In chapter 4, you learned about regression analysis and how this approach is used to determine what kind of relationship exists between variables X and Y. You also learned how managers use this approach to make logical, sound and informed decisions in business operations.

Ms. Tessa Roberts, owner of Cold Stone Ice Cream, would like to study the effect of atmospheric temperature on sales of ice cream during the summer season. A sample of 21 consecutive days (shown below) is presented in the attached Excel file:

Day	Daily High Temperatue (Degrees F)	Sales per Store (Thousands of Dollars)
1	63	1.52
2	70	1.68
3	73	1.80
4	75	2.05
5	80	2.36
6	82	2.25
7	85	2.68
8	88	2.90
9	90	3.14
10	91	3.06
11	92	3.24
12	75	1.92
13	98	3.40
14	100	3.28
15	92	3.17
16	87	2.83
17	84	2.58
18	88	2.86
19	80	2.26
20	82	2.14
21	76	1.98

A simple linear regression of the form Yⁱ = b⁰ + b¹X, with DAILY HIGH as the explaining (independent) variable, and SALES as the explained (dependent variable).

Analyze the data provided in the attached MS Excel file to answer the questions listed below:

• Assuming a linear relationship, use the least-squares method to find the regression equation. State the equation.
• Interpret the b⁰ and b¹ coefficient values in the equation.
• What sort of relationship exists between the two variables? Does that relationship make sense to you? Why or why not?
• Interpret the R-Square value in your output.
• Predict the sales of ice cream at zero temperature. Does your prediction make sense? Why or why not?
• At a level of a = 0.01, test for the significance of the relationship between temperature and the sales of ice cream.