Fill This Form To Receive Instant Help

Help in Homework
trustpilot ratings
google ratings


Homework answers / question archive / Question1)One of the most common questions of prospective house buyers pertains to the cost of heating in dollars (Y)

Question1)One of the most common questions of prospective house buyers pertains to the cost of heating in dollars (Y)

Statistics

Question1)One of the most common questions of prospective house buyers pertains to the cost of heating in dollars (Y). To provide its customers with information on that matter, a large real estate firm used the following 4 variables to predict heating costs: the daily minimum outside temperature in degrees of Fahrenheit (X1) the amount of insulation in inches (X2), the number of windows in the house (X3), and the age of the furnace in years (X4). Given below is the Excel output for the regression model.

mso-para-margin-bottom:.0001pt; line-height:200%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;}

 

   
 

 

Regression Statistics

         

R Square

0.808

         

Observations

20

         
             

ANOVA

           

 

df

SS

MS

F

Significance F

 

Regression

4

169503.4241

42375.8600

15.7874

0.0000

 

Residual

15

40262.3259

2684.1550

     

Total

19

209765.7500

 

 

 

 
             

 

Coefficients

Standard Error

t Stat

P-value

Lower 90%

Upper 90%

Intercept

421.4277

77.8614

5.4125

0.0000

284.9327

557.9227

X1 (Temperature)

-4.5098

0.8129

-5.5476

0.0000

-5.9349

-3.0847

X2 (Insulation)

-14.9029

5.0508

-2.5905

0.0099

-23.7573

-6.0485

X3 (Windows)

0.2151

4.8675

0.0442

0.9653

-8.3181

8.7484

X4 (Furnace Age)

6.3780

4.1026

1.5546

0.1408

-0.8140

13.5702



According to these results, the proportion of variability in heating costs explained by the regression model is ________  ( use three decimals)

     
       

https://fiu.blackboard.com/images/ci/icons/generic_updown.gifQuestion 2

 

   
 

A professor of industrial relations believes that an individual's wage rate at a factory (Y) depends on his performance rating (X1) and the number of economics courses the employee successfully completed in college (X2). The professor randomly selects 6 workers and collects the following information:

Employee

Y ($)

X1

X2

1

10

3

0

2

12

1

5

3

15

8

1

4

17

5

8

5

20

7

12

6

25

10

9


Use this information to run a multiple regression in Excel. (you can try copying and pasting the table above so that you don't have to type in the numbers).

  The margin of error for a 90% confidence interval of the intercept of the regression line is ___________________  (use three decimals)

     
       

https://fiu.blackboard.com/images/ci/icons/generic_updown.gifQuestion 3

 

   
 

A professor of industrial relations believes that an individual's wage rate at a factory (Y) depends on his performance rating (X1) and the number of economics courses the employee successfully completed in college (X2). The professor randomly selects 6 workers and collects information to build a regression model.

For this regression model, wage rate is ____________________

     
       

https://fiu.blackboard.com/images/ci/icons/generic_updown.gifQuestion 4

 

   
 

The director of cooperative education at a state college wants to examine the effect of cooperative education job experience on marketability in the work place. She takes a random sample of 4 students. For these 4, she finds out how many times each had a cooperative education job and how many job offers they received upon graduation. These data are presented in the table below.

mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;} table.MsoTableGrid {mso-style-name:"Table Grid"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-priority:59; mso-style-unhide:no; border:solid windowtext 1.0pt; mso-border-alt:solid windowtext .5pt; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-border-insideh:.5pt solid windowtext; mso-border-insidev:.5pt solid windowtext; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Calibri","sans-serif"; mso-bidi-font-family:"Times New Roman";}

Student

CoopJobs

JobOffer

1

1

4

2

2

6

3

1

3

4

0

1


The standard error of the regression is  _________
 

     
       

https://fiu.blackboard.com/images/ci/icons/generic_updown.gifQuestion 5

 

   
 

A real estate builder wishes to determine how house size (House) is influenced by family income (Income), family size (Size), and education of the head of household (School). House size is measured in hundreds of square feet, income is measured in thousands of dollars, and education is in years. The builder randomly selected 50 families and ran the multiple regression. Microsoft Excel output is provided below

mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;}

Regression Statistics

Multiple R 0.865

R Square 0.748

Adjusted R Square 0.726

Standard Error 5.195

Observations 50

ANOVA

 

df

SS

MS

F

Signif F

Regression

 

3605.7736

1201.9245

 

0.0000

Residual

 

1214.2264

26.3962

Total

49

4820.0000

 

 

 

Coeff

StdError

t Stat

p-value

Intercept

-1.6335

5.8078

-0.281

0.7798

Income

0.4485

0.1137

3.9545

0.0003

Size

4.2615

0.8062

5.286

0.0001

School

-0.6517

0.4319

-1.509

0.1383


one individual in the sample had an annual income of $100,000, a family size of 10, and an education of 16 years. This individual owned a home with an area of 7,000 square feet (House = 70.00). What is the residual (in hundreds of square feet) for this data point?  (use three decimals)

     
       

https://fiu.blackboard.com/images/ci/icons/generic_updown.gifQuestion 6

 

   
 

The department head of the accounting department wanted to see if she could predict the GPA of students using the number of course units (credits) and total SAT scores of each. She takes a sample of students and generates the following Microsoft Excel output:

Summary Output - Regression Statistics

Multiple R

0.916

R Square

?

