7 122 SUMMARY OUTPUT Regression Statistics Multiple R 0.8949096107 R Square 0.8008632114 Adjusted R Square 0.7610358536 Standard Error 12.4323885764 Observations 7 ANOVA df SS MS F Significance F Regression 1 3108.0357142857 3108.0357142857 20.1083691483 0.0064932569 Residual 5 772.8214285714 154.5642857143 Total 6 3880.8571428571 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 56.7142857143 10.5072861018 5.3976150611 0.0029479517 29.7044469192 83.7241245093 29.7044469192 83.7241245093 Time Period 10.5357142857 2.3495005983 4.4842356259 0.0064932569 4.496130725 16.5752978464 4.496130725 16.5752978464 RESIDUAL OUTPUT Observation Predicted Electrical Demand Residuals 1 67.25 6.75 2 77.7857142857 1.2142857143 3 88.3214285714 -8.3214285714 4 98.8571428571 -8.8571428571 5 109.3928571429 -4.3928571429 6 119.9285714286 22.0714285714 7 130.4642857143 -8.4642857143

Time Period Line Fit Plot

Electrical Demand 1 2 3 4 5 6 7 74 79 80 90 105 142 122 Predicted Electrical Demand 1 2 3 4 5 6 7 67.25 77.785714285714292 88.321428571428584 98.857142857142861 109.39285714285714 119.92857142857143 130.46428571428572Time Period

Electrical Demand

## Sheet3

SUMMARY OUTPUT

Regression Statistics

Multiple R0.833333333

R Square0.694444444

Standard Error1.31101106

Observations6

ANOVA

dfSSMSFSignificance F

Regression115.62515.6259.0909090.039351852

Residual46.8751.71875

Total522.5

CoefficientsStandard Errort StatP-valueLower 95%Upper 95%Lower 95.0%Upper 95.0%

Intercept21.7425436391.1477470.31505-2.8380767576.838076757-2.8380767576.838076757

Payroll (X)1.250.4145780993.0151130.0393520.0989466672.4010533330.0989466672.401053333

## Sheet1

 Triple A Construction Co. Sales (Y) Payroll (X) 6 3 8 4 9 6 5 4 4.5 2 9.5 5 SUMMARY OUTPUT Regression Statistics Multiple R 0.8333333333 R Square 0.6944444444 Adjusted R Square 0.6180555556 Standard Error 1.3110110602 Observations 6 ANOVA df SS MS F Significance F Regression 1 15.625 15.625 9.0909090909 0.0393518519 Residual 4 6.875 1.71875 Total 5 22.5 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 2 1.7425436389 1.1477474397 0.3150499206 -2.8380767567 6.8380767567 -2.8380767567 6.8380767567 Payroll (X) 1.25 0.4145780988 3.0151134458 0.0393518519 0.0989466669 2.4010533331 0.0989466669 2.4010533331 RESIDUAL OUTPUT Observation Predicted Sales (Y) Residuals 1 5.75 0.25 2 7 1 3 9.5 -0.5 4 7 -2 5 4.5 0 6 8.25 1.25

Payroll (X) Residual Plot

3 4 6 4 2 5 0.25 1 -0.5 -2 0 1.25Payroll (X)

Residuals

Payroll (X) Line Fit Plot

Sales (Y) 3 4 6 4 2 5 6 8 9 5 4.5 9.5 Predicted Sales (Y) 3 4 6 4 2 5 5.75 7 9.5 7 4.5 8.25Payroll (X)

Sales (Y)

Payroll (X) Residual Plot

3 4 6 4 2 5 0.25 1 -0.5 -2 0 1.25Payroll (X)

Residuals

Payroll (X) Line Fit Plot

Sales (Y) 3 4 6 4 2 5 6 8 9 5 4.5 9.5 Predicted Sales (Y) 3 4 6 4 2 5 5.75 7 9.5 7 4.5 8.25Payroll (X)

Sales (Y)

## Sheet1

 Triple A Construction Co. Sales (Y) Payroll (X) 6 3 8 4 9 6 5 4 4.5 2 9.5 5 SUMMARY OUTPUT Regression Statistics Multiple R 0.8333333333 R Square 0.6944444444 Adjusted R Square 0.6180555556 Standard Error 1.3110110602 Observations 6 ANOVA df SS MS F Significance F Regression 1 15.625 15.625 9.0909090909 0.0393518519 Residual 4 6.875 1.71875 Total 5 22.5 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 2 1.7425436389 1.1477474397 0.3150499206 -2.8380767567 6.8380767567 -2.8380767567 6.8380767567 Payroll (X) 1.25 0.4145780988 3.0151134458 0.0393518519 0.0989466669 2.4010533331 0.0989466669 2.4010533331 RESIDUAL OUTPUT Observation Predicted Sales (Y) Residuals 1 5.75 0.25 2 7 1 3 9.5 -0.5 4 7 -2 5 4.5 0 6 8.25 1.25

Payroll (X) Residual Plot

3 4 6 4 2 5 0.25 1 -0.5 -2 0 1.25Payroll (X)

Residuals

Payroll (X) Line Fit Plot

Sales (Y) 3 4 6 4 2 5 6 8 9 5 4.5 9.5 Predicted Sales (Y) 3 4 6 4 2 5 5.75 7 9.5 7 4.5 8.25Payroll (X)

Sales (Y)

Payroll (X) Residual Plot

3 4 6 4 2 5 0.25 1 -0.5 -2 0 1.25Payroll (X)

Residuals

Payroll (X) Line Fit Plot

Sales (Y) 3 4 6 4 2 5 6 8 9 5 4.5 9.5 Predicted Sales (Y) 3 4 6 4 2 5 5.75 7 9.5 7 4.5 8.25Payroll (X)

Sales (Y)

## Sheet1

 Triple A Construction Co. Sales (Y) Payroll (X) 6 3 8 4 9 6 5 4 4.5 2 9.5 5 SUMMARY OUTPUT Regression Statistics Multiple R 0.8333333333 R Square 0.6944444444 Adjusted R Square 0.6180555556 Standard Error 1.3110110602 Observations 6 ANOVA df SS MS F Significance F Regression 1 15.625 15.625 9.0909090909 0.0393518519 Residual 4 6.875 1.71875 Total 5 22.5 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 2 1.7425436389 1.1477474397 0.3150499206 -2.8380767567 6.8380767567 -2.8380767567 6.8380767567 Payroll (X) 1.25 0.4145780988 3.0151134458 0.0393518519 0.0989466669 2.4010533331 0.0989466669 2.4010533331 RESIDUAL OUTPUT Observation Predicted Sales (Y) Residuals 1 5.75 0.25 2 7 1 3 9.5 -0.5 4 7 -2 5 4.5 0 6 8.25 1.25

Payroll (X) Residual Plot

3 4 6 4 2 5 0.25 1 -0.5 -2 0 1.25Payroll (X)

Residuals

Payroll (X) Line Fit Plot

Sales (Y) 3 4 6 4 2 5 6 8 9 5 4.5 9.5 Predicted Sales (Y) 3 4 6 4 2 5 5.75 7 9.5 7 4.5 8.25Payroll (X)

Sales (Y)

Payroll (X) Residual Plot

3 4 6 4 2 5 0.25 1 -0.5 -2 0 1.25Payroll (X)

Residuals

Payroll (X) Line Fit Plot

Sales (Y) 3 4 6 4 2 5 6 8 9 5 4.5 9.5 Predicted Sales (Y) 3 4 6 4 2 5 5.75 7 9.5 7 4.5 8.25Payroll (X)

Sales (Y)

## Sheet1

 Triple A Construction Co. Sales (Y) Payroll (X) 6 3 8 4 9 6 5 4 4.5 2 9.5 5 SUMMARY OUTPUT Regression Statistics Multiple R 0.8333333333 R Square 0.6944444444 Adjusted R Square 0.6180555556 Standard Error 1.3110110602 Observations 6 ANOVA df SS MS F Significance F Regression 1 15.625 15.625 9.0909090909 0.0393518519 Residual 4 6.875 1.71875 Total 5 22.5 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 2 1.7425436389 1.1477474397 0.3150499206 -2.8380767567 6.8380767567 -2.8380767567 6.8380767567 Payroll (X) 1.25 0.4145780988 3.0151134458 0.0393518519 0.0989466669 2.4010533331 0.0989466669 2.4010533331 RESIDUAL OUTPUT Observation Predicted Sales (Y) Residuals 1 5.75 0.25 2 7 1 3 9.5 -0.5 4 7 -2 5 4.5 0 6 8.25 1.25

Payroll (X) Residual Plot

3 4 6 4 2 5 0.25 1 -0.5 -2 0 1.25Payroll (X)

Residuals

Payroll (X) Line Fit Plot

Sales (Y) 3 4 6 4 2 5 6 8 9 5 4.5 9.5 Predicted Sales (Y) 3 4 6 4 2 5 5.75 7 9.5 7 4.5 8.25Payroll (X)

Sales (Y)

Payroll (X) Residual Plot

3 4 6 4 2 5 0.25 1 -0.5 -2 0 1.25Payroll (X)

Residuals

Payroll (X) Line Fit Plot

Sales (Y) 3 4 6 4 2 5 6 8 9 5 4.5 9.5 Predicted Sales (Y) 3 4 6 4 2 5 5.75 7 9.5 7 4.5 8.25Payroll (X)

Sales (Y)

## Sheet1

 Triple A Construction Co. Sales (Y) Payroll (X) 6 3 8 4 9 6 5 4 4.5 2 9.5 5 SUMMARY OUTPUT Regression Statistics Multiple R 0.8333333333 R Square 0.6944444444 Adjusted R Square 0.6180555556 Standard Error 1.3110110602 Observations 6 ANOVA df SS MS F Significance F Regression 1 15.625 15.625 9.0909090909 0.0393518519 Residual 4 6.875 1.71875 Total 5 22.5 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 2 1.7425436389 1.1477474397 0.3150499206 -2.8380767567 6.8380767567 -2.8380767567 6.8380767567 Payroll (X) 1.25 0.4145780988 3.0151134458 0.0393518519 0.0989466669 2.4010533331 0.0989466669 2.4010533331 RESIDUAL OUTPUT Observation Predicted Sales (Y) Residuals 1 5.75 0.25 2 7 1 3 9.5 -0.5 4 7 -2 5 4.5 0 6 8.25 1.25

Payroll (X) Residual Plot

3 4 6 4 2 5 0.25 1 -0.5 -2 0 1.25Payroll (X)

Residuals

Payroll (X) Line Fit Plot

Sales (Y) 3 4 6 4 2 5 6 8 9 5 4.5 9.5 Predicted Sales (Y) 3 4 6 4 2 5 5.75 7 9.5 7 4.5 8.25Payroll (X)

Sales (Y)

Payroll (X) Residual Plot

3 4 6 4 2 5 0.25 1 -0.5 -2 0 1.25Payroll (X)

Residuals

Payroll (X) Line Fit Plot

Sales (Y) 3 4 6 4 2 5 6 8 9 5 4.5 9.5 Predicted Sales (Y) 3 4 6 4 2 5 5.75 7 9.5 7 4.5 8.25Payroll (X)

Sales (Y)

