REGRESSION ANALYSIS

The owner of large chain of ice-cream stores would like to study the effect of atmospheric temperature on sales during the summer season.Temperature is the independent variable.A random sample of 21 days is selected with the results given as follows:

DAILY HIGH

SALES PER

TEMPERATURE (X)

STORE (Y)

DAY

( F)

1

48

25000

2

60

28000

3

63

28500

4

75

30500

5

80

33600

6

82

32500

7

85

36800

8

88

39000

9

90

41000

10

91

43600

11

92

44000

12

65

29200

13

98

46500

14

100

48000

15

92

38700

16

87

38300

17

84

35800

18

88

38600

19

80

32600

20

82

31400

21

76

39800

MINITAB PRINTOUT

Regression Analysis: SALES PER STOREversus DAILY HIGH TEMPE

The regression equation is

SALES PER STORE (Y) = 812 + 436 DAILY HIGH TEMPERATURE (X)

PredictorCoefSE CoefTP

Constant81241290.200.846

DAILY HIGH TEMPERATURE (X)436.3250.228.690.000

S = 2930.58R-Sq = 79.9%R-Sq(adj) = 78.8%

Analysis of Variance

SourceDFSSMSFP

Regression164837403364837403375.500.000

Residual Error191631773958588284

Total20811551429

Pearson correlation of SALES PER STORE (Y) and DAILY HIGH TEMPERATURE (X) = 0.894

1. What is the linear regression equation on the Minitab printout?

2. What is the dependent variable?

3. What is the independent variable?

4. What is the coefficient of correlation?Is the relationship strong, moderate, or weak?

5. What percentage of the variation in sales can be explained by the variation in temperature?

6. What is the slope and what does it mean in terms of this problem?

7. What is the y intercept and what does it mean in terms of this problem?

8. Conduct a hypothesis test to determine if there is positive correlation at the .05 significance level.Show all 6 steps.