Descriptive statistics allows us to understand data. It usually used as a starting point of the analysis.

In this assignment you will be asked to compute and explain descriptive statistics for a real-world dataset.

Optional practice (Textbook):

Chapter 1

• 11 (p. 49), 24-28 (p. 51), 76 (p. 55), 85, 86 (p. 59).

Chapter 2

• 1 (p. 125), 6 (p. 126), 12-17 (p. 127), 22 (p. 131), 34-39 (p. 132)
• 42 (p. 133), 43-45 (p. 134), 118 (g, h) (p. 158).

Note that there are answers provided in the text book.

Directions:

1. Pick any dataset relevant to your major that you would like to analyze. Avoid use the same or similar datasets to one you use for your final project. Make sure that the dataset has continuous and categorical variables.
2. Use R, Python or other tool to calculate descriptive statistics part of the dataset at large.
3. Use one of the categorical variables to divide dataset into meaningful parts. Calculate descriptive statistics for each of the groups.
4. Submit a Word report describing each step and a result of this process, include relevant scripts and outputs produced by the tool you use. Your report has to include:
   1. Mean, Standard deviation, median, max and min values.
   2. Box plots showing comparison of the groups.
   3. Histogram for at least one variable and the description of the variable distribution
   4. Textual description for a - b, explaining what they tell about the data.
   5. Discussion on the possible predictive analytics questions applicable to the selected dataset.