## Sheet1

 Triple A Construction Co. Sales (Y) Payroll (X) 6 3 8 4 9 6 5 4 4.5 2 9.5 5 SUMMARY OUTPUT Regression Statistics Multiple R 0.8333333333 R Square 0.6944444444 Adjusted R Square 0.6180555556 Standard Error 1.3110110602 Observations 6 ANOVA df SS MS F Significance F Regression 1 15.625 15.625 9.0909090909 0.0393518519 Residual 4 6.875 1.71875 Total 5 22.5 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 2 1.7425436389 1.1477474397 0.3150499206 -2.8380767567 6.8380767567 -2.8380767567 6.8380767567 Payroll (X) 1.25 0.4145780988 3.0151134458 0.0393518519 0.0989466669 2.4010533331 0.0989466669 2.4010533331 RESIDUAL OUTPUT Observation Predicted Sales (Y) Residuals 1 5.75 0.25 2 7 1 3 9.5 -0.5 4 7 -2 5 4.5 0 6 8.25 1.25

Payroll (X) Residual Plot

3 4 6 4 2 5 0.25 1 -0.5 -2 0 1.25Payroll (X)

Residuals

Payroll (X) Line Fit Plot

Sales (Y) 3 4 6 4 2 5 6 8 9 5 4.5 9.5 Predicted Sales (Y) 3 4 6 4 2 5 5.75 7 9.5 7 4.5 8.25Payroll (X)

Sales (Y)

Payroll (X) Residual Plot

3 4 6 4 2 5 0.25 1 -0.5 -2 0 1.25Payroll (X)

Residuals

Payroll (X) Line Fit Plot

Sales (Y) 3 4 6 4 2 5 6 8 9 5 4.5 9.5 Predicted Sales (Y) 3 4 6 4 2 5 5.75 7 9.5 7 4.5 8.25Payroll (X)

Sales (Y)

## Sheet1

 Triple A Construction Co. Sales (Y) Payroll (X) 6 3 8 4 9 6 5 4 4.5 2 9.5 5 SUMMARY OUTPUT Regression Statistics Multiple R 0.8333333333 R Square 0.6944444444 Adjusted R Square 0.6180555556 Standard Error 1.3110110602 Observations 6 ANOVA df SS MS F Significance F Regression 1 15.625 15.625 9.0909090909 0.0393518519 Residual 4 6.875 1.71875 Total 5 22.5 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 2 1.7425436389 1.1477474397 0.3150499206 -2.8380767567 6.8380767567 -2.8380767567 6.8380767567 Payroll (X) 1.25 0.4145780988 3.0151134458 0.0393518519 0.0989466669 2.4010533331 0.0989466669 2.4010533331 RESIDUAL OUTPUT Observation Predicted Sales (Y) Residuals 1 5.75 0.25 2 7 1 3 9.5 -0.5 4 7 -2 5 4.5 0 6 8.25 1.25

Payroll (X) Residual Plot

3 4 6 4 2 5 0.25 1 -0.5 -2 0 1.25Payroll (X)

Residuals

Payroll (X) Line Fit Plot

Sales (Y) 3 4 6 4 2 5 6 8 9 5 4.5 9.5 Predicted Sales (Y) 3 4 6 4 2 5 5.75 7 9.5 7 4.5 8.25Payroll (X)

Sales (Y)

Payroll (X) Residual Plot

3 4 6 4 2 5 0.25 1 -0.5 -2 0 1.25Payroll (X)

Residuals

Payroll (X) Line Fit Plot

Sales (Y) 3 4 6 4 2 5 6 8 9 5 4.5 9.5 Predicted Sales (Y) 3 4 6 4 2 5 5.75 7 9.5 7 4.5 8.25Payroll (X)

Sales (Y)

## Sheet1

 Triple A Construction Co. Sales (Y) Payroll (X) 6 3 8 4 9 6 5 4 4.5 2 9.5 5 SUMMARY OUTPUT Regression Statistics Multiple R 0.8333333333 R Square 0.6944444444 Adjusted R Square 0.6180555556 Standard Error 1.3110110602 Observations 6 ANOVA df SS MS F Significance F Regression 1 15.625 15.625 9.0909090909 0.0393518519 Residual 4 6.875 1.71875 Total 5 22.5 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 2 1.7425436389 1.1477474397 0.3150499206 -2.8380767567 6.8380767567 -2.8380767567 6.8380767567 Payroll (X) 1.25 0.4145780988 3.0151134458 0.0393518519 0.0989466669 2.4010533331 0.0989466669 2.4010533331 RESIDUAL OUTPUT Observation Predicted Sales (Y) Residuals 1 5.75 0.25 2 7 1 3 9.5 -0.5 4 7 -2 5 4.5 0 6 8.25 1.25

Payroll (X) Residual Plot

3 4 6 4 2 5 0.25 1 -0.5 -2 0 1.25Payroll (X)

Residuals

Payroll (X) Line Fit Plot

Sales (Y) 3 4 6 4 2 5 6 8 9 5 4.5 9.5 Predicted Sales (Y) 3 4 6 4 2 5 5.75 7 9.5 7 4.5 8.25Payroll (X)

Sales (Y)

Payroll (X) Residual Plot

3 4 6 4 2 5 0.25 1 -0.5 -2 0 1.25Payroll (X)

Residuals

Payroll (X) Line Fit Plot

Sales (Y) 3 4 6 4 2 5 6 8 9 5 4.5 9.5 Predicted Sales (Y) 3 4 6 4 2 5 5.75 7 9.5 7 4.5 8.25Payroll (X)

Sales (Y)

## Sheet3

SUMMARY OUTPUT

Regression Statistics

Multiple R0.819680305

R Square0.671875802

Standard Error24312.60729

Observations14

ANOVA

dfSSMSFSignificance F

Regression2133139369686.66E+0911.261950.002178765

Residual1165021316035.91E+08

Total1319816068571

CoefficientsStandard Errort StatP-valueLower 95%Upper 95%Lower 95.0%Upper 95.0%

Intercept146630.893625482.082875.7542740.00012890545.20731202716.579890545.20731202716.5798

Square feet43.8193664910.280965074.2621840.00133821.1911149466.4476180421.1911149466.44761804

Age-2898.686247796.5649421-3.638980.003895-4651.913863-1145.45863-4651.913863-1145.45863

## Sheet1

 Selling Price Square feet Age 95000 1926 30 119000 2069 40 124800 1720 30 135000 1396 15 142800 1706 32 145000 1847 38 159000 1950 27 165000 2323 30 182000 2285 26 183000 3752 35 200000 2300 18 211000 2525 17 215000 3800 40 219000 1740 12 SUMMARY OUTPUT Regression Statistics Multiple R 0.8196803049 R Square 0.6718758022 Adjusted R Square 0.6122168572 Standard Error 24312.6072850603 Observations 14 ANOVA df SS MS F Significance F Regression 2 13313936968.4553 6656968484.22766 11.261945743 0.0021787652 Residual 11 6502131602.97325 591102872.997569 Total 13 19816068571.4286 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 146630.893555974 25482.0828687578 5.7542742605 0.0001275664 90545.2073136126 202716.579798335 90545.2073136126 202716.579798335 Square feet 43.8193664901 10.2809650702 4.2621841618 0.0013380948 21.1911149391 66.447618041 21.1911149391 66.447618041 Age -2898.686246708 796.5649420672 -3.638982955 0.0038949963 -4651.9138632471 -1145.4586301689 -4651.9138632471 -1145.4586301689 RESIDUAL OUTPUT Observation Predicted Selling Price Residuals 1 144066.40601462 -49066.40601462 2 121345.712955621 -2345.712955621 3 135039.616517664 -10239.6165176644 4 164322.4354755 -29322.4354754996 5 128628.772893387 14171.2271066126 6 117415.18608824 27584.8139117598 7 153814.129550506 5185.8704494943 8 161462.69451118 3537.30548882 9 171392.303571389 10607.696428611 10 209587.137991958 -26587.1379919581 11 195239.084042404 4760.915957596 12 207997.127749379 3002.872250621 13 197197.036349942 17802.9636500581 14 188092.35628821 30907.6437117904

Square feet Residual Plot

1926 2069 1720 1396 1706 1847 1950 2323 2285 3752 2300 2525 3800 1740 -49066.406014619977 -2345.7129556209984 -10239.616517664399 -29322.435475499602 14171.227106612627 27584.813911759818 5185.8704494942504 3537.3054888200131 10607.696428610972 -26587.137991958123 4760.9159575959784 3002.8722506209742 17802.96365005814 30907.643711790413Square feet

Residuals

Age Residual Plot

30 40 30 15 32 38 27 30 26 35 18 17 40 12 -49066.406014619977 -2345.7129556209984 -10239.616517664399 -29322.435475499602 14171.227106612627 27584.813911759818 5185.8704494942504 3537.3054888200131 10607.696428610972 -26587.137991958123 4760.9159575959784 3002.8722506209742 17802.96365005814 30907.643711790413Age

Residuals

Square feet Line Fit Plot

Selling Price 1926 2069 1720 1396 1706 1847 1950 2323 2285 3752 2300 2525 3800 1740 95000 119000 124800 135000 142800 145000 159000 165000 182000 183000 200000 211000 215000 219000 Predicted Selling Price 1926 2069 1720 1396 1706 1847 1950 2323 2285 3752 2300 2525 3800 1740 144066.40601461998 121345.712955621 135039.6165176644 164322.4354754996 128628.77289338737 117415.18608824018 153814.12955050575 161462.69451117999 171392.30357138903 209587.13799195812 195239.08404240402 207997.12774937903 197197.03634994186 188092.35628820959Square feet

Selling Price

Age Line Fit Plot

Selling Price 30 40 30 15 32 38 27 30 26 35 18 17 40 12 95000 119000 124800 135000 142800 145000 159000 165000 182000 183000 200000 211000 215000 219000 Predicted Selling Price 30 40 30 15 32 38 27 30 26 35 18 17 40 12 144066.40601461998 121345.712955621 135039.6165176644 164322.4354754996 128628.77289338737 117415.18608824018 153814.12955050575 161462.69451117999 171392.30357138903 209587.13799195812 195239.08404240402 207997.12774937903 197197.03634994186 188092.35628820959Age

Selling Price

Square feet Residual Plot

1926 2069 1720 1396 1706 1847 1950 2323 2285 3752 2300 2525 3800 1740 -49066.406014619977 -2345.7129556209984 -10239.616517664399 -29322.435475499602 14171.227106612627 27584.813911759818 5185.8704494942504 3537.3054888200131 10607.696428610972 -26587.137991958123 4760.9159575959784 3002.8722506209742 17802.96365005814 30907.643711790413Square feet

Residuals

Age Residual Plot

30 40 30 15 32 38 27 30 26 35 18 17 40 12 -49066.406014619977 -2345.7129556209984 -10239.616517664399 -29322.435475499602 14171.227106612627 27584.813911759818 5185.8704494942504 3537.3054888200131 10607.696428610972 -26587.137991958123 4760.9159575959784 3002.8722506209742 17802.96365005814 30907.643711790413Age

Residuals

Square feet Line Fit Plot

Selling Price 1926 2069 1720 1396 1706 1847 1950 2323 2285 3752 2300 2525 3800 1740 95000 119000 124800 135000 142800 145000 159000 165000 182000 183000 200000 211000 215000 219000 Predicted Selling Price 1926 2069 1720 1396 1706 1847 1950 2323 2285 3752 2300 2525 3800 1740 144066.40601461998 121345.712955621 135039.6165176644 164322.4354754996 128628.77289338737 117415.18608824018 153814.12955050575 161462.69451117999 171392.30357138903 209587.13799195812 195239.08404240402 207997.12774937903 197197.03634994186 188092.35628820959Square feet

