Write down the regression equation state the r-squared


Question - Most of the time houses prices depend on the local market conditions. In addition one of the factors is the number of bedrooms (as bedrooms increase prices increases). Recently Come Real Estate Agency has conducted a survey and selected a random sample of 211 for July 2015 sale in Melbourne and the data analyzed is summarized as follows. 

SUMMARY OUTPUT

Regression Statistics

Multiple R

0.817326539

R Square

0.668022671

Adjusted R Square

0.66615763

Standard Error

115.8071494

Observations

180

 

ANOVA

 

 

 

 

 

 

df

SS

MS

F

Significance F

Regression

1

4803674

4803674

358.1811918

1.74254E-44

Residual

178

2387211

13411.3

 

 

Total

179

7190885

 

 

 

 

 

Coefficients

Standard Error

t Stat

P-value

Lower 95%

Upper 95%

Lower 95.0%

Upper

95.0%

Intercept

-137.8814237

25.56878832

-5.39257

2.18686E-07

-188.3383819

-87.4244654

-188.3383819

-87.424465

Bedrooms

178.6267021

9.438326385

18.92568

1.74254E-44

160.0012892

197.252115

160.0012892

197.25212

 

House Price 

 

 

Mean

317.6166667

Standard Error

14.93923611

Median

271.8

Mode

230.5

Standard Deviation

200.4308849

Sample Variance

40172.53961

Kurtosis

9.869866365

Skewness

2.287541039

Range

1544.3

a) Write down the regression equation.

b) State the R-squared value and the standard error and explain what they mean with respect to the data. 

c) Write down the value of the gradient of the regression line and explain what it means for this data.

d) Are the values for the constant and the gradient (slope) significant (i.e. significantly different from zero) in this case? Justify your answer. 

e) Conduct a hypothesis test on the slope coefficient to test whether there is a linear relationship between number of bedrooms and prices of the houses. Include the null and alternative hypotheses; key test results and an appropriate conclusion.

f) Does the linear regression provide a good model? Give statistical reasons based on the scatterplot, p-values, the standard error and coefficient of determination.

g) If you were developing a model to predict the prices of the houses on the number of bedrooms, what other factors would you like to be able to include?

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Basic Statistics: Write down the regression equation state the r-squared
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