Critical Thinking Assignment Data Manipulation of Clothing Store Data In this assignment, you will import the Portfolio_Clothing_store.csv into SAS. (You can find this information by clicking “Next” a

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Critical Thinking Assignment

Data Manipulation of Clothing Store Data

In this assignment, you will import the Portfolio_Clothing_store.csv into SAS. (You can find this information by clicking “Next” at the bottom of the next two pages.) Next, you will examine the variables in the dataset using the variables list document accessed through this link. Now, create a business question this data can answer. Finally, extract the variables necessary to answer the business question and load them into a SAS dataset for analysis.  Your analysis should include the necessary descriptive analytics tests to communicate your understanding of what story the data is telling us.

Your deliverables for this assignment are:

  1. Business questions
  2. Screenshot of the imported dataset. Screenshots must include the date and time of the screen capture.
  3. Screenshot of the loaded dataset. Screenshots must include the date and time of the screen capture.
  4. Descriptive statistics to include charts, graphs, tables, business insights, and the story the data is telling us.

Limit the charts, graphs, and tables in your analysis report to only those figures needed to support your findings and analysis. Do not include statistics outputs that are not relevant to your analysis. Your analysis report should be comprehensive yet concise, specific, and most importantly, insightful.

Your paper should be 4-6 pages in length, not counting the title page and the references page, and must refer to two scholarly sources, as well as the course materials. Figure notation for screenshots should comply with APA guidelines.

Critical Thinking Assignment Data Manipulation of Clothing Store Data In this assignment, you will import the Portfolio_Clothing_store.csv into SAS. (You can find this information by clicking “Next” a
List of variables for Portfolio_Clothing_Store.csv data set Customer id: a unique customer identification number ZIP_CODE: Customer’s zip code FRE: Total number of purchase visits MON: Total net sales CC_CARD: 0 indicates does not use a credit card, 1 indicates the customer uses a credit card AVRG: Average amount spent per visit The following variables contain the percent of total sales spent by the customer on the respective product category: PSWEATERS: Sweaters PKNIT_TOPS: Knit tops PKNIT_DRES: Knit dresses PBLOUSES: Blouses PJACKETS: Jackets PCAR_PNTS: Career pants PCAS_PNTS: Casual pants PSHIRTS: Shirts PDRESSES: Dresses PSUITS: Suits POUTERWEAR: Outerwear PJEWELRY: Jewelry PFASHION: Fashionable wear PLEGWEAR: Leg wear PCOLLSPEND: Collectibles GMP: Gross margin percentage PROMOS: Number of marketing promotions on file DAYS: Number of days the customer has been on file MARKDOWN: Markdown percentage on customer purchases CLUSTYPE: MICROVISION LIFESTYLE CLUSTER TYPE PERCRET: Percent of Returns In days between purchase: Number of days between purchases In lifetime average time between visits in days: Lifetime average time between visits in days. Six most common lifestyle cluster types in the dataset: Cluster 10 Home Sweet Home: families, medium-high income and education, manager/professionals, technical/sales Cluster 1 Upper Crust: metropolitan families, very high income and education, homeowners, managers/professionals Cluster 4 Mid-life Success: families, very high education, high income, managers/professionals, technical/ sales Cluster 16 Country Home Families: large families, rural areas, medium education, medium income, precision/crafts Cluster 8 Movers and Shakers: singles, couples, students and recent graduates, high education and income, managers/professionals, technical/sales Cluster 15 Great Beginnings: young, singles and couples, medium-high education, medium income, some renters, managers/professionals, technical/sales

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