The following data was collected to explore how the number of square feet in a h

Question

The following data was collected to explore how the number of square feet in a house, the number of bedrooms, and the age of the house affect the selling price of the house. The dependent variable is the selling price of the house, the first independent variable (x1) is the square footage, the second independent variable (x2) is the number of bedrooms, and the third independent variable (x3) is the age of the house.

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Step 1 of 2 :

Find the p-value for the regression equation that fits the given data. Round your answer to four decimal places.

HOW TO PULL THIS UP IN EXCEL-----------------------------------------

Effects on Selling Price of Houses Square Feet Number of Bedrooms Age Selling Price 2049 5 5 282900 10101 4 8 268600 1033 3 9 137900 1286 2 8 114900 2920 4 4 113000 2443 10 10 154700 2206 2 5 234000 1360 3 4 183400 2405 3 1 193100

Explanation / Answer

In Excel go to the data analysis.

Then click on Regression. In Regression window insert one column of Y in Input Y range and 3 columns of X independent variables in X range. And click ok then you will get regression output.

Below is the regression output

SUMMARY OUTPUT

Regression Statistics

Multiple R

0.568848565

R Square

0.32358869

Adjusted R Square

-0.0822581

Standard Error

66044.74984

Observations

9

ANOVA

df

SS

MS

F

Significance F

Regression

3

1.04E+10

3.48E+09

0.797317

0.54602

Residual

5

2.18E+10

4.36E+09

Total

8

3.22E+10

Coefficients

Standard Error

t Stat

P-value

Lower 95%

Upper 95%

Lower 95.0%

Upper 95.0%

Intercept

183709.641

58385.22

3.146509

0.025479

33625.65

333793.6

33625.65

333793.6

X Variable 1

12.26279222

8.510276

1.440939

0.209161

-9.61357

34.13915

-9.61357

34.13915

X Variable 2

2282.447

10603.7

0.21525

0.838077

-24975.2

29540.12

-24975.2

29540.12

X Variable 3

-6842.06831

8998.016

-0.7604

0.481315

-29972.2

16288.07

-29972.2

16288.07

From the regression output,

P value = 0.5460

P value > 0.05 so we cannot reject null hypothesis.

We can conclude that the model is not significant at 5% level of significance. The given regression model is not good fit for the data.

SUMMARY OUTPUT

Regression Statistics

Multiple R

0.568848565

R Square

0.32358869

Adjusted R Square

-0.0822581

Standard Error

66044.74984

Observations

9

ANOVA

df

SS

MS

F

Significance F

Regression

3

1.04E+10

3.48E+09

0.797317

0.54602

Residual

5

2.18E+10

4.36E+09

Total

8

3.22E+10

Coefficients

Standard Error

t Stat

P-value

Lower 95%

Upper 95%

Lower 95.0%

Upper 95.0%

Intercept

183709.641

58385.22

3.146509

0.025479

33625.65

333793.6

33625.65

333793.6

X Variable 1

12.26279222

8.510276

1.440939

0.209161

-9.61357

34.13915

-9.61357

34.13915

X Variable 2

2282.447

10603.7

0.21525

0.838077

-24975.2

29540.12

-24975.2

29540.12

X Variable 3

-6842.06831

8998.016

-0.7604

0.481315

-29972.2

16288.07

-29972.2

16288.07

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