Question

In: Statistics and Probability

Sure Value Convenience Store Case Study: You work in the corporate office for a nationwide convenient...

Sure Value Convenience Store Case Study:

You work in the corporate office for a nationwide convenient store franchise that operates nearly 10,000 stores. The per-store daily customer count (i.e., the mean number of customers in a store in one day) has been steady, at 900, for some time. To increase the customer count, the chain is considering cutting prices for coffee beverages. The question to be determined is how much to cut prices to increase the daily customer count without reducing the gross margin on coffee sales too much. You decide to carry out an experiment in a sample of 24 stores where customer counts have been running almost exactly at the national average of 900. In 6 of the stores, the price of small coffee will now be $0.59, in 6 stores the price of a small coffee will now be $0.69, in 6 stores, the price of a small coffee will now be $0.79, and in 6 stores, the price of a small coffee will now be $0.89. After four weeks of selling the coffee at the new price, the daily customer counts in the stores were recorded and stored as follows...

$0.59 Sales $0.69 Sales $0.79 Sales $0.89 Sales
964 953 942 920
972 950 937 918
962 959 945 925
968 955 948 919
975 960 945 915
960 954 941 906

1) Analyze the data and determine whether there is evidence of a difference in the daily customer count, based on the price of a small coffee.

2) If appropriate, determine which mean prices differ in daily customer counts.

3) What price do you recommend for a small coffee?

Response should be computer-generated, such as in Word or Excel, not a photo of handwritten work. Preferably excel if at all possible. It should follow a basic memo format with introduction, analysis, and conclusions.

Solutions

Expert Solution

1.

Null hypothesis, H0: There is no difference in the average daily customer count, based on the price of a small coffee.

vs.

Alternative hypothesis, Ha: H0 is not true.

$0.59 Sales $0.69 Sales $0.79 Sales $0.89 Sales Anova: Single Factor
964 953 942 920
972 950 937 918 SUMMARY
962 959 945 925 Groups Count Sum Average Variance
968 955 948 919 $0.59 Sales 6 5801 966.8333 34.56667
975 960 945 915 $0.69 Sales 6 5731 955.1667 14.16667
960 954 941 906 $0.79 Sales 6 5658 943 14.8
$0.89 Sales 6 5503 917.1667 40.56667
ANOVA
Source of Variation SS df MS F P-value F crit
Between Groups 8145.458 3 2715.153 104.3286 2.2E-12 3.098391
Within Groups 520.5 20 26.025
Total 8665.958 23

Since p-value of F test=2.2x10-12<0.05 so there is sufficient evidence to conclude that there is a significant difference in the daily customer count, based on the price of a small coffee.

2. Since null hypothesis is rejected so we use Fisher's LSD method.

A=mean counts of Group named $0.59 Sales=966.8333

B=mean counts of Group named $0.69 Sales=955.1667

C=mean counts of Group named $0.79 Sales=943

D=mean counts of Group named $0.89 Sales=917.1667

Hence all mean prices are differ from each other in daily customer counts.

3. We recommend $0.59 for a small coffee since under this price average customer counts are highest.


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