Adjusted R Square

0.732

Standard Error

 0.247

Observations

6


ANOVA

 

df

SS

MS

F

Signif F

Regression

2

0.95219

0.4761

7.813

0.0646

Residual

3

0.18281

0.06094

   

Total

5

1.135

     


mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;}

 

Coeff

StdError

Intercept

4.593897

1.133742

Units

-0.247270

0.062685

SAT Total

0.001443

0.001012


The value of the test statistic for the intercept of the regression line is ________ (use three decimals)

     
       

https://fiu.blackboard.com/images/ci/icons/generic_updown.gifQuestion 7

 

   
 

A large national bank charges local companies for using their services. A bank official reported the results of a regression analysis designed to predict the bank's charges (Y)-measured in dollars per month-for services rendered to local companies. One independent variable used to predict service charges to a company is the company's sales revenue (X) -measured in millions of dollars. Data for 21 companies who use the bank's services were used to fit the model.  The results of the simple linear regression are provided below.

    Y = -2,700 + 20X


According to these results, the error (residuals ) degrees of freedom for this model is  ________

     
       

https://fiu.blackboard.com/images/ci/icons/generic_updown.gifQuestion 8

 

   
 

One of the most common questions of prospective house buyers pertains to the cost of heating in dollars (Y). To provide its customers with information on that matter, a large real estate firm used the following 4 variables to predict heating costs: the daily minimum outside temperature in degrees of Fahrenheit (X1) the amount of insulation in inches (X2), the number of windows in the house (X3), and the age of the furnace in years (X4). Given below is the Excel output for the regression model.

mso-para-margin-bottom:.0001pt; line-height:200%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;}

Regression Statistics

         

R Square

0.808

         

Adjusted R Square

0.7568

         

Observations

20

         
             

ANOVA

           

 

df

SS

MS

F

Significance F

 

Regression

4

169503.4241

42375.8600

15.7874

0.0000

 

Residual

15

40262.3259

2684.1550

     

Total

19

209765.7500

 

 

 

 
             

 

Coefficients

Standard Error

t Stat

P-value

Lower 90%

Upper 90%

Intercept

421.4277

77.8614

5.4125

0.0000

284.9327

557.9227

X1 (Temperature)

-4.5098

0.8129

-5.5476

0.0000

-5.9349

-3.0847

X2 (Insulation)

-14.9029

5.0508

-2.5905

0.0099

-23.7573

-6.0485

X3 (Windows)

0.2151

4.8675

0.0442

0.9653

-8.3181

8.7484

X4 (Furnace Age)

6.3780

4.1026

1.5546

0.1408

-0.8140

13.5702



The margin of error for a 99% confidence interval for the coefficient of Insulation is  ________

     
       

https://fiu.blackboard.com/images/ci/icons/generic_updown.gifQuestion 9

 

   
 

The department head of the accounting department wanted to see if she could predict the GPA of students using the number of course units (credits) and total SAT scores of each. She takes a sample of students and generates the following Microsoft Excel output:

Summary Output - Regression Statistics

Multiple R

0.916

R Square

?

Adjusted R Square

0.732

Standard Error

 0.247

Observations

6


ANOVA

 

df

SS

MS

F

Signif F

Regression

2

0.95219

0.4761

7.813

0.0646

Residual

3

0.18281

0.06094

   

Total

5

1.135

     

 

 

Coeff

StdError

Intercept

4.594

1.134

Units

-0.247

0.063

SAT Total

0.001

0.001



The proportion of variability in the response variable explained by the regression model is ________ (use three decimals)

     
       

https://fiu.blackboard.com/images/ci/icons/generic_updown.gifQuestion 10

 

   
 

One of the most common questions of prospective house buyers pertains to the cost of heating in dollars (Y). To provide its customers with information on that matter, a large real estate firm used the following 4 variables to predict heating costs: the daily minimum outside temperature in degrees of Fahrenheit (X1) the amount of insulation in inches (X2), the number of windows in the house (X3), and the age of the furnace in years (X4). Given below is the Excel output for the regression model.
 
mso-para-margin-bottom:.0001pt; line-height:200%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;}

Regression Statistics

         

R Square

0.808

         

Adjusted R Square

0.7568

         

Observations

20

         
             

ANOVA

           

 

df

SS

MS

F

Significance F

 

Regression

4

169503.4241

42375.8600

15.7874

0.0000

 

Residual

15

40262.3259

2684.1550

     

Total

19

209765.7500

 

 

 

 
             

 

Coefficients

Standard Error

t Stat

P-value

Lower 90%

Upper 90%

Intercept

421.4277

77.8614

5.4125

0.0000

284.9327

557.9227

X1 (Temperature)

-4.5098

0.8129

-5.5476

0.0000

-5.9349

-3.0847

X2 (Insulation)

-14.9029

5.0508

-2.5905

0.0099

-23.7573

-6.0485

X3 (Windows)

0.2151

4.8675

0.0442

0.9653

-8.3181

8.7484

X4 (Furnace Age)

6.3780

4.1026

1.5546

0.1408

-0.8140

13.5702



According to these results,  the coefficient for temperature is significant in predicting heating costs.

     
       

 

Option 1

Low Cost Option
Download this past answer in few clicks

4.83 USD

PURCHASE SOLUTION

Already member?


Option 2

Custom new solution created by our subject matter experts

GET A QUOTE

rated 5 stars

Purchased 7 times

Completion Status 100%

Sitejabber (5.0)

BBC (5.0)

Trustpilot (4.8)

Google (5.0)