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Income

($1000s) Household

Size Amount

Charged ($)

89.31 2.00 10985.47

61.08 5.00 9792.97

43.95 4.00 6527.55

55.15 6.00 9708.57

40.39 2.00 6335.20

34.06 3.00 4809.57

86.43 4.00 12314.59

79.49 3.00 10823.38

48.40 2.00 7172.03

46.49 3.00 6996.39

50.68 3.00 7349.23

73.77 5.00 11719.66

34.18 4.00 6062.71

81.64 3.00 9861.66

65.08 4.00 10411.15

41.96 3.00 8169.33

41.48 2.00 6705.15

45.21 3.00 5871.21

48.98 5.00 8627.18

35.70 4.00 6466.99

77.36 2.00 9646.36

79.00 3.00 11910.64

52.03 3.00 7624.06

42.91 3.00 7635.68

38.69 5.00 7955.40

59.49 2.00 6076.57

82.77 2.00 11637.13

9.93 3.00 3911.33

59.54 3.00 6756.73

44.92 3.00 7031.92

33.79 3.00 7777.11

27.66 2.00 5470.17

53.17 3.00 8559.86

35.15 4.00 6306.89

89.46 2.00 10466.76

26.45 5.00 4101.41

89.59 6.00 14962.20

73.96 4.00 12153.59

73.15 4.00 11324.99

56.15 3.00 9704.71

46.57 4.00 9592.53

38.29 5.00 7372.79

34.84 4.00 6708.17

74.27 2.00 8743.04

50.16 4.00 9211.51

85.99 3.00 12318.45

50.82 4.00 9109.05

79.99 3.00 12882.03

68.38 5.00 11310.12

64.42 5.00 11356.01

57.78 3.00 8030.32

50.85 3.00 8905.52

41.35 2.00 5863.35

68.88 4.00 10199.39

87.37 4.00 13589.18

42.15 7.00 8958.60

85.91 3.00 9884.07

79.22 4.00 11881.21

72.59 5.00 11091.60

70.79 3.00 12217.53

65.91 4.00 11661.95

50.88 4.00 6898.00

29.77 4.00 5342.99

82.30 2.00 9685.05

44.81 2.00 6882.08

3.99 1.00 1612.58

57.16 6.00 10069.27

23.25 5.00 8063.88

15.31 3.00 6064.65

72.60 3.00 12132.90

72.53 5.00 11562.23

80.31 1.00 9250.01

43.47 6.00 8147.12

65.82 2.00 10219.37

78.58 4.00 11057.59

37.36 5.00 8690.96

50.86 3.00 7186.18

77.72 3.00 12597.46

73.55 2.00 9859.00

73.87 4.00 10205.59

1) Provide graphical summaries of the data. Comment on yourfindings.

2) Develop an estimated regression equation, using annualincome as the independent variable. Insert Regression equationestimation results here (excluding the ANOVA):

a. Interpret the estimated slope coefficient.

b. Interpret the R-square.

c. Interpret the p-value on the slope.

d. Interpret the 95% confidence interval.

3) Develop an estimated regression equation, using householdsize as the independent variable. Insert Regression equationestimation results here (excluding the ANOVA):

a. Interpret the estimated slope coefficient.

b. Interpret the R-square.

c. Interpret the p-value on the slope.

d. Interpret the 95% confidence interval.

4) Which of the two models is the better predictor of annualcredit card charges? Defend your decision.

5) Provide a scatterplot of the standardized residuals fromyour chosen best model and comment whether the assumption appear tobe met.