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2.1. Introduction

In this chapter, we will explore concepts of descriptive statistics such as normal distribution, measures of central tendency, measures of variability, and other related ideas. We will learn the difference between descriptive and inferential statistics and explore various data types in statistics, such as categorical and numerical types.

We will use a spreadsheet application to manipulate the data in the chapter to solve problems because spreadsheets are one of the best tools for this particular work. Many different spreadsheet applications exist, but perhaps the most ubiquitous are Excel (a proprietary commercial spreadsheet application) and Google Sheets (a free web-based spreadsheet application). We will use Google Sheets because it is free, works from almost any device (including your laptop, tablet, or phone) and includes spreadsheet functions that are very similar to those in Excel. So, if you are familiar with Microsoft Excel, you will find Google Sheets very easy to use.

Lastly, we will discuss the difference between correlation and causation and explore why correlation does not imply causation. Understanding this concept is crucially important for making correct assumptions and decisions when analyzing data.

2.1.1. Learning Goals

• Explore the concepts of descriptive statistics and data visualization.

• Distinguish between descriptive and inferential statistics.

• Learn to apply the various measures of central tendency and the measures of variability