Selling Price

Age Line Fit Plot

Selling Price 30 40 30 15 32 38 27 30 26 35 18 17 40 12 95000 119000 124800 135000 142800 145000 159000 165000 182000 183000 200000 211000 215000 219000 Predicted Selling Price 30 40 30 15 32 38 27 30 26 35 18 17 40 12 144066.40601461998 121345.712955621 135039.6165176644 164322.4354754996 128628.77289338737 117415.18608824018 153814.12955050575 161462.69451117999 171392.30357138903 209587.13799195812 195239.08404240402 207997.12774937903 197197.03634994186 188092.35628820959Age

Selling Price

Square feet Residual Plot

1926 2069 1720 1396 1706 1847 1950 2323 2285 3752 2300 2525 3800 1740 -49066.406014619977 -2345.7129556209984 -10239.616517664399 -29322.435475499602 14171.227106612627 27584.813911759818 5185.8704494942504 3537.3054888200131 10607.696428610972 -26587.137991958123 4760.9159575959784 3002.8722506209742 17802.96365005814 30907.643711790413Square feet

Residuals

Age Residual Plot

30 40 30 15 32 38 27 30 26 35 18 17 40 12 -49066.406014619977 -2345.7129556209984 -10239.616517664399 -29322.435475499602 14171.227106612627 27584.813911759818 5185.8704494942504 3537.3054888200131 10607.696428610972 -26587.137991958123 4760.9159575959784 3002.8722506209742 17802.96365005814 30907.643711790413Age

Residuals

Square feet Line Fit Plot

Selling Price 1926 2069 1720 1396 1706 1847 1950 2323 2285 3752 2300 2525 3800 1740 95000 119000 124800 135000 142800 145000 159000 165000 182000 183000 200000 211000 215000 219000 Predicted Selling Price 1926 2069 1720 1396 1706 1847 1950 2323 2285 3752 2300 2525 3800 1740 144066.40601461998 121345.712955621 135039.6165176644 164322.4354754996 128628.77289338737 117415.18608824018 153814.12955050575 161462.69451117999 171392.30357138903 209587.13799195812 195239.08404240402 207997.12774937903 197197.03634994186 188092.35628820959Square feet

Selling Price

Age Line Fit Plot

Selling Price 30 40 30 15 32 38 27 30 26 35 18 17 40 12 95000 119000 124800 135000 142800 145000 159000 165000 182000 183000 200000 211000 215000 219000 Predicted Selling Price 30 40 30 15 32 38 27 30 26 35 18 17 40 12 144066.40601461998 121345.712955621 135039.6165176644 164322.4354754996 128628.77289338737 117415.18608824018 153814.12955050575 161462.69451117999 171392.30357138903 209587.13799195812 195239.08404240402 207997.12774937903 197197.03634994186 188092.35628820959Age

Selling Price

Square feet Residual Plot

1926 2069 1720 1396 1706 1847 1950 2323 2285 3752 2300 2525 3800 1740 -49066.406014619977 -2345.7129556209984 -10239.616517664399 -29322.435475499602 14171.227106612627 27584.813911759818 5185.8704494942504 3537.3054888200131 10607.696428610972 -26587.137991958123 4760.9159575959784 3002.8722506209742 17802.96365005814 30907.643711790413Square feet

Residuals

Age Residual Plot

30 40 30 15 32 38 27 30 26 35 18 17 40 12 -49066.406014619977 -2345.7129556209984 -10239.616517664399 -29322.435475499602 14171.227106612627 27584.813911759818 5185.8704494942504 3537.3054888200131 10607.696428610972 -26587.137991958123 4760.9159575959784 3002.8722506209742 17802.96365005814 30907.643711790413Age

Residuals

Square feet Line Fit Plot

Selling Price 1926 2069 1720 1396 1706 1847 1950 2323 2285 3752 2300 2525 3800 1740 95000 119000 124800 135000 142800 145000 159000 165000 182000 183000 200000 211000 215000 219000 Predicted Selling Price 1926 2069 1720 1396 1706 1847 1950 2323 2285 3752 2300 2525 3800 1740 144066.40601461998 121345.712955621 135039.6165176644 164322.4354754996 128628.77289338737 117415.18608824018 153814.12955050575 161462.69451117999 171392.30357138903 209587.13799195812 195239.08404240402 207997.12774937903 197197.03634994186 188092.35628820959Square feet

Selling Price

Age Line Fit Plot

Selling Price 30 40 30 15 32 38 27 30 26 35 18 17 40 12 95000 119000 124800 135000 142800 145000 159000 165000 182000 183000 200000 211000 215000 219000 Predicted Selling Price 30 40 30 15 32 38 27 30 26 35 18 17 40 12 144066.40601461998 121345.712955621 135039.6165176644 164322.4354754996 128628.77289338737 117415.18608824018 153814.12955050575 161462.69451117999 171392.30357138903 209587.13799195812 195239.08404240402 207997.12774937903 197197.03634994186 188092.35628820959Age

Selling Price

## Sheet1

 Selling Price Square feet Age 95000 1926 30 119000 2069 40 124800 1720 30 135000 1396 15 142800 1706 32 145000 1847 38 159000 1950 27 165000 2323 30 182000 2285 26 183000 3752 35 200000 2300 18 211000 2525 17 215000 3800 40 219000 1740 12 SUMMARY OUTPUT Regression Statistics Multiple R 0.8196803049 R Square 0.6718758022 Adjusted R Square 0.6122168572 Standard Error 24312.6072850603 Observations 14 ANOVA df SS MS F Significance F Regression 2 13313936968.4553 6656968484.22766 11.261945743 0.0021787652 Residual 11 6502131602.97325 591102872.997569 Total 13 19816068571.4286 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 146630.893555974 25482.0828687578 5.7542742605 0.0001275664 90545.2073136126 202716.579798335 90545.2073136126 202716.579798335 Square feet 43.8193664901 10.2809650702 4.2621841618 0.0013380948 21.1911149391 66.447618041 21.1911149391 66.447618041 Age -2898.686246708 796.5649420672 -3.638982955 0.0038949963 -4651.9138632471 -1145.4586301689 -4651.9138632471 -1145.4586301689 RESIDUAL OUTPUT Observation Predicted Selling Price Residuals 1 144066.40601462 -49066.40601462 2 121345.712955621 -2345.712955621 3 135039.616517664 -10239.6165176644 4 164322.4354755 -29322.4354754996 5 128628.772893387 14171.2271066126 6 117415.18608824 27584.8139117598 7 153814.129550506 5185.8704494943 8 161462.69451118 3537.30548882 9 171392.303571389 10607.696428611 10 209587.137991958 -26587.1379919581 11 195239.084042404 4760.915957596 12 207997.127749379 3002.872250621 13 197197.036349942 17802.9636500581 14 188092.35628821 30907.6437117904

Square feet Residual Plot

1926 2069 1720 1396 1706 1847 1950 2323 2285 3752 2300 2525 3800 1740 -49066.406014619977 -2345.7129556209984 -10239.616517664399 -29322.435475499602 14171.227106612627 27584.813911759818 5185.8704494942504 3537.3054888200131 10607.696428610972 -26587.137991958123 4760.9159575959784 3002.8722506209742 17802.96365005814 30907.643711790413Square feet

Residuals

Age Residual Plot

30 40 30 15 32 38 27 30 26 35 18 17 40 12 -49066.406014619977 -2345.7129556209984 -10239.616517664399 -29322.435475499602 14171.227106612627 27584.813911759818 5185.8704494942504 3537.3054888200131 10607.696428610972 -26587.137991958123 4760.9159575959784 3002.8722506209742 17802.96365005814 30907.643711790413Age

Residuals

Square feet Line Fit Plot

Selling Price 1926 2069 1720 1396 1706 1847 1950 2323 2285 3752 2300 2525 3800 1740 95000 119000 124800 135000 142800 145000 159000 165000 182000 183000 200000 211000 215000 219000 Predicted Selling Price 1926 2069 1720 1396 1706 1847 1950 2323 2285 3752 2300 2525 3800 1740 144066.40601461998 121345.712955621 135039.6165176644 164322.4354754996 128628.77289338737 117415.18608824018 153814.12955050575 161462.69451117999 171392.30357138903 209587.13799195812 195239.08404240402 207997.12774937903 197197.03634994186 188092.35628820959Square feet

Selling Price

Age Line Fit Plot

Selling Price 30 40 30 15 32 38 27 30 26 35 18 17 40 12 95000 119000 124800 135000 142800 145000 159000 165000 182000 183000 200000 211000 215000 219000 Predicted Selling Price 30 40 30 15 32 38 27 30 26 35 18 17 40 12 144066.40601461998 121345.712955621 135039.6165176644 164322.4354754996 128628.77289338737 117415.18608824018 153814.12955050575 161462.69451117999 171392.30357138903 209587.13799195812 195239.08404240402 207997.12774937903 197197.03634994186 188092.35628820959Age

Selling Price

Square feet Residual Plot

1926 2069 1720 1396 1706 1847 1950 2323 2285 3752 2300 2525 3800 1740 -49066.406014619977 -2345.7129556209984 -10239.616517664399 -29322.435475499602 14171.227106612627 27584.813911759818 5185.8704494942504 3537.3054888200131 10607.696428610972 -26587.137991958123 4760.9159575959784 3002.8722506209742 17802.96365005814 30907.643711790413Square feet

Residuals

Age Residual Plot

30 40 30 15 32 38 27 30 26 35 18 17 40 12 -49066.406014619977 -2345.7129556209984 -10239.616517664399 -29322.435475499602 14171.227106612627 27584.813911759818 5185.8704494942504 3537.3054888200131 10607.696428610972 -26587.137991958123 4760.9159575959784 3002.8722506209742 17802.96365005814 30907.643711790413Age

Residuals

Square feet Line Fit Plot

Selling Price 1926 2069 1720 1396 1706 1847 1950 2323 2285 3752 2300 2525 3800 1740 95000 119000 124800 135000 142800 145000 159000 165000 182000 183000 200000 211000 215000 219000 Predicted Selling Price 1926 2069 1720 1396 1706 1847 1950 2323 2285 3752 2300 2525 3800 1740 144066.40601461998 121345.712955621 135039.6165176644 164322.4354754996 128628.77289338737 117415.18608824018 153814.12955050575 161462.69451117999 171392.30357138903 209587.13799195812 195239.08404240402 207997.12774937903 197197.03634994186 188092.35628820959Square feet

Selling Price

Age Line Fit Plot

Selling Price 30 40 30 15 32 38 27 30 26 35 18 17 40 12 95000 119000 124800 135000 142800 145000 159000 165000 182000 183000 200000 211000 215000 219000 Predicted Selling Price 30 40 30 15 32 38 27 30 26 35 18 17 40 12 144066.40601461998 121345.712955621 135039.6165176644 164322.4354754996 128628.77289338737 117415.18608824018 153814.12955050575 161462.69451117999 171392.30357138903 209587.13799195812 195239.08404240402 207997.12774937903 197197.03634994186 188092.35628820959Age

