math Please complete the following activities contained within the Excel sheet. Linear Regression Task Instructions for Excel Practical: First at

math
Please complete the following activities contained within the Excel sheet.

Linear Regression Task

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math Please complete the following activities contained within the Excel sheet. Linear Regression Task Instructions for Excel Practical: First at
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Instructions for Excel Practical: First attempt the linear regression task. Please use the data that is on the following slide, titled “Assignment Data” to complete the first part of the assignment. Then follow up with the next two tabs to perform a multiple regression using Excel. The first multiple regression example is already completed for you in the Lecture.

Part 1: Complete the following Task

Create a scatterplot of the data on the next tab. Be sure to label the axes and include a regression line. Be sure to include the equation and r-squared value and place it on the top right of the graph.

Part 2: Interpret your results: please type in your answers to the following questions. Be sure to fully elaborate on them and try to include at least 2-3 sentences to explain each part.

A. What kind of correlation (positive, negative, or no correlation) exists between the MPG of a car and its acceleration time? Is it a strong correlation?

B. What are the degrees of freedom of the data set? Using the critical values table in the Lecture, is the R-squared Statistic significant enough for us to make any statistical inferences? If not, how large should the sample be to substantiate the results of this study?

C. Should we interpret the y-intercept in this case? Why or why not?

D. Using your equation, predict the MPG of a car that accelerates in 7.5 seconds.

E. In a few sentences, summarize the findings of this study? What effect does MPG consumption have on the acceleration time of a vehicle?

Linear Regression Data

Acceleration MPG Data retrieved from: https://www.consumerreports.org/fuel-economy-efficiency/vehicle-fuel-economy-vs-performance/

Honda Accord Hybrid EX 7.4 47

Toyota Camry Hybrid LE 7.8 47

Chevrolet Malibu Hybrid 8 41

Hyundai Sonata Hybrid SE 8.2 39

Ford Fusion SE Hybrid 8.3 39

Toyota Camry LE(4-cyl.) 8 32

Nissan Altima 2.5 SV 7.6 31

Honda Accord EX(1.5T) 7.7 31

Chevrolet Malibu LT(1.5T) 8.4 29

Kia Optima EX(4-cyl.) 8 28

Hyundai Sonata SEL(4-cyl.) 8.3 28

Volkswagen Passat SE(4-cyl.) 8.6 28

https://www.consumerreports.org/cars/honda/accord
https://www.consumerreports.org/cars/kia/optima
https://www.consumerreports.org/cars/hyundai/sonata
https://www.consumerreports.org/cars/volkswagen/passat
https://www.consumerreports.org/cars/toyota/camry
https://www.consumerreports.org/cars/chevrolet/malibu
https://www.consumerreports.org/cars/hyundai/sonata
https://www.consumerreports.org/cars/ford/fusion
https://www.consumerreports.org/cars/toyota/camry
https://www.consumerreports.org/cars/nissan/altima
https://www.consumerreports.org/cars/honda/accord
https://www.consumerreports.org/cars/chevrolet/malibu

Mult Regression_Height

Height Momheight Dadheight Outlier? Prediction Error

66 66 71

64 62 68

64 65 70 Five Number Summary for Height

69 66 76 Minimum:

66 63 70 1st quartile:

63 61 68 Median:

68 64 69 3rd quartile:

65 62 66 Maximum:

64 70 73

65 70 75 Outlier Parameters

66 63 70 IQR:

