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Assignment Overview

As a team, complete the following tasks:

• Review the assigned questions.
• Determine how your team will approach the assignment. Remember that both team members should make equal contributions towards completing the assignment; however, “equal” is defined by each team.
• Develop a response for all of the questions. Note: For problem-based questions, please use Excel as the preferred electronic format for files.
• Submit assignment by the due date. Late submissions are penalized.

This assignment will be graded in three parts. The rubric provided will be used to grade the case analysis portion of the assignment.  There is no rubric for the discussion or the problems, although all discussion posts need to be substantive and the problems should be solved using Excel.  If you have questions or need clarification, please contact me.

Questions

Respond to each of following questions or critical thinking prompts:

1. Choose a forecasting technique that would be appropriate for each of the following scenarios: (4 points)
   • demand for Valentine’s Day greeting cards,
   • demand for ice cream during a year,
   • demand for a new solar-powered car, and
   • demand for services in a beauty salon during a week.
2. Forecasting is essential to improving a hotel’s future performance. If you are the manager of a hotel: (3 points)
   • What types of forecasts will be needed?
   • What demand forecasting methods would you use to estimate room occupancy?
   • What are the main challenges in developing an accurate forecast for estimating room occupancy?
3. Management of Mayo Department Store believes that it can get estimates of sales of its Cool-Breeze air conditioners if it evaluates the relationship between sales and the average weekly temperature. Accordingly the management team of Mayo Department Store has collected weekly sales data for these air-conditioners for 10 weekly average day time temperatures and is given in the table below. (18 points)

Week	1	2	3	4	5	6	7	8	9	10
Air-conditioner Sales in units	100	85	103	110	70	115	150	120	97	85
Average temperature	78	70	85	90	75	94	101	98	86	80

Given the above information,

1. Develop a linear regression equation for air-conditioner sales as a function of  average weekly temperature
2. Forecast air-conditioner sales if the average weekly temperature is 88⁰ degrees Fahrenheit.
3. Evaluate the “goodness of fit” of the regression equation developed in (a) by computing the values of R²_, r, and S_yxand interpret the results.      
4. A marketing analyst for a major shoe manufacturer is considering the introduction of a brand new pair of running shoes. Before introducing the shoes to the market the analyst wants to determine the impact of price and in-store promotions on the sales of the new shoes. A sample of 24 shoe retail stores were selected for a test marketing study and the data obtained from the study is given in the table below. (20 points)

Store	Price ($)	In-store Promotion ($)	Shoe sales	Store	Price ($)	In-store Promotion ($)	Shoe sales
1	55	2500	410	13	75	5000	270
2	55	2500	380	14	75	5000	310
3	55	2500	320	15	75	5000	380
4	55	5000	350	16	75	7500	300
5	55	5000	450	17	75	7500	330
6	55	5000	470	18	75	7500	420
7	55	7500	490	19	95	2500	180
8	55	7500	510	20	95	2500	200
9	55	7500	540	21	95	5000	190
10	75	2500	250	22	95	5000	220
11	75	2500	300	23	95	7500	240
12	75	2500	290	24	95	7500	270

1. Based on this data use Excel to develop a multiple regression equation
2. Interpret the coefficients b₀, b₁, and b_2.
3. Interpret the statistics “multiple R” and the “coefficient of determination (R²)” in the regression output
4. Forecast shoe sales if the price is $65 and the amount spent on advertising is $4400.

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