Selling Price

Square feet Residual Plot

1926 2069 1720 1396 1706 1847 1950 2323 2285 3752 2300 2525 3800 1740 -49066.406014619977 -2345.7129556209984 -10239.616517664399 -29322.435475499602 14171.227106612627 27584.813911759818 5185.8704494942504 3537.3054888200131 10607.696428610972 -26587.137991958123 4760.9159575959784 3002.8722506209742 17802.96365005814 30907.643711790413Square feet

Residuals

Age Residual Plot

30 40 30 15 32 38 27 30 26 35 18 17 40 12 -49066.406014619977 -2345.7129556209984 -10239.616517664399 -29322.435475499602 14171.227106612627 27584.813911759818 5185.8704494942504 3537.3054888200131 10607.696428610972 -26587.137991958123 4760.9159575959784 3002.8722506209742 17802.96365005814 30907.643711790413Age

Residuals

Square feet Line Fit Plot

Selling Price 1926 2069 1720 1396 1706 1847 1950 2323 2285 3752 2300 2525 3800 1740 95000 119000 124800 135000 142800 145000 159000 165000 182000 183000 200000 211000 215000 219000 Predicted Selling Price 1926 2069 1720 1396 1706 1847 1950 2323 2285 3752 2300 2525 3800 1740 144066.40601461998 121345.712955621 135039.6165176644 164322.4354754996 128628.77289338737 117415.18608824018 153814.12955050575 161462.69451117999 171392.30357138903 209587.13799195812 195239.08404240402 207997.12774937903 197197.03634994186 188092.35628820959Square feet

Selling Price

Age Line Fit Plot

Selling Price 30 40 30 15 32 38 27 30 26 35 18 17 40 12 95000 119000 124800 135000 142800 145000 159000 165000 182000 183000 200000 211000 215000 219000 Predicted Selling Price 30 40 30 15 32 38 27 30 26 35 18 17 40 12 144066.40601461998 121345.712955621 135039.6165176644 164322.4354754996 128628.77289338737 117415.18608824018 153814.12955050575 161462.69451117999 171392.30357138903 209587.13799195812 195239.08404240402 207997.12774937903 197197.03634994186 188092.35628820959Age

Selling Price

Square feet Residual Plot

1926 2069 1720 1396 1706 1847 1950 2323 2285 3752 2300 2525 3800 1740 -49066.406014619977 -2345.7129556209984 -10239.616517664399 -29322.435475499602 14171.227106612627 27584.813911759818 5185.8704494942504 3537.3054888200131 10607.696428610972 -26587.137991958123 4760.9159575959784 3002.8722506209742 17802.96365005814 30907.643711790413Square feet

Residuals

Age Residual Plot

30 40 30 15 32 38 27 30 26 35 18 17 40 12 -49066.406014619977 -2345.7129556209984 -10239.616517664399 -29322.435475499602 14171.227106612627 27584.813911759818 5185.8704494942504 3537.3054888200131 10607.696428610972 -26587.137991958123 4760.9159575959784 3002.8722506209742 17802.96365005814 30907.643711790413Age

Residuals

Square feet Line Fit Plot

Selling Price 1926 2069 1720 1396 1706 1847 1950 2323 2285 3752 2300 2525 3800 1740 95000 119000 124800 135000 142800 145000 159000 165000 182000 183000 200000 211000 215000 219000 Predicted Selling Price 1926 2069 1720 1396 1706 1847 1950 2323 2285 3752 2300 2525 3800 1740 144066.40601461998 121345.712955621 135039.6165176644 164322.4354754996 128628.77289338737 117415.18608824018 153814.12955050575 161462.69451117999 171392.30357138903 209587.13799195812 195239.08404240402 207997.12774937903 197197.03634994186 188092.35628820959Square feet

Selling Price

Age Line Fit Plot

Selling Price 30 40 30 15 32 38 27 30 26 35 18 17 40 12 95000 119000 124800 135000 142800 145000 159000 165000 182000 183000 200000 211000 215000 219000 Predicted Selling Price 30 40 30 15 32 38 27 30 26 35 18 17 40 12 144066.40601461998 121345.712955621 135039.6165176644 164322.4354754996 128628.77289338737 117415.18608824018 153814.12955050575 161462.69451117999 171392.30357138903 209587.13799195812 195239.08404240402 207997.12774937903 197197.03634994186 188092.35628820959Age

Selling Price

## Sheet1

 Selling Price Square feet Age 95000 1926 30 119000 2069 40 124800 1720 30 135000 1396 15 142800 1706 32 145000 1847 38 159000 1950 27 165000 2323 30 182000 2285 26 183000 3752 35 200000 2300 18 211000 2525 17 215000 3800 40 219000 1740 12 SUMMARY OUTPUT Regression Statistics Multiple R 0.8196803049 R Square 0.6718758022 Adjusted R Square 0.6122168572 Standard Error 24312.6072850603 Observations 14 ANOVA df SS MS F Significance F Regression 2 13313936968.4553 6656968484.22766 11.261945743 0.0021787652 Residual 11 6502131602.97325 591102872.997569 Total 13 19816068571.4286 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 146630.893555974 25482.0828687578 5.7542742605 0.0001275664 90545.2073136126 202716.579798335 90545.2073136126 202716.579798335 Square feet 43.8193664901 10.2809650702 4.2621841618 0.0013380948 21.1911149391 66.447618041 21.1911149391 66.447618041 Age -2898.686246708 796.5649420672 -3.638982955 0.0038949963 -4651.9138632471 -1145.4586301689 -4651.9138632471 -1145.4586301689 RESIDUAL OUTPUT Observation Predicted Selling Price Residuals 1 144066.40601462 -49066.40601462 2 121345.712955621 -2345.712955621 3 135039.616517664 -10239.6165176644 4 164322.4354755 -29322.4354754996 5 128628.772893387 14171.2271066126 6 117415.18608824 27584.8139117598 7 153814.129550506 5185.8704494943 8 161462.69451118 3537.30548882 9 171392.303571389 10607.696428611 10 209587.137991958 -26587.1379919581 11 195239.084042404 4760.915957596 12 207997.127749379 3002.872250621 13 197197.036349942 17802.9636500581 14 188092.35628821 30907.6437117904

Square feet Residual Plot

1926 2069 1720 1396 1706 1847 1950 2323 2285 3752 2300 2525 3800 1740 -49066.406014619977 -2345.7129556209984 -10239.616517664399 -29322.435475499602 14171.227106612627 27584.813911759818 5185.8704494942504 3537.3054888200131 10607.696428610972 -26587.137991958123 4760.9159575959784 3002.8722506209742 17802.96365005814 30907.643711790413Square feet

Residuals

Age Residual Plot

30 40 30 15 32 38 27 30 26 35 18 17 40 12 -49066.406014619977 -2345.7129556209984 -10239.616517664399 -29322.435475499602 14171.227106612627 27584.813911759818 5185.8704494942504 3537.3054888200131 10607.696428610972 -26587.137991958123 4760.9159575959784 3002.8722506209742 17802.96365005814 30907.643711790413Age

Residuals

Square feet Line Fit Plot

Selling Price 1926 2069 1720 1396 1706 1847 1950 2323 2285 3752 2300 2525 3800 1740 95000 119000 124800 135000 142800 145000 159000 165000 182000 183000 200000 211000 215000 219000 Predicted Selling Price 1926 2069 1720 1396 1706 1847 1950 2323 2285 3752 2300 2525 3800 1740 144066.40601461998 121345.712955621 135039.6165176644 164322.4354754996 128628.77289338737 117415.18608824018 153814.12955050575 161462.69451117999 171392.30357138903 209587.13799195812 195239.08404240402 207997.12774937903 197197.03634994186 188092.35628820959Square feet

Selling Price

Age Line Fit Plot

Selling Price 30 40 30 15 32 38 27 30 26 35 18 17 40 12 95000 119000 124800 135000 142800 145000 159000 165000 182000 183000 200000 211000 215000 219000 Predicted Selling Price 30 40 30 15 32 38 27 30 26 35 18 17 40 12 144066.40601461998 121345.712955621 135039.6165176644 164322.4354754996 128628.77289338737 117415.18608824018 153814.12955050575 161462.69451117999 171392.30357138903 209587.13799195812 195239.08404240402 207997.12774937903 197197.03634994186 188092.35628820959Age

Selling Price

Square feet Residual Plot

1926 2069 1720 1396 1706 1847 1950 2323 2285 3752 2300 2525 3800 1740 -49066.406014619977 -2345.7129556209984 -10239.616517664399 -29322.435475499602 14171.227106612627 27584.813911759818 5185.8704494942504 3537.3054888200131 10607.696428610972 -26587.137991958123 4760.9159575959784 3002.8722506209742 17802.96365005814 30907.643711790413Square feet

Residuals

Age Residual Plot

30 40 30 15 32 38 27 30 26 35 18 17 40 12 -49066.406014619977 -2345.7129556209984 -10239.616517664399 -29322.435475499602 14171.227106612627 27584.813911759818 5185.8704494942504 3537.3054888200131 10607.696428610972 -26587.137991958123 4760.9159575959784 3002.8722506209742 17802.96365005814 30907.643711790413Age

Residuals

Square feet Line Fit Plot

Selling Price 1926 2069 1720 1396 1706 1847 1950 2323 2285 3752 2300 2525 3800 1740 95000 119000 124800 135000 142800 145000 159000 165000 182000 183000 200000 211000 215000 219000 Predicted Selling Price 1926 2069 1720 1396 1706 1847 1950 2323 2285 3752 2300 2525 3800 1740 144066.40601461998 121345.712955621 135039.6165176644 164322.4354754996 128628.77289338737 117415.18608824018 153814.12955050575 161462.69451117999 171392.30357138903 209587.13799195812 195239.08404240402 207997.12774937903 197197.03634994186 188092.35628820959Square feet

Selling Price

Age Line Fit Plot

Selling Price 30 40 30 15 32 38 27 30 26 35 18 17 40 12 95000 119000 124800 135000 142800 145000 159000 165000 182000 183000 200000 211000 215000 219000 Predicted Selling Price 30 40 30 15 32 38 27 30 26 35 18 17 40 12 144066.40601461998 121345.712955621 135039.6165176644 164322.4354754996 128628.77289338737 117415.18608824018 153814.12955050575 161462.69451117999 171392.30357138903 209587.13799195812 195239.08404240402 207997.12774937903 197197.03634994186 188092.35628820959Age

Selling Price

Square feet Residual Plot

1926 2069 1720 1396 1706 1847 1950 2323 2285 3752 2300 2525 3800 1740 -49066.406014619977 -2345.7129556209984 -10239.616517664399 -29322.435475499602 14171.227106612627 27584.813911759818 5185.8704494942504 3537.3054888200131 10607.696428610972 -26587.137991958123 4760.9159575959784 3002.8722506209742 17802.96365005814 30907.643711790413Square feet

Residuals

Age Residual Plot

30 40 30 15 32 38 27 30 26 35 18 17 40 12 -49066.406014619977 -2345.7129556209984 -10239.616517664399 -29322.435475499602 14171.227106612627 27584.813911759818 5185.8704494942504 3537.3054888200131 10607.696428610972 -26587.137991958123 4760.9159575959784 3002.8722506209742 17802.96365005814 30907.643711790413Age

