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

1. Ingesting
2. Cleaning
3. Vis ualization and Exploratory Analys is
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.)

CardioGood Fitne s s

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 individua ls 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.
Hints:

You can use Excel and, for example, develop frequency tables, bar charts, pie charts, and
histograms, and compute numerDATA

Product Age Gender Education Marital Status Usage Fitness Income Miles

TM195 18 Male 14 Single 3 4 29562 112

TM195 19 Male 15 Single 2 3 31836 75

TM195 19 Female 14 Partnered 4 3 30699 66

TM195 19 Male 12 Single 3 3 32973 85

TM195 20 Male 13 Partnered 4 2 35247 47

TM195 20 Female 14 Partnered 3 3 32973 66

TM195 21 Female 14 Partnered 3 3 35247 75

TM195 21 Male 13 Single 3 3 32973 85

TM195 21 Male 15 Single 5 4 35247 141

TM195 21 Female 15 Partnered 2 3 37521 85

TM195 22 Male 14 Single 3 3 36384 85

TM195 22 Female 14 Partnered 3 2 35247 66

TM195 22 Female 16 Single 4 3 36384 75

TM195 22 Female 14 Single 3 3 35247 75

TM195 23 Male 16 Partnered 3 1 38658 47

TM195 23 Male 16 Partnered 3 3 40932 75

TM195 23 Female 14 Single 2 3 34110 103

TM195 23 Male 16 Partnered 4 3 39795 94

TM195 23 Female 16 Single 4 3 38658 113

TM195 23 Female 15 Partnered 2 2 34110 38

TM195 23 Male 14 Single 4 3 38658 113

TM195 23 Male 16 Single 4 3 40932 94

TM195 24 Female 16 Single 4 3 42069 94

TM195 24 Female 16 Partnered 5 5 44343 188

TM195 24 Male 14 Single 2 3 45480 113

TM195 24 Male 13 Partnered 3 2 42069 47

TM195 24 Female 16 Single 4 3 46617 75

TM195 25 Female 14 Partnered 3 3 48891 75

TM195 25 Male 14 Partnered 2 3 45480 56

TM195 25 Female 14 Partnered 2 2 53439 47

TM195 25 Female 14 Partnered 3 3 39795 85

TM195 25 Male 16 Single 3 4 40932 113

TM195 25 Female 16 Partnered 2 2 40932 47

TM195 25 Male 16 Single 3 3 43206 85

TM195 26 Female 14 Partnered 3 4 44343 113

TM195 26 Female 16 Partnered 4 3 52302 113

TM195 26 Male 16 Partnered 2 2 53439 47

TM195 26 Male 16 Partnered 3 3 51165 85

TM195 26 Female 16 Single 3 3 36384 66

TM195 26 Male 16 Partnered 4 4 44343 132

TM195 26 Male 16 Single 3 3 50028 85

TM195 27 Female 14 Partnered 3 2 45480 66

TM195 27 Male 16 Single 4 3 54576 85

TM195 27 Female 14 Partnered 2 3 45480 56

TM195 28 Female 14 Partnered 2 3 46617 56

TM195 28 Female 16 Partnered 2 3 52302 66

TM195 28 Male 14 Single 3 3 52302 103

TM195 28 Female 14 Partnered 3 3 54576 94

TM195 28 Male 14 Single 4 3 54576 113

TM195 28 Female 16 Partnered 3 3 51165 56

TM195 29 Male 18 Partnered 3 3 68220 85

TM195 29 Female 14 Partnered 2 2 46617 38

TM195 29 Female 16 Partnered 4 3 50028 94

TM195 30 Male 14 Partnered 4 4 46617 141

TM195 30 Male 14 Single 3 3 54576 85

TM195 31 Male 14 Partnered 2 2 54576 47

TM195 31 Female 14 Single 2 2 45480 47

TM195 32 Female 14 Single 3 4 46617 113

TM195 32 Male 14 Partnered 4 3 52302 85

TM195 33 Female 16 Single 2 2 55713 38

TM195 33 Female 16 Partnered 3 3 46617 85

TM195 34 Male 16 Single 4 5 51165 169

TM195 34 Female 16 Single 2 2 52302 66

TM195 35 Male 16 Partnered 4 3 48891 85

TM195 35 Female 16 Partnered 3 3 60261 94

TM195 35 Female 18 Single 3 3 67083 85

TM195 36 Male 12 Single 4 3 44343 94

TM195 37 Female 16 Partnered 3 3 37521 85

TM195 38 Male 16 Partnered 3 3 46617 75

TM195 38 Female 14 Part

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