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Data Analytics for Cardio Good Fitness: Customer Profile and Exploratory Analysis


Data Analytics usually refers to the analysis of data for business applications and consists of the following steps:

1. Ingesting
2. Cleaning
3. Visualization and Exploratory Analysis
4. Feature Extraction and Selection
5. Modeling
6. Evaluation
7. Inference

These steps are neither discrete nor independent. The statistical methods we studied in this class are especially useful in the 3rd step where it is checked whether the data collected is meaningful, appropriate, and useful for the further analysis. For example, when dealing with qualitative data features, we construct frequency distribution tables and plot graphs (bar-carts, pie-charts, etc.) for each variable or for a set of variables (Chapter 2), and when dealing with quantitative data, in addition to plotting graphs, we describe the data using numerical measures – range, quartiles, interquartile range, mean, median, variance, standard deviation, etc. (Chapters 2 and 3). We might also investigate the relationship between two quantitative variables, plot the scatter plots and perform linear regression method (Chapter 12). In addition, we can compute confidence intervals (Chapter 8) and make hypothesis and perform the hypothesis testing (Chapter 9). 

If we find that the data collected is skewed or we find unreasonable/meaningless relationships between certain data features, we might have to discard this data, go back to step 1, and collect a new data set. 

In this project you are given a small data set and asked to perform Visualization and Exploratory Analysis using the statistical methods you leant in this class. It is up to you which methods you would like to use and how you want to present your findings (tables, graphs, etc.)

Cardio Good Fitness 

The market research team at AdRight is assigned the task to identify the profile of the typical customer for each treadmill product offered by CardioGood Fitness. The market research team decides to investigate whether there are differences across the product lines with respect to customer characteristics. The team decides to collect data on individuals who purchased a treadmill at a CardioGood Fitness retail store during 
the prior three months. The data are stored in the CardioGood Fitness Excel file. The team identifies the following customer variables to study: product purchased, TM195, TM498, or TM798; gender; age, in years; education, in years; relationship status, single or partnered; annual household income ($); average number of times the customer plans to use the treadmill each week; average number of miles the customer 
expects to walk/run each week; and self-rated fitness on an 1-to-5 ordinal scale, where 1 is poor shape and 5 is excellent shape. 

1. Create a customer profile for each CardioGood Fitness tread-mill product line by developing appropriate tables and charts.

You can use Excel and, for example, develop frequency tables, bar charts, pie charts, and histograms, and compute numerical measures we studied in Chapters 2-3 for each of the given variables (gender, age, etc.). You can compute the confidence interval estimates for the population mean (studied in Chapter 8) for some of the quantitative variables.

In addition, you can pose a hypothesis regarding some of the quantitative variables and perform the hypothesis test illustrating the methods we studied in Chapter 9 and you can also perform a regression analysis concept we studied in Chapter 12. 

This data set is an illustration of a real-world situation and the purpose of the project is for you to apply the concepts we studied throughout the class to expand and enhance your understanding of those concepts by working with a particular data set with a minimal guidance from me. It is up to you to choose which of the concepts we studied are useful for this particular problem and to present your findings.

2. Write a report to be presented to the management of Cardio-Good Fitness detailing your findings. The report could be in the PowerPoint, MS Word, Excel, or the pdf format and should contain graphs/charts and findings from step 1 as well as your conclusions.

3. Log in to zoom and record a presentation, no longer than 3 minutes, going over the report from step 2. Please have your web-camera on and share the screen of your presentation going over it and explaining your work.

4. Please email me only the recording of your presentation (not your report) by using Blackboard Learn My Messages (please do not email your presentation to my work address). After you log in to Blackboard Learn, on the left-hand side you will see “My Messages”. Please click there, create a new message and attach your recording. If your recording is too large to be sent over the email, you can compress it or you can send me the link to the recording that is saved in your One Drive folder at your gator uhd email account). The deadline is December 6th at 11:59pm and late projects will not be accepted.

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