Residuals

Square feet Line Fit Plot

Selling Price 1926 2069 1720 1396 1706 1847 1950 2323 2285 3752 2300 2525 3800 1740 95000 119000 124800 135000 142800 145000 159000 165000 182000 183000 200000 211000 215000 219000 Predicted Selling Price 1926 2069 1720 1396 1706 1847 1950 2323 2285 3752 2300 2525 3800 1740 144066.40601461998 121345.712955621 135039.6165176644 164322.4354754996 128628.77289338737 117415.18608824018 153814.12955050575 161462.69451117999 171392.30357138903 209587.13799195812 195239.08404240402 207997.12774937903 197197.03634994186 188092.35628820959Square feet

Selling Price

Age Line Fit Plot

Selling Price 30 40 30 15 32 38 27 30 26 35 18 17 40 12 95000 119000 124800 135000 142800 145000 159000 165000 182000 183000 200000 211000 215000 219000 Predicted Selling Price 30 40 30 15 32 38 27 30 26 35 18 17 40 12 144066.40601461998 121345.712955621 135039.6165176644 164322.4354754996 128628.77289338737 117415.18608824018 153814.12955050575 161462.69451117999 171392.30357138903 209587.13799195812 195239.08404240402 207997.12774937903 197197.03634994186 188092.35628820959Age

Selling Price

Square feet Residual Plot

1926 2069 1720 1396 1706 1847 1950 2323 2285 3752 2300 2525 3800 1740 -49066.406014619977 -2345.7129556209984 -10239.616517664399 -29322.435475499602 14171.227106612627 27584.813911759818 5185.8704494942504 3537.3054888200131 10607.696428610972 -26587.137991958123 4760.9159575959784 3002.8722506209742 17802.96365005814 30907.643711790413Square feet

Residuals

Age Residual Plot

30 40 30 15 32 38 27 30 26 35 18 17 40 12 -49066.406014619977 -2345.7129556209984 -10239.616517664399 -29322.435475499602 14171.227106612627 27584.813911759818 5185.8704494942504 3537.3054888200131 10607.696428610972 -26587.137991958123 4760.9159575959784 3002.8722506209742 17802.96365005814 30907.643711790413Age

Residuals

Square feet Line Fit Plot

Selling Price 1926 2069 1720 1396 1706 1847 1950 2323 2285 3752 2300 2525 3800 1740 95000 119000 124800 135000 142800 145000 159000 165000 182000 183000 200000 211000 215000 219000 Predicted Selling Price 1926 2069 1720 1396 1706 1847 1950 2323 2285 3752 2300 2525 3800 1740 144066.40601461998 121345.712955621 135039.6165176644 164322.4354754996 128628.77289338737 117415.18608824018 153814.12955050575 161462.69451117999 171392.30357138903 209587.13799195812 195239.08404240402 207997.12774937903 197197.03634994186 188092.35628820959Square feet

Selling Price

Age Line Fit Plot

Selling Price 30 40 30 15 32 38 27 30 26 35 18 17 40 12 95000 119000 124800 135000 142800 145000 159000 165000 182000 183000 200000 211000 215000 219000 Predicted Selling Price 30 40 30 15 32 38 27 30 26 35 18 17 40 12 144066.40601461998 121345.712955621 135039.6165176644 164322.4354754996 128628.77289338737 117415.18608824018 153814.12955050575 161462.69451117999 171392.30357138903 209587.13799195812 195239.08404240402 207997.12774937903 197197.03634994186 188092.35628820959Age

Selling Price

## Sheet1

 Selling Price Square feet Age 95000 1926 30 119000 2069 40 124800 1720 30 135000 1396 15 142800 1706 32 145000 1847 38 159000 1950 27 165000 2323 30 182000 2285 26 183000 3752 35 200000 2300 18 211000 2525 17 215000 3800 40 219000 1740 12 SUMMARY OUTPUT Regression Statistics Multiple R 0.8196803049 R Square 0.6718758022 Adjusted R Square 0.6122168572 Standard Error 24312.6072850603 Observations 14 ANOVA df SS MS F Significance F Regression 2 13313936968.4553 6656968484.22766 11.261945743 0.0021787652 Residual 11 6502131602.97325 591102872.997569 Total 13 19816068571.4286 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 146630.893555974 25482.0828687578 5.7542742605 0.0001275664 90545.2073136126 202716.579798335 90545.2073136126 202716.579798335 Square feet 43.8193664901 10.2809650702 4.2621841618 0.0013380948 21.1911149391 66.447618041 21.1911149391 66.447618041 Age -2898.686246708 796.5649420672 -3.638982955 0.0038949963 -4651.9138632471 -1145.4586301689 -4651.9138632471 -1145.4586301689 RESIDUAL OUTPUT Observation Predicted Selling Price Residuals 1 144066.40601462 -49066.40601462 2 121345.712955621 -2345.712955621 3 135039.616517664 -10239.6165176644 4 164322.4354755 -29322.4354754996 5 128628.772893387 14171.2271066126 6 117415.18608824 27584.8139117598 7 153814.129550506 5185.8704494943 8 161462.69451118 3537.30548882 9 171392.303571389 10607.696428611 10 209587.137991958 -26587.1379919581 11 195239.084042404 4760.915957596 12 207997.127749379 3002.872250621 13 197197.036349942 17802.9636500581 14 188092.35628821 30907.6437117904

Square feet Residual Plot

1926 2069 1720 1396 1706 1847 1950 2323 2285 3752 2300 2525 3800 1740 -49066.406014619977 -2345.7129556209984 -10239.616517664399 -29322.435475499602 14171.227106612627 27584.813911759818 5185.8704494942504 3537.3054888200131 10607.696428610972 -26587.137991958123 4760.9159575959784 3002.8722506209742 17802.96365005814 30907.643711790413Square feet

Residuals

Age Residual Plot

30 40 30 15 32 38 27 30 26 35 18 17 40 12 -49066.406014619977 -2345.7129556209984 -10239.616517664399 -29322.435475499602 14171.227106612627 27584.813911759818 5185.8704494942504 3537.3054888200131 10607.696428610972 -26587.137991958123 4760.9159575959784 3002.8722506209742 17802.96365005814 30907.643711790413Age

Residuals

Square feet Line Fit Plot

Selling Price 1926 2069 1720 1396 1706 1847 1950 2323 2285 3752 2300 2525 3800 1740 95000 119000 124800 135000 142800 145000 159000 165000 182000 183000 200000 211000 215000 219000 Predicted Selling Price 1926 2069 1720 1396 1706 1847 1950 2323 2285 3752 2300 2525 3800 1740 144066.40601461998 121345.712955621 135039.6165176644 164322.4354754996 128628.77289338737 117415.18608824018 153814.12955050575 161462.69451117999 171392.30357138903 209587.13799195812 195239.08404240402 207997.12774937903 197197.03634994186 188092.35628820959Square feet

Selling Price

Age Line Fit Plot

Selling Price 30 40 30 15 32 38 27 30 26 35 18 17 40 12 95000 119000 124800 135000 142800 145000 159000 165000 182000 183000 200000 211000 215000 219000 Predicted Selling Price 30 40 30 15 32 38 27 30 26 35 18 17 40 12 144066.40601461998 121345.712955621 135039.6165176644 164322.4354754996 128628.77289338737 117415.18608824018 153814.12955050575 161462.69451117999 171392.30357138903 209587.13799195812 195239.08404240402 207997.12774937903 197197.03634994186 188092.35628820959Age

Selling Price

Square feet Residual Plot

1926 2069 1720 1396 1706 1847 1950 2323 2285 3752 2300 2525 3800 1740 -49066.406014619977 -2345.7129556209984 -10239.616517664399 -29322.435475499602 14171.227106612627 27584.813911759818 5185.8704494942504 3537.3054888200131 10607.696428610972 -26587.137991958123 4760.9159575959784 3002.8722506209742 17802.96365005814 30907.643711790413Square feet

Residuals

Age Residual Plot

30 40 30 15 32 38 27 30 26 35 18 17 40 12 -49066.406014619977 -2345.7129556209984 -10239.616517664399 -29322.435475499602 14171.227106612627 27584.813911759818 5185.8704494942504 3537.3054888200131 10607.696428610972 -26587.137991958123 4760.9159575959784 3002.8722506209742 17802.96365005814 30907.643711790413Age

Residuals

Square feet Line Fit Plot

Selling Price 1926 2069 1720 1396 1706 1847 1950 2323 2285 3752 2300 2525 3800 1740 95000 119000 124800 135000 142800 145000 159000 165000 182000 183000 200000 211000 215000 219000 Predicted Selling Price 1926 2069 1720 1396 1706 1847 1950 2323 2285 3752 2300 2525 3800 1740 144066.40601461998 121345.712955621 135039.6165176644 164322.4354754996 128628.77289338737 117415.18608824018 153814.12955050575 161462.69451117999 171392.30357138903 209587.13799195812 195239.08404240402 207997.12774937903 197197.03634994186 188092.35628820959Square feet

Selling Price

Age Line Fit Plot

Selling Price 30 40 30 15 32 38 27 30 26 35 18 17 40 12 95000 119000 124800 135000 142800 145000 159000 165000 182000 183000 200000 211000 215000 219000 Predicted Selling Price 30 40 30 15 32 38 27 30 26 35 18 17 40 12 144066.40601461998 121345.712955621 135039.6165176644 164322.4354754996 128628.77289338737 117415.18608824018 153814.12955050575 161462.69451117999 171392.30357138903 209587.13799195812 195239.08404240402 207997.12774937903 197197.03634994186 188092.35628820959Age

Selling Price

Square feet Residual Plot

1926 2069 1720 1396 1706 1847 1950 2323 2285 3752 2300 2525 3800 1740 -49066.406014619977 -2345.7129556209984 -10239.616517664399 -29322.435475499602 14171.227106612627 27584.813911759818 5185.8704494942504 3537.3054888200131 10607.696428610972 -26587.137991958123 4760.9159575959784 3002.8722506209742 17802.96365005814 30907.643711790413Square feet

Residuals

Age Residual Plot

30 40 30 15 32 38 27 30 26 35 18 17 40 12 -49066.406014619977 -2345.7129556209984 -10239.616517664399 -29322.435475499602 14171.227106612627 27584.813911759818 5185.8704494942504 3537.3054888200131 10607.696428610972 -26587.137991958123 4760.9159575959784 3002.8722506209742 17802.96365005814 30907.643711790413Age

Residuals

Square feet Line Fit Plot

Selling Price 1926 2069 1720 1396 1706 1847 1950 2323 2285 3752 2300 2525 3800 1740 95000 119000 124800 135000 142800 145000 159000 165000 182000 183000 200000 211000 215000 219000 Predicted Selling Price 1926 2069 1720 1396 1706 1847 1950 2323 2285 3752 2300 2525 3800 1740 144066.40601461998 121345.712955621 135039.6165176644 164322.4354754996 128628.77289338737 117415.18608824018 153814.12955050575 161462.69451117999 171392.30357138903 209587.13799195812 195239.08404240402 207997.12774937903 197197.03634994186 188092.35628820959Square feet

