Customer Months Since Last Service (x1) Type of Repair Electrical (0) Mechanical (1) (x2) Truck (1) or Car (0) (x3) Mileage of Vehicle (x4) Repair Time in hours (y) 1 2 1 1 98855 2.9 2 6 0 0 86883 3 3 8 1 1 75645 4.8 4 3 0 0 97823 1.8 5 2 1 1 62099 2.9 6 7 1 0 67697 4.9 7 9 0 1 73113 4.2 8 8 0 0 76240 4.8 9 4 1 1 71170 4.4 10 6 1 1 60626 4.5 An...

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Question

Basic Math

Customer

Months Since
Last Service (x1)

Type of Repair

Electrical (0)

Mechanical (1)

(x2)

Truck (1)
or
Car (0)

(x3)

Mileage of Vehicle

(x4)

Repair Time
in hours (y)

1

2

1

1

98855

2.9

2

6

0

0

86883

3

3

8

1

1

75645

4.8

4

3

0

0

97823

1.8

5

2

1

1

62099

2.9

6

7

1

0

67697

4.9

7

9

0

1

73113

4.2

8

8

0

0

76240

4.8

9

4

1

1

71170

4.4

10

6

1

1

60626

4.5

An analyst at a local automotive garage wanted to see ifthere were relationships between repair time in hours (y) andmonths since last service(x1), type of repair(x2), whether it was atruck or car(x3), or the mileage of the vehicle(x4). Use a level ofsignificance of 0.05.

  1. What is the dependent variable?

  1. What are the independent variables?

  1. Run the regression analysis with the four independentvariables. Write out the prediction equation.
  1. From a global perspective is the model worth keeping? Why?

  1. Evaluate the individual independent variables, circle thevariables would you consider removing? Explainwhy?         X1                        X2                          X3                           X4

  1. Rerun the regression analysis after removing the unnecessaryindependent variables. Write the regression equation:
  1. What repair time will it take for a car with 90000 miles, notserviced for six months, and requires for electrical repairs?

Answer & Explanation Solved by verified expert
4.5 Ratings (974 Votes)
1 The dependent variable is Repair times in hour 2 The independent variables Months Since Last Service Type of Repair Electrical 0 Mechanical 1 Truck 1 or Car 0 Mileage of Vehicle 3 Following is the output of regression analysis SUMMARY OUTPUT Regression Statistics Multiple R 0936550158 R Square 0877126198 Adjusted R Square 0778827156 Standard Error 0507390569 Observations 10 ANOVA    See Answer
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