Regression Paper- state decision rule and use sample

I have been assigned a Regression Paper. The project is to Project selling price (in thousands) if mean square feet of homes is 3,000 sqft. See attached spreadsheet. I must use the below null and alternative hypothesis?

H0: Projected selling price (in thousands) if mean square feet of homes is 3,000 sqft by regression is valid.

H1: Projected selling price (in thousands) if mean square feet of homes is 3,000 sqft by regression is not valid.

Regression Paper
Using numerical data from one of the data sets available through the "Data Sets" link on your page, develop one research question and formulate a hypothesis which can be tested with linear regression analysis.

Prepare a 1,050-1,750-word paper describing the results of the linear regression analysis on your collected data. Be sure to include the following in your paper:

a) Formulate a hypothesis statement regarding your research issue.

b) Perform a regression hypothesis test on the data.

c) Interpret the results of your regression hypothesis test.

Be sure to include your raw data tables and the results of your computations in your paper, using both graphical and tabular methods of displaying data and results.

© SolutionLibrary Inc. solutionlibary.com 9836dcf9d7 https://solutionlibrary.com/statistics/correlation-and-regression-analysis/regression-paper-state-decision-rule-and-use-sample-7egr

Solution Preview

...xist between the price of the house and the size of the house in square feet.

b) Perform a regression hypothesis test on the data.

Null hypothesis:
H0: There is no linear relationship exist between the price of the house and the size of the house in square feet.
Alternative hypothesis:
H1: There is a linear relationship exist between the price of the house and the size of the house in square feet.

So using Microsoft excel we can find the linear regression equation the steps involved to solve is shown below
Data Data Analysis Regression

SUMMARY OUTPUT

Regression Statistics
Multiple R 0.37
R Square 0.14
Adjusted R Square 0.13
Standard Error 44
Observations 105

ANOVA
df SS MS F Significance F
Regression 1 31770.204 31770.2 16.4441 1E-04
Residual 103 198997.38 1932.01
Total 104 230767.59

Coefficients Standard Error t Stat P-value Lower 95% Upper 95%
Intercept 64.8 38.7841 1.67061 0.09783 -12.126 141.7
X Variable 1 0.07 0.0173334 4.05513 9.8E-05 0.0359 0.105

The regression equation is
Price = 64.8 + 0.07 size
Conclusion:
Since the p value is less than 0.05 given level of significance there is no evidence to accept the null hypothesis. Hence we conclude that there is a linear relationship exists between the price of the house and the size of the house in square feet.

c) Interpret the results of your regression hypothesis test.

Since the p value is less than 0.05 given level of significance there is no evidence to accept the null hypothesis. Hence we conclude that there is a linear relationship exists between the price of the house ...