68 68 69 Lower Limit

66 60 77 Upper Limit

60 61 65 # of outliers

60 59 62

60 62 63

64 60 72

70 62 74

64 61 70

63 61 70

61 60 67

69 65 68

63 64 65

66 66 66

65 64 70

64 62 68

66 64 73

65 61 67

63 60 67

63 64 74

65 62 75

66 64 70

67 64 70

67.5 69 69

62 60 60

64 63 74

63 62 68

64 62 69

63 58 74

62.5 65 71.5

65 64 72

66.5 63 74

69.5 66 72

63.5 60 67

69 64 74

67 63 72

67 66 70

67 69 66

67 65 68

67 63 71

63 60 69

63 61 67

60 60 66

63 68 75

65.5 62 71

66 63 72

68 67 70

65 62 67

65 63.5 69

66 67 70

62 63 69

66 65 68

67 64 71

63 61 70

66 60 69

67 62 71

61 63 69

62 60 68

63.5 62 63

61 62 65

64 61 63

61 65 69

61 64 68

63 61 72

62 62 72

64 64 74

64 62 68

65 66 73

64 61 67

59 60 64

65 67 68

63 65 68

64.5 62 66.5

64 63 71

70 62 76

60 61 65

69 68 73

62 63 68

65 69 72

64 62 66

64 64 70

66 61 70

63 62 68

67 68 69

60 60 68

68 67 72

67 70 76

66 60 71

67 68 72

65 64 70

61 64 64

64 63 72

68 64 71

64 63 71

65.5 62 70

67 66 70

67 69 74

62.5 65.5 68

62 61 65

62 57 65

64 62 63

66 66 73

65 64 67

67 65 70

64.5 66 70

70 65 77

63 56 62

65 64 71

60 64 69

69 64 75

64 63 67

62 62 66

63 62 65

64 69 70

62 62 68

72 65 72

63 60 69

67 69 66

68 64 67

72 70 76

59.5 59 65

65 70 70

68 60 70

61 59 67

66 64 69

62.5 67 71.5

65 62 70

65 63 70

63 61 65

70 68 71

64 64 67

63 65 66

68 69 72

63.5 64 69

67.5 70 72

66 65 71

66 63 72

61 61 65

63 65 72

61 60 67

61 60 62

66 65 70

63 61 63

66 62 67

66 62 68

68 63 69

62 62 68

66 67 70

63 62 69

66 62 72

66 65 69

69 66 70

68 65 73

64 65 74

69 67 74

65 63 70

67 64 69

66 67 67

63 62 70

61 66 66

67 64 72

67 63 71

63.5 65 71

60 64 66

67 65 68

61 59 70

63 65 64

62 64 66

67 66 71

60 62 65

64.5 65.5 66

60.5 62 67

69.5 63 74

66 64 74

65 62 67

63 60 72

68 66 74

68 62 71

65 63 71

63 65 68

62 60 67

63 66 68

71 69 72

63 63 68

63 63 66

62 64 66

63 61 67

65 67 68

65 64 73

64 58 72

68 68 72

64 63 68

61 62 67

62 60 66

66 62 71

68 66 72

65 64 68

69 65 75

65 69 72

64 68 72

64 64 70

67 62 71

68 65 67

73 68 76

Attribution: n=214 female students at University of California at Davis
Please perform the multiple regression for each of these tabs and do not delete them. Please include the output of your regressions to this assignment.

Mult Regression_Bodyfat

Triceps Thigh Midarm Bodyfat z-score outlier?

19.5 43.1 29.1 11.9

24.7 49.8 28.2 22.8

30.7 51.9 37 18.7

29.8 54.3 31.1 20.1 Outlier Parameters Calculate for Bodyfat Column

19.1 42.2 30.9 12.9 IQR: mean:

25.6 53.9 23.7 21.7 Lower Limit median:

31.4 58.5 27.6 27.1 Upper Limit mode:

27.9 52.1 30.6 25.4 # of outliers standard deviation:

22.1 49.9 23.2 21.3 # of outliers

25.5 53.5 24.8 19.3

31.1 56.6 30 25.4

30.4 56.7 28.3 27.2

18.7 46.5 23 11.7

19.7 44.2 28.6 17.8

14.6 42.7 21.3 12.8

29.5 54.4 30.1 23.9

27.7 55.3 25.7 22.6

30.2 58.6 24.6 25.4

22.7 48.2 27.1 14.8

25.2 51 27.5 21.1

Attribution: Data source:Applied Regression Models, (4th edition), Kutner, Neter, and Nachtsheim
Run a multiple regression that predicts bodyfat based on midarm, thigh, and triceps measurements.

Write your equation here:

Using the model, provided, find the predicted body fat of someone who has a triceps measurement of 21.4, thigh of 45.6, and midarm measurement of 27.6. Show your work below: