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Question

Module 1-201810 Fall 2 x y "A Homework 1 C | www.webassign.netweb/Student/Assignment-Responses/submit?de: 168827 10 17. 1S points Previous Answers PeckDev Stat? S.E O23 My Hote% + Ask Your Teacher | The following data on sale price, size, and land-to-building ratio for 10 large industrial properties appeared in a paper Sale Price Size Land-to- (millions (thousands Building Property of dollars) of sq, ft. 2165 752 2421 223 917 2867 1698 1045 1109 406 Ratio 2.0 3.4 3.5 4.8 1.8 2.2 3.2 4.9 7.7 17.3 10.5 2.6 30.6 20.1 8.1 10.2 6.6 10 4.4 (a) Calculate the value of the correlation coefficient between sale price and size. (Give the answer to three decimal places.) (b) Calculate the value of the corelation coefficient between sale price and land-to-building ratio. (Give the answer to three decimal places.) (c) If you wanted to predict sale price and you could use either size or land-to-building ratio as the basis for making pradictions, which would you use? Size (d) Based on your choice in Part (c), find the equation of the least-squares regression line you would use for predicting y-sale price. (Give answers to three decimal places.) Need Help? Lean. 1Mktontei n10:20 PM 10/6/2017

Explanation / Answer

To fine correlation coefficient using excel, use ‘=correl(data range 1, data range 2)’

Correlation coefficient between sale price and size = 0.702109

Correlation coefficient between sale price and land-to-building ratio =-0.33918

Since, correlation coefficient is high between sale price and size, the variable to enter into the regression model is ‘size’.

To fine the regression coefficient in excel,

Data->data analysis->regression

Enter the X and Y input ranges. The output is given as follows:

SUMMARY OUTPUT

Regression Statistics

Multiple R

0.702109

R Square

0.492957

Adjusted R Square

0.429576

Standard Error

6.71735

Observations

ANOVA

Significance F

Regression

350.9536557

350.9537

7.777746723

0.023599727

Residual

360.9823443

45.12279

Total

711.936

Coefficients

Standard Error

t Stat

P-value

Lower 95%

Upper 95%

Lower 95.0%

Upper 95.0%

Intercept

1.293187

3.799877899

0.340323

0.742376879

-7.46934697

10.05572

-7.46935

10.05572

size

0.005292

0.001897658

2.788861

0.023599727

0.000916297

0.009668

0.000916

0.009668

Equation of the least square:

y = 1.293187 + 0.005292*X