Tips for Pam and Sue Essay

Pamela Suzette Grier was born on May 26TH, 1949 in Winston-Salem, North Carolina to Sylvia Samuels, a nurse, and Clarence Ransom Grier, a mechanic and Technical Sergeant in the US Air Force. She moved frequently throughout her childhood due to her father's military career, but the family eventually settled in Denver, where she attended both secondary school and college. In order to raise money for tuition for her sophomore year, Grier entered several beauty pageants around the state and even earned second runner up in the 1967 Miss Colorado Pageant – it was there that she was discovered by an agent who urged her to take up acting and she quickly moved to Los Angeles with her aunt and cousin, Rosey Grier, a pro-football player and actor, in 1968 to pursue a career.

Base on your bubble chart, can you categorize different publishers into four different types 1)

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,

2. What were the specifi c eff ects of the Lasix, hot water, and alcohol on the couple’s blood pressure?

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

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.

Using MINITAB run the multiple regression analysis using the variables CALLS, TIME, and YEARS to predict SALES. State the equation for this multiple regression model.

4. Discuss the benefits and drawbacks of a binary tree versus a bushier tree. The structure of binary is simple than a bushier tree. Each parent node only has two child. It save the storage space. Besides, binary tree may deeper than bushier tree. The result record of binary may not very refine. 5. Construct a classification and regression tree to classify salary based on the other variables. Do as much as you can by hand, before turning to the software. Data： NO. 1 2 3 4 5 6 7 8 9 10 11 Staff Sales Management Occupation Service Gender Female Male Male Male Female Male Female Female Male Female Male Age 45 25 33 25 35 26 45 40 30 50 25 Salary $48,000 $25,000 $35,000 $45,000 $65,000 $45,000 $70,000 $50,000 $40,000 $40,000

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

Introduction. The client, Running for Glory (“RfG”), is currently seeking to expand to the suburbs of Seattle, Washington, to expand their customer base and attract a younger clientele. However, the business does not have the data necessary to make a sound business decision and needs a report that will analyze data including, but not limited to, demographics, economics, and shopping trends so that the perfect retail location can be selected for their expansion.

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.

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.

3) a) The most highly positively correlated variables are ‘Quantity of the Product’ and ‘Respectability of Product’.