Using Multiple Regression Project For New Stores

For the Descriptive Statistics report the mean and standard deviation for the price of the houses for each of the 3 locations.

analyze sales data to find ways to improve store performance. Over time, however, Wal-Mart had

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.

We have data out of 250 stores. The data include demographics, economics, sales of the stores, compositions of those sales as well as sales behavior per households. There are 31 variables being consider for each store and those variables range from sales,

Pam and Susan’s is a chain of discount department stores. There are currently 250 stores, mostly located throughout the South. As the company has grown and wants to expand, Pam and Susan’s is in the search of the most profitable location for the new stores. Store locations decisions are based upon estimates of sales potential. The company is currently considering two sites A and B for the next store opening. Using the information gathered on demographics and economic trading zones, size, composition and sales of the 250 existing stores we will built a regression model to provide the best estimate of sales from the two sites and recommend the most profitable one for the next store opening.

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

Recently, due to decrease, in sales Company Q had to close two stores in high crime rate areas. Those closures where due to the result of months of losses in profits from those two stores. If those stores, in higher-crime areas were

4) Location- Decisions about where to locate a store are critical to any retailer’s success. Location decisions are particularly important because of their high cost, long-term commitment and impact on customer patronage. Choosing a particular location type involves evaluating a series of trade-offs. These trade-offs generally include the occupancy cost of location, the pedestrian and vehicle customer traffic associated with the location, the restrictions placed on the store operations by the property managers, and the convince of the location for customers.

The new owner of a beauty shop is trying to decide whether to hire one, two, or three beauticians. She estimates that profits next year (in thousands of dollars) will vary with demand for her services and has estimated demand in three categories low, medium and high.

2. (TCO 3) You currently work for an automotive parts supply store.  Your company is growing and is considering expansion.  The company currently has three locations (North, South, and Central) in one state.  Each parts supply store carries inventory in four categories.  You have been presented with the sales figures for the last three years for each location and inventory category by store.  Based on this information, you're tasked with analyzing current sales for each store by category and overall total sales by store and category.

Income level data is a good indicator of residents’ spending power. Income positively correlates with retail expenditures in many product categories. During market evaluation, retailers look at the median or average household income, and seek a minimum number of households within a certain income range prior to establishing a business or setting prices. Another common practice is to analyze the distribution of household incomes. Different businesses may avoid extremely high or low-income areas. Some specialty fashion stores target incomes above $100,000.

MATH 533(Applied Managerial Statistics) Project AJ Davis Department Stores; Part C: Regression and Correlation Analysis

In this particular case, Randy will need to assign the correct numbers to the correct category. For the purposes of this case study, assume T will equal 1 to make the equation represent one year of employment in one of the ice cream shops. For following variables, Nn will equal 50 as there will be 50 applicants total selected to be hired, rxy will represent .30 in one equation representing the interview and job performance and in the other equation, it will represent .50 which will represent the work sample predictor and job performance, SDy will be chosen to represent .20, Ẑs will be .80 because it will be the predictor score of the selected applicants, Na will represent 100, as that is the total number of applicants that submitted applications, and Cy will represent the cost per applicant in the interview and job performance in one equation as 100 and it will represent 150 in the other equation for work sample and job

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.