Selling Price

Age Line Fit Plot

Selling Price 30 40 30 15 32 38 27 30 26 35 18 17 40 12 95000 119000 124800 135000 142800 145000 159000 165000 182000 183000 200000 211000 215000 219000 Predicted Selling Price 30 40 30 15 32 38 27 30 26 35 18 17 40 12 144066.40601461998 121345.712955621 135039.6165176644 164322.4354754996 128628.77289338737 117415.18608824018 153814.12955050575 161462.69451117999 171392.30357138903 209587.13799195812 195239.08404240402 207997.12774937903 197197.03634994186 188092.35628820959Age

Selling Price

Square feet Residual Plot

1926 2069 1720 1396 1706 1847 1950 2323 2285 3752 2300 2525 3800 1740 -49066.406014619977 -2345.7129556209984 -10239.616517664399 -29322.435475499602 14171.227106612627 27584.813911759818 5185.8704494942504 3537.3054888200131 10607.696428610972 -26587.137991958123 4760.9159575959784 3002.8722506209742 17802.96365005814 30907.643711790413Square feet

Residuals

Age Residual Plot

30 40 30 15 32 38 27 30 26 35 18 17 40 12 -49066.406014619977 -2345.7129556209984 -10239.616517664399 -29322.435475499602 14171.227106612627 27584.813911759818 5185.8704494942504 3537.3054888200131 10607.696428610972 -26587.137991958123 4760.9159575959784 3002.8722506209742 17802.96365005814 30907.643711790413Age

Residuals

Square feet Line Fit Plot

Selling Price 1926 2069 1720 1396 1706 1847 1950 2323 2285 3752 2300 2525 3800 1740 95000 119000 124800 135000 142800 145000 159000 165000 182000 183000 200000 211000 215000 219000 Predicted Selling Price 1926 2069 1720 1396 1706 1847 1950 2323 2285 3752 2300 2525 3800 1740 144066.40601461998 121345.712955621 135039.6165176644 164322.4354754996 128628.77289338737 117415.18608824018 153814.12955050575 161462.69451117999 171392.30357138903 209587.13799195812 195239.08404240402 207997.12774937903 197197.03634994186 188092.35628820959Square feet

Selling Price

Age Line Fit Plot

Selling Price 30 40 30 15 32 38 27 30 26 35 18 17 40 12 95000 119000 124800 135000 142800 145000 159000 165000 182000 183000 200000 211000 215000 219000 Predicted Selling Price 30 40 30 15 32 38 27 30 26 35 18 17 40 12 144066.40601461998 121345.712955621 135039.6165176644 164322.4354754996 128628.77289338737 117415.18608824018 153814.12955050575 161462.69451117999 171392.30357138903 209587.13799195812 195239.08404240402 207997.12774937903 197197.03634994186 188092.35628820959Age

Selling Price

## Sheet1

 Selling Price Square feet Age 95000 1926 30 119000 2069 40 124800 1720 30 135000 1396 15 142800 1706 32 145000 1847 38 159000 1950 27 165000 2323 30 182000 2285 26 183000 3752 35 200000 2300 18 211000 2525 17 215000 3800 40 219000 1740 12 SUMMARY OUTPUT Regression Statistics Multiple R 0.8196803049 R Square 0.6718758022 Adjusted R Square 0.6122168572 Standard Error 24312.6072850603 Observations 14 ANOVA df SS MS F Significance F Regression 2 13313936968.4553 6656968484.22766 11.261945743 0.0021787652 Residual 11 6502131602.97325 591102872.997569 Total 13 19816068571.4286 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 146630.893555974 25482.0828687578 5.7542742605 0.0001275664 90545.2073136126 202716.579798335 90545.2073136126 202716.579798335 Square feet 43.8193664901 10.2809650702 4.2621841618 0.0013380948 21.1911149391 66.447618041 21.1911149391 66.447618041 Age -2898.686246708 796.5649420672 -3.638982955 0.0038949963 -4651.9138632471 -1145.4586301689 -4651.9138632471 -1145.4586301689 RESIDUAL OUTPUT Observation Predicted Selling Price Residuals 1 144066.40601462 -49066.40601462 2 121345.712955621 -2345.712955621 3 135039.616517664 -10239.6165176644 4 164322.4354755 -29322.4354754996 5 128628.772893387 14171.2271066126 6 117415.18608824 27584.8139117598 7 153814.129550506 5185.8704494943 8 161462.69451118 3537.30548882 9 171392.303571389 10607.696428611 10 209587.137991958 -26587.1379919581 11 195239.084042404 4760.915957596 12 207997.127749379 3002.872250621 13 197197.036349942 17802.9636500581 14 188092.35628821 30907.6437117904

Square feet Residual Plot

1926 2069 1720 1396 1706 1847 1950 2323 2285 3752 2300 2525 3800 1740 -49066.406014619977 -2345.7129556209984 -10239.616517664399 -29322.435475499602 14171.227106612627 27584.813911759818 5185.8704494942504 3537.3054888200131 10607.696428610972 -26587.137991958123 4760.9159575959784 3002.8722506209742 17802.96365005814 30907.643711790413Square feet

Residuals

Age Residual Plot

30 40 30 15 32 38 27 30 26 35 18 17 40 12 -49066.406014619977 -2345.7129556209984 -10239.616517664399 -29322.435475499602 14171.227106612627 27584.813911759818 5185.8704494942504 3537.3054888200131 10607.696428610972 -26587.137991958123 4760.9159575959784 3002.8722506209742 17802.96365005814 30907.643711790413Age

Residuals

Square feet Line Fit Plot

Selling Price 1926 2069 1720 1396 1706 1847 1950 2323 2285 3752 2300 2525 3800 1740 95000 119000 124800 135000 142800 145000 159000 165000 182000 183000 200000 211000 215000 219000 Predicted Selling Price 1926 2069 1720 1396 1706 1847 1950 2323 2285 3752 2300 2525 3800 1740 144066.40601461998 121345.712955621 135039.6165176644 164322.4354754996 128628.77289338737 117415.18608824018 153814.12955050575 161462.69451117999 171392.30357138903 209587.13799195812 195239.08404240402 207997.12774937903 197197.03634994186 188092.35628820959Square feet

Selling Price

Age Line Fit Plot

Selling Price 30 40 30 15 32 38 27 30 26 35 18 17 40 12 95000 119000 124800 135000 142800 145000 159000 165000 182000 183000 200000 211000 215000 219000 Predicted Selling Price 30 40 30 15 32 38 27 30 26 35 18 17 40 12 144066.40601461998 121345.712955621 135039.6165176644 164322.4354754996 128628.77289338737 117415.18608824018 153814.12955050575 161462.69451117999 171392.30357138903 209587.13799195812 195239.08404240402 207997.12774937903 197197.03634994186 188092.35628820959Age

Selling Price

Square feet Residual Plot

1926 2069 1720 1396 1706 1847 1950 2323 2285 3752 2300 2525 3800 1740 -49066.406014619977 -2345.7129556209984 -10239.616517664399 -29322.435475499602 14171.227106612627 27584.813911759818 5185.8704494942504 3537.3054888200131 10607.696428610972 -26587.137991958123 4760.9159575959784 3002.8722506209742 17802.96365005814 30907.643711790413Square feet

Residuals

Age Residual Plot

30 40 30 15 32 38 27 30 26 35 18 17 40 12 -49066.406014619977 -2345.7129556209984 -10239.616517664399 -29322.435475499602 14171.227106612627 27584.813911759818 5185.8704494942504 3537.3054888200131 10607.696428610972 -26587.137991958123 4760.9159575959784 3002.8722506209742 17802.96365005814 30907.643711790413Age

Residuals

Square feet Line Fit Plot

Selling Price 1926 2069 1720 1396 1706 1847 1950 2323 2285 3752 2300 2525 3800 1740 95000 119000 124800 135000 142800 145000 159000 165000 182000 183000 200000 211000 215000 219000 Predicted Selling Price 1926 2069 1720 1396 1706 1847 1950 2323 2285 3752 2300 2525 3800 1740 144066.40601461998 121345.712955621 135039.6165176644 164322.4354754996 128628.77289338737 117415.18608824018 153814.12955050575 161462.69451117999 171392.30357138903 209587.13799195812 195239.08404240402 207997.12774937903 197197.03634994186 188092.35628820959Square feet

Selling Price

Age Line Fit Plot

Selling Price 30 40 30 15 32 38 27 30 26 35 18 17 40 12 95000 119000 124800 135000 142800 145000 159000 165000 182000 183000 200000 211000 215000 219000 Predicted Selling Price 30 40 30 15 32 38 27 30 26 35 18 17 40 12 144066.40601461998 121345.712955621 135039.6165176644 164322.4354754996 128628.77289338737 117415.18608824018 153814.12955050575 161462.69451117999 171392.30357138903 209587.13799195812 195239.08404240402 207997.12774937903 197197.03634994186 188092.35628820959Age

Selling Price

Square feet Residual Plot

1926 2069 1720 1396 1706 1847 1950 2323 2285 3752 2300 2525 3800 1740 -49066.406014619977 -2345.7129556209984 -10239.616517664399 -29322.435475499602 14171.227106612627 27584.813911759818 5185.8704494942504 3537.3054888200131 10607.696428610972 -26587.137991958123 4760.9159575959784 3002.8722506209742 17802.96365005814 30907.643711790413Square feet

Residuals

Age Residual Plot

30 40 30 15 32 38 27 30 26 35 18 17 40 12 -49066.406014619977 -2345.7129556209984 -10239.616517664399 -29322.435475499602 14171.227106612627 27584.813911759818 5185.8704494942504 3537.3054888200131 10607.696428610972 -26587.137991958123 4760.9159575959784 3002.8722506209742 17802.96365005814 30907.643711790413Age

Residuals

Square feet Line Fit Plot

Selling Price 1926 2069 1720 1396 1706 1847 1950 2323 2285 3752 2300 2525 3800 1740 95000 119000 124800 135000 142800 145000 159000 165000 182000 183000 200000 211000 215000 219000 Predicted Selling Price 1926 2069 1720 1396 1706 1847 1950 2323 2285 3752 2300 2525 3800 1740 144066.40601461998 121345.712955621 135039.6165176644 164322.4354754996 128628.77289338737 117415.18608824018 153814.12955050575 161462.69451117999 171392.30357138903 209587.13799195812 195239.08404240402 207997.12774937903 197197.03634994186 188092.35628820959Square feet

Selling Price

Age Line Fit Plot

Selling Price 30 40 30 15 32 38 27 30 26 35 18 17 40 12 95000 119000 124800 135000 142800 145000 159000 165000 182000 183000 200000 211000 215000 219000 Predicted Selling Price 30 40 30 15 32 38 27 30 26 35 18 17 40 12 144066.40601461998 121345.712955621 135039.6165176644 164322.4354754996 128628.77289338737 117415.18608824018 153814.12955050575 161462.69451117999 171392.30357138903 209587.13799195812 195239.08404240402 207997.12774937903 197197.03634994186 188092.35628820959Age

Selling Price

Square feet Residual Plot

1926 2069 1720 1396 1706 1847 1950 2323 2285 3752 2300 2525 3800 1740 -49066.406014619977 -2345.7129556209984 -10239.616517664399 -29322.435475499602 14171.227106612627 27584.813911759818 5185.8704494942504 3537.3054888200131 10607.696428610972 -26587.137991958123 4760.9159575959784 3002.8722506209742 17802.96365005814 30907.643711790413Square feet

