Case Study : Locating New Pam And Susan 's Stores

For example he would discuss the stats of percentage of black kids in a school or Latino kids. He would also provides stats about how many of the students of schools schools in poverty would graduate or go to college.

Do Number of Stores Influence Conclusion- This has no impact on conclusion because the data range for stores was from 27 to 87 giving it a mean of 59.8 and SD of 27.5. This demonstrated that the numbers of stores that participated in the programs was sufficient enough to analyze and interpret. Program V just happens to have the highest number of stores participating, but that would be irrelevant if it wasn’t the most profitable.

The case involves the decision to locate a new store at one of two candidate sites. The decision will be based on estimates of sales potential, and for this purpose, you will need to develop a multiple regression model to predict sales. Specific case questions are given in the textbook, and the necessary data is in the file named pamsue.xls.

Pam and Susan’s department stores are in the process of opening a new business unit. There are two locations that are being considered for the new store and decision is based upon estimates of sales for both of them. My job is to use data gathered from each store as well census data in store’s trading zones to predict sales at both of the sites that are being consider for their newest store.

AJ DAVIS is a department store chain, which has many credit customers and wants to find out more information about these customers. A sample of 50 credit customers is selected with data collected on the following five variables:

Obtain data on at least three demographics such as: crime, education levels, gender, ethnicity, race, sexuality, education, or socio-economic data where available.

This data has been compiled by the sales department in order to attract future customers, therefore specific and primary data.

A firm has decided through regression analysis that its sales (S) are a function of

The purpose of this case is to determine which key variables drive Crusty Pizza Restaurant’s monthly profit and then forecast what the monthly profit would be for potential stores. Based off of this information we will be able to make a recommendation to Crusty Dough Pizza Restaurant on which stores they should open and which they avoid. The group was provided 60 restaurants’ data that included monthly profit, student population, advertising expenditures, parking spots, population within 20 miles, pizza varieties, and competitors within 15 miles. For the potential stores we were given all of this

The key segments of these brasserie shoppers are the segments fashion, convenience, shop time, fitness, perception, brand4els, popular, store display, sales staff, fabric, cut, seam, shape of bra on hanger, shape of bra on body, colors, and match. The profile of each segment according to each segmentation variable in the analysis are fashion(I’m very

This will help to ensure how successful they will be and give them a market plan when it comes to what type of products and services they will offer at each new location. Researching customers in all the projected new locations will give them an idea of what they will have in each store according to the areas shoppers like and dislike and how willing they are to try something new.

The average household income was $59,534.00. The college education attainment was 30.9% and the unemployment rate was 4.6%. The age category age of 18 under was 17% and above was 75%. The population growth was 22%. The percentage of White/Caucasian was 50.5%, Black/African-American was 23.7% percent and Hispanic/Latino was 43.8% percent.

When TRO has enough accounts to be profitable in those geographical areas, Steve wants to move to Denver, and end in California. Using data from the case, it shows that California, Michigan, and Ohio are ranked top 10 in sales for optical goods stores. The advantage TRO has on his competitors are it is recognized leader in the industry, and has been selected Transition Lab of the Year and honored by the Optical Lab Association as one of the top 25 labs in the country. Based on geographical distribution of optical good stores, Steve’s plans for growth do make sense. He is aiming growth in profitable states. On the other hand, Steve’s plans should include states that are actually closer to him, and that are, as well, ranked in the top 10 in sales such as New-York, and Illinois.

Using the sample data given in Table 2-20, make a recommendation for how many units of each style Wally should make during the initial phase of production. Assume that all of the 10 styles in the sample problem are made in Hong Kong and that Wally’s initial production commitment must be at least 10,000 units. Ignore price differences among styles in your initial analysis.

As a result, this paper makes an assumption that only data from wholesalers 1 and 2 will fit the model best, in terms of finding the correct diamond for the professor.