Explain why it is even possible to conduct such an analysis


Scatterplots, Correlation, and Linear Regression

In this project you will analyze the Smith household's energy usage in relation to the average temperature. Using the average temperature and the average daily kWh usage (per bill/month) data set provided inBGE Data for Correlation and Regression on Blackboard, complete the followingregression analysis.(The values for the data can be cut and pasted or manually entered into the used software, Minitab. Be careful not to copy the headings along with the measurements.)

1. Explain why it is even possible to conduct such an analysis of the association between these data. Further, establish a rationale for why this analysis would be useful.

2. Create and include a clearly-labeled scatterplot using the logical predictor and response variables (x and y), and summarize the relationship seen in the plot. (Note you can print the scatterplot generated by Minitab).

3. Find the value of the correlation coefficient(r) for these data.

4. Find the critical value of the correlation coefficient (CV r) and determine if there is a significant linear relationship between the data variables.

5. Find and provide the equation that describes the linear relationship between the variables.

6. Interpret both the slope (marginal change) and the y-intercept of the linear equation. Does the y-intercept make sense in the context?

7. Use the equation to calculate the predicted responses(y) for the average monthly temperatures of 38 and 68 degrees, respectively.

Turn in:
- Cover sheet
- Typed responses in the order of the questions given.
- All graphs or plots used to answer the questions.

Smith Household's
Energy Data

Avg. Montly Temp (x)

Avg. Daily kWh Usage (y)

33

95.1

36

74.8

40

75.6

31

96.4

33

113.3

32

97.4

38

82.4

42

79.6

40

84.3

35

97.5

33

91.0

35

81.1

45

71.3

35

104.2

30

124.0

36

98.1

36

96.2

28

108.3

37

88.7

35

102.2

37

80.6

42

66.0

48

58.5

46

84.9

46

92.0

40

76.5

43

80.7

41

89.4

42

75.3

37

97.1

52

51.4

53

41.9

57

37.6

52

72.0

56

56.1

50

59.8

54

60.4

51

51.9

56

51.6

53

62.1

63

37.0

63

35.9

66

33.4

65

48.9

63

49.9

63

41.1

58

58.5

63

46.6

59

47.9

58

55.7

69

46.8

63

31.7

63

37.0

63

32.9

64

42.4

61

52.5

57

52.4

59

48.9

68

48.2

57

56.5

61

49.0

49

54.3

50

52.0

50

49.6

49

60.6

51

69.6

52

61.7

48

73.6

49

64.2

50

62.4

50

70.6

41

68.4

41

73.9

45

59.3

48

67.0

37

102.9

39

97.6

37

93.2

40

89.3

44

81.4

35

100.3

Attachment:- Data for Assignment.rar

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Applied Statistics: Explain why it is even possible to conduct such an analysis
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