Residuals

Age Residual Plot

30 40 30 15 32 38 27 30 26 35 18 17 40 12 -49066.406014619977 -2345.7129556209984 -10239.616517664399 -29322.435475499602 14171.227106612627 27584.813911759818 5185.8704494942504 3537.3054888200131 10607.696428610972 -26587.137991958123 4760.9159575959784 3002.8722506209742 17802.96365005814 30907.643711790413Age

Residuals

Square feet Line Fit Plot

Selling Price 1926 2069 1720 1396 1706 1847 1950 2323 2285 3752 2300 2525 3800 1740 95000 119000 124800 135000 142800 145000 159000 165000 182000 183000 200000 211000 215000 219000 Predicted Selling Price 1926 2069 1720 1396 1706 1847 1950 2323 2285 3752 2300 2525 3800 1740 144066.40601461998 121345.712955621 135039.6165176644 164322.4354754996 128628.77289338737 117415.18608824018 153814.12955050575 161462.69451117999 171392.30357138903 209587.13799195812 195239.08404240402 207997.12774937903 197197.03634994186 188092.35628820959Square feet

Selling Price

Age Line Fit Plot

Selling Price 30 40 30 15 32 38 27 30 26 35 18 17 40 12 95000 119000 124800 135000 142800 145000 159000 165000 182000 183000 200000 211000 215000 219000 Predicted Selling Price 30 40 30 15 32 38 27 30 26 35 18 17 40 12 144066.40601461998 121345.712955621 135039.6165176644 164322.4354754996 128628.77289338737 117415.18608824018 153814.12955050575 161462.69451117999 171392.30357138903 209587.13799195812 195239.08404240402 207997.12774937903 197197.03634994186 188092.35628820959Age

Selling Price

## Sheet1

 Selling Price Square feet Age 95000 1926 30 119000 2069 40 124800 1720 30 135000 1396 15 142800 1706 32 145000 1847 38 159000 1950 27 165000 2323 30 182000 2285 26 183000 3752 35 200000 2300 18 211000 2525 17 215000 3800 40 219000 1740 12 SUMMARY OUTPUT Regression Statistics Multiple R 0.8196803049 R Square 0.6718758022 Adjusted R Square 0.6122168572 Standard Error 24312.6072850603 Observations 14 ANOVA df SS MS F Significance F Regression 2 13313936968.4553 6656968484.22766 11.261945743 0.0021787652 Residual 11 6502131602.97325 591102872.997569 Total 13 19816068571.4286 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 146630.893555974 25482.0828687578 5.7542742605 0.0001275664 90545.2073136126 202716.579798335 90545.2073136126 202716.579798335 Square feet 43.8193664901 10.2809650702 4.2621841618 0.0013380948 21.1911149391 66.447618041 21.1911149391 66.447618041 Age -2898.686246708 796.5649420672 -3.638982955 0.0038949963 -4651.9138632471 -1145.4586301689 -4651.9138632471 -1145.4586301689 RESIDUAL OUTPUT Observation Predicted Selling Price Residuals 1 144066.40601462 -49066.40601462 2 121345.712955621 -2345.712955621 3 135039.616517664 -10239.6165176644 4 164322.4354755 -29322.4354754996 5 128628.772893387 14171.2271066126 6 117415.18608824 27584.8139117598 7 153814.129550506 5185.8704494943 8 161462.69451118 3537.30548882 9 171392.303571389 10607.696428611 10 209587.137991958 -26587.1379919581 11 195239.084042404 4760.915957596 12 207997.127749379 3002.872250621 13 197197.036349942 17802.9636500581 14 188092.35628821 30907.6437117904

Square feet Residual Plot

1926 2069 1720 1396 1706 1847 1950 2323 2285 3752 2300 2525 3800 1740 -49066.406014619977 -2345.7129556209984 -10239.616517664399 -29322.435475499602 14171.227106612627 27584.813911759818 5185.8704494942504 3537.3054888200131 10607.696428610972 -26587.137991958123 4760.9159575959784 3002.8722506209742 17802.96365005814 30907.643711790413Square feet

Residuals

Age Residual Plot

30 40 30 15 32 38 27 30 26 35 18 17 40 12 -49066.406014619977 -2345.7129556209984 -10239.616517664399 -29322.435475499602 14171.227106612627 27584.813911759818 5185.8704494942504 3537.3054888200131 10607.696428610972 -26587.137991958123 4760.9159575959784 3002.8722506209742 17802.96365005814 30907.643711790413Age

Residuals

Square feet Line Fit Plot

Selling Price 1926 2069 1720 1396 1706 1847 1950 2323 2285 3752 2300 2525 3800 1740 95000 119000 124800 135000 142800 145000 159000 165000 182000 183000 200000 211000 215000 219000 Predicted Selling Price 1926 2069 1720 1396 1706 1847 1950 2323 2285 3752 2300 2525 3800 1740 144066.40601461998 121345.712955621 135039.6165176644 164322.4354754996 128628.77289338737 117415.18608824018 153814.12955050575 161462.69451117999 171392.30357138903 209587.13799195812 195239.08404240402 207997.12774937903 197197.03634994186 188092.35628820959Square feet

Selling Price

Age Line Fit Plot

Selling Price 30 40 30 15 32 38 27 30 26 35 18 17 40 12 95000 119000 124800 135000 142800 145000 159000 165000 182000 183000 200000 211000 215000 219000 Predicted Selling Price 30 40 30 15 32 38 27 30 26 35 18 17 40 12 144066.40601461998 121345.712955621 135039.6165176644 164322.4354754996 128628.77289338737 117415.18608824018 153814.12955050575 161462.69451117999 171392.30357138903 209587.13799195812 195239.08404240402 207997.12774937903 197197.03634994186 188092.35628820959Age

Selling Price

Square feet Residual Plot

1926 2069 1720 1396 1706 1847 1950 2323 2285 3752 2300 2525 3800 1740 -49066.406014619977 -2345.7129556209984 -10239.616517664399 -29322.435475499602 14171.227106612627 27584.813911759818 5185.8704494942504 3537.3054888200131 10607.696428610972 -26587.137991958123 4760.9159575959784 3002.8722506209742 17802.96365005814 30907.643711790413Square feet

Residuals

Age Residual Plot

30 40 30 15 32 38 27 30 26 35 18 17 40 12 -49066.406014619977 -2345.7129556209984 -10239.616517664399 -29322.435475499602 14171.227106612627 27584.813911759818 5185.8704494942504 3537.3054888200131 10607.696428610972 -26587.137991958123 4760.9159575959784 3002.8722506209742 17802.96365005814 30907.643711790413Age

Residuals

Square feet Line Fit Plot

Selling Price 1926 2069 1720 1396 1706 1847 1950 2323 2285 3752 2300 2525 3800 1740 95000 119000 124800 135000 142800 145000 159000 165000 182000 183000 200000 211000 215000 219000 Predicted Selling Price 1926 2069 1720 1396 1706 1847 1950 2323 2285 3752 2300 2525 3800 1740 144066.40601461998 121345.712955621 135039.6165176644 164322.4354754996 128628.77289338737 117415.18608824018 153814.12955050575 161462.69451117999 171392.30357138903 209587.13799195812 195239.08404240402 207997.12774937903 197197.03634994186 188092.35628820959Square feet

Selling Price

Age Line Fit Plot

Selling Price 30 40 30 15 32 38 27 30 26 35 18 17 40 12 95000 119000 124800 135000 142800 145000 159000 165000 182000 183000 200000 211000 215000 219000 Predicted Selling Price 30 40 30 15 32 38 27 30 26 35 18 17 40 12 144066.40601461998 121345.712955621 135039.6165176644 164322.4354754996 128628.77289338737 117415.18608824018 153814.12955050575 161462.69451117999 171392.30357138903 209587.13799195812 195239.08404240402 207997.12774937903 197197.03634994186 188092.35628820959Age

Selling Price

Square feet Residual Plot

1926 2069 1720 1396 1706 1847 1950 2323 2285 3752 2300 2525 3800 1740 -49066.406014619977 -2345.7129556209984 -10239.616517664399 -29322.435475499602 14171.227106612627 27584.813911759818 5185.8704494942504 3537.3054888200131 10607.696428610972 -26587.137991958123 4760.9159575959784 3002.8722506209742 17802.96365005814 30907.643711790413Square feet

Residuals

Age Residual Plot

30 40 30 15 32 38 27 30 26 35 18 17 40 12 -49066.406014619977 -2345.7129556209984 -10239.616517664399 -29322.435475499602 14171.227106612627 27584.813911759818 5185.8704494942504 3537.3054888200131 10607.696428610972 -26587.137991958123 4760.9159575959784 3002.8722506209742 17802.96365005814 30907.643711790413Age

Residuals

Square feet Line Fit Plot

Selling Price 1926 2069 1720 1396 1706 1847 1950 2323 2285 3752 2300 2525 3800 1740 95000 119000 124800 135000 142800 145000 159000 165000 182000 183000 200000 211000 215000 219000 Predicted Selling Price 1926 2069 1720 1396 1706 1847 1950 2323 2285 3752 2300 2525 3800 1740 144066.40601461998 121345.712955621 135039.6165176644 164322.4354754996 128628.77289338737 117415.18608824018 153814.12955050575 161462.69451117999 171392.30357138903 209587.13799195812 195239.08404240402 207997.12774937903 197197.03634994186 188092.35628820959Square feet

Selling Price

Age Line Fit Plot

Selling Price 30 40 30 15 32 38 27 30 26 35 18 17 40 12 95000 119000 124800 135000 142800 145000 159000 165000 182000 183000 200000 211000 215000 219000 Predicted Selling Price 30 40 30 15 32 38 27 30 26 35 18 17 40 12 144066.40601461998 121345.712955621 135039.6165176644 164322.4354754996 128628.77289338737 117415.18608824018 153814.12955050575 161462.69451117999 171392.30357138903 209587.13799195812 195239.08404240402 207997.12774937903 197197.03634994186 188092.35628820959Age

Selling Price

Square feet Residual Plot

1926 2069 1720 1396 1706 1847 1950 2323 2285 3752 2300 2525 3800 1740 -49066.406014619977 -2345.7129556209984 -10239.616517664399 -29322.435475499602 14171.227106612627 27584.813911759818 5185.8704494942504 3537.3054888200131 10607.696428610972 -26587.137991958123 4760.9159575959784 3002.8722506209742 17802.96365005814 30907.643711790413Square feet

Residuals

Age Residual Plot

30 40 30 15 32 38 27 30 26 35 18 17 40 12 -49066.406014619977 -2345.7129556209984 -10239.616517664399 -29322.435475499602 14171.227106612627 27584.813911759818 5185.8704494942504 3537.3054888200131 10607.696428610972 -26587.137991958123 4760.9159575959784 3002.8722506209742 17802.96365005814 30907.643711790413Age

Residuals

Square feet Line Fit Plot

Selling Price 1926 2069 1720 1396 1706 1847 1950 2323 2285 3752 2300 2525 3800 1740 95000 119000 124800 135000 142800 145000 159000 165000 182000 183000 200000 211000 215000 219000 Predicted Selling Price 1926 2069 1720 1396 1706 1847 1950 2323 2285 3752 2300 2525 3800 1740 144066.40601461998 121345.712955621 135039.6165176644 164322.4354754996 128628.77289338737 117415.18608824018 153814.12955050575 161462.69451117999 171392.30357138903 209587.13799195812 195239.08404240402 207997.12774937903 197197.03634994186 188092.35628820959Square feet

Selling Price

Age Line Fit Plot

Selling Price 30 40 30 15 32 38 27 30 26 35 18 17 40 12 95000 119000 124800 135000 142800 145000 159000 165000 182000 183000 200000 211000 215000 219000 Predicted Selling Price 30 40 30 15 32 38 27 30 26 35 18 17 40 12 144066.40601461998 121345.712955621 135039.6165176644 164322.4354754996 128628.77289338737 117415.18608824018 153814.12955050575 161462.69451117999 171392.30357138903 209587.13799195812 195239.08404240402 207997.12774937903 197197.03634994186 188092.35628820959Age

Selling Price

## Sheet1

 Selling Price Square feet Age 95000 1926 30 119000 2069 40 124800 1720 30 135000 1396 15 142800 1706 32 145000 1847 38 159000 1950 27 165000 2323 30 182000 2285 26 183000 3752 35 200000 2300 18 211000 2525 17 215000 3800 40 219000 1740 12 SUMMARY OUTPUT Regression Statistics Multiple R 0.8196803049 R Square 0.6718758022 Adjusted R Square 0.6122168572 Standard Error 24312.6072850603 Observations 14 ANOVA df SS MS F Significance F Regression 2 13313936968.4553 6656968484.22766 11.261945743 0.0021787652 Residual 11 6502131602.97325 591102872.997569 Total 13 19816068571.4286 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 146630.893555974 25482.0828687578 5.7542742605 0.0001275664 90545.2073136126 202716.579798335 90545.2073136126 202716.579798335 Square feet 43.8193664901 10.2809650702 4.2621841618 0.0013380948 21.1911149391 66.447618041 21.1911149391 66.447618041 Age -2898.686246708 796.5649420672 -3.638982955 0.0038949963 -4651.9138632471 -1145.4586301689 -4651.9138632471 -1145.4586301689 RESIDUAL OUTPUT Observation Predicted Selling Price Residuals 1 144066.40601462 -49066.40601462 2 121345.712955621 -2345.712955621 3 135039.616517664 -10239.6165176644 4 164322.4354755 -29322.4354754996 5 128628.772893387 14171.2271066126 6 117415.18608824 27584.8139117598 7 153814.129550506 5185.8704494943 8 161462.69451118 3537.30548882 9 171392.303571389 10607.696428611 10 209587.137991958 -26587.1379919581 11 195239.084042404 4760.915957596 12 207997.127749379 3002.872250621 13 197197.036349942 17802.9636500581 14 188092.35628821 30907.6437117904

Square feet Residual Plot

1926 2069 1720 1396 1706 1847 1950 2323 2285 3752 2300 2525 3800 1740 -49066.406014619977 -2345.7129556209984 -10239.616517664399 -29322.435475499602 14171.227106612627 27584.813911759818 5185.8704494942504 3537.3054888200131 10607.696428610972 -26587.137991958123 4760.9159575959784 3002.8722506209742 17802.96365005814 30907.643711790413Square feet

Residuals

Age Residual Plot

30 40 30 15 32 38 27 30 26 35 18 17 40 12 -49066.406014619977 -2345.7129556209984 -10239.616517664399 -29322.435475499602 14171.227106612627 27584.813911759818 5185.8704494942504 3537.3054888200131 10607.696428610972 -26587.137991958123 4760.9159575959784 3002.8722506209742 17802.96365005814 30907.643711790413Age

Residuals

Square feet Line Fit Plot

Selling Price 1926 2069 1720 1396 1706 1847 1950 2323 2285 3752 2300 2525 3800 1740 95000 119000 124800 135000 142800 145000 159000 165000 182000 183000 200000 211000 215000 219000 Predicted Selling Price 1926 2069 1720 1396 1706 1847 1950 2323 2285 3752 2300 2525 3800 1740 144066.40601461998 121345.712955621 135039.6165176644 164322.4354754996 128628.77289338737 117415.18608824018 153814.12955050575 161462.69451117999 171392.30357138903 209587.13799195812 195239.08404240402 207997.12774937903 197197.03634994186 188092.35628820959Square feet

Selling Price

Age Line Fit Plot

Selling Price 30 40 30 15 32 38 27 30 26 35 18 17 40 12 95000 119000 124800 135000 142800 145000 159000 165000 182000 183000 200000 211000 215000 219000 Predicted Selling Price 30 40 30 15 32 38 27 30 26 35 18 17 40 12 144066.40601461998 121345.712955621 135039.6165176644 164322.4354754996 128628.77289338737 117415.18608824018 153814.12955050575 161462.69451117999 171392.30357138903 209587.13799195812 195239.08404240402 207997.12774937903 197197.03634994186 188092.35628820959Age

Selling Price

Square feet Residual Plot

1926 2069 1720 1396 1706 1847 1950 2323 2285 3752 2300 2525 3800 1740 -49066.406014619977 -2345.7129556209984 -10239.616517664399 -29322.435475499602 14171.227106612627 27584.813911759818 5185.8704494942504 3537.3054888200131 10607.696428610972 -26587.137991958123 4760.9159575959784 3002.8722506209742 17802.96365005814 30907.643711790413Square feet

Residuals

Age Residual Plot

30 40 30 15 32 38 27 30 26 35 18 17 40 12 -49066.406014619977 -2345.7129556209984 -10239.616517664399 -29322.435475499602 14171.227106612627 27584.813911759818 5185.8704494942504 3537.3054888200131 10607.696428610972 -26587.137991958123 4760.9159575959784 3002.8722506209742 17802.96365005814 30907.643711790413Age

Residuals

Square feet Line Fit Plot

Selling Price 1926 2069 1720 1396 1706 1847 1950 2323 2285 3752 2300 2525 3800 1740 95000 119000 124800 135000 142800 145000 159000 165000 182000 183000 200000 211000 215000 219000 Predicted Selling Price 1926 2069 1720 1396 1706 1847 1950 2323 2285 3752 2300 2525 3800 1740 144066.40601461998 121345.712955621 135039.6165176644 164322.4354754996 128628.77289338737 117415.18608824018 153814.12955050575 161462.69451117999 171392.30357138903 209587.13799195812 195239.08404240402 207997.12774937903 197197.03634994186 188092.35628820959Square feet

Selling Price

Age Line Fit Plot

Selling Price 30 40 30 15 32 38 27 30 26 35 18 17 40 12 95000 119000 124800 135000 142800 145000 159000 165000 182000 183000 200000 211000 215000 219000 Predicted Selling Price 30 40 30 15 32 38 27 30 26 35 18 17 40 12 144066.40601461998 121345.712955621 135039.6165176644 164322.4354754996 128628.77289338737 117415.18608824018 153814.12955050575 161462.69451117999 171392.30357138903 209587.13799195812 195239.08404240402 207997.12774937903 197197.03634994186 188092.35628820959Age

Selling Price

Square feet Residual Plot

1926 2069 1720 1396 1706 1847 1950 2323 2285 3752 2300 2525 3800 1740 -49066.406014619977 -2345.7129556209984 -10239.616517664399 -29322.435475499602 14171.227106612627 27584.813911759818 5185.8704494942504 3537.3054888200131 10607.696428610972 -26587.137991958123 4760.9159575959784 3002.8722506209742 17802.96365005814 30907.643711790413Square feet

Residuals

Age Residual Plot

30 40 30 15 32 38 27 30 26 35 18 17 40 12 -49066.406014619977 -2345.7129556209984 -10239.616517664399 -29322.435475499602 14171.227106612627 27584.813911759818 5185.8704494942504 3537.3054888200131 10607.696428610972 -26587.137991958123 4760.9159575959784 3002.8722506209742 17802.96365005814 30907.643711790413Age

Residuals

Square feet Line Fit Plot

Selling Price 1926 2069 1720 1396 1706 1847 1950 2323 2285 3752 2300 2525 3800 1740 95000 119000 124800 135000 142800 145000 159000 165000 182000 183000 200000 211000 215000 219000 Predicted Selling Price 1926 2069 1720 1396 1706 1847 1950 2323 2285 3752 2300 2525 3800 1740 144066.40601461998 121345.712955621 135039.6165176644 164322.4354754996 128628.77289338737 117415.18608824018 153814.12955050575 161462.69451117999 171392.30357138903 209587.13799195812 195239.08404240402 207997.12774937903 197197.03634994186 188092.35628820959Square feet

Selling Price

Age Line Fit Plot

Selling Price 30 40 30 15 32 38 27 30 26 35 18 17 40 12 95000 119000 124800 135000 142800 145000 159000 165000 182000 183000 200000 211000 215000 219000 Predicted Selling Price 30 40 30 15 32 38 27 30 26 35 18 17 40 12 144066.40601461998 121345.712955621 135039.6165176644 164322.4354754996 128628.77289338737 117415.18608824018 153814.12955050575 161462.69451117999 171392.30357138903 209587.13799195812 195239.08404240402 207997.12774937903 197197.03634994186 188092.35628820959Age

Selling Price

Square feet Residual Plot

1926 2069 1720 1396 1706 1847 1950 2323 2285 3752 2300 2525 3800 1740 -49066.406014619977 -2345.7129556209984 -10239.616517664399 -29322.435475499602 14171.227106612627 27584.813911759818 5185.8704494942504 3537.3054888200131 10607.696428610972 -26587.137991958123 4760.9159575959784 3002.8722506209742 17802.96365005814 30907.643711790413Square feet

Residuals

Age Residual Plot

30 40 30 15 32 38 27 30 26 35 18 17 40 12 -49066.406014619977 -2345.7129556209984 -10239.616517664399 -29322.435475499602 14171.227106612627 27584.813911759818 5185.8704494942504 3537.3054888200131 10607.696428610972 -26587.137991958123 4760.9159575959784 3002.8722506209742 17802.96365005814 30907.643711790413Age

Residuals

Square feet Line Fit Plot

Selling Price 1926 2069 1720 1396 1706 1847 1950 2323 2285 3752 2300 2525 3800 1740 95000 119000 124800 135000 142800 145000 159000 165000 182000 183000 200000 211000 215000 219000 Predicted Selling Price 1926 2069 1720 1396 1706 1847 1950 2323 2285 3752 2300 2525 3800 1740 144066.40601461998 121345.712955621 135039.6165176644 164322.4354754996 128628.77289338737 117415.18608824018 153814.12955050575 161462.69451117999 171392.30357138903 209587.13799195812 195239.08404240402 207997.12774937903 197197.03634994186 188092.35628820959Square feet

Selling Price

Age Line Fit Plot

Selling Price 30 40 30 15 32 38 27 30 26 35 18 17 40 12 95000 119000 124800 135000 142800 145000 159000 165000 182000 183000 200000 211000 215000 219000 Predicted Selling Price 30 40 30 15 32 38 27 30 26 35 18 17 40 12 144066.40601461998 121345.712955621 135039.6165176644 164322.4354754996 128628.77289338737 117415.18608824018 153814.12955050575 161462.69451117999 171392.30357138903 209587.13799195812 195239.08404240402 207997.12774937903 197197.03634994186 188092.35628820959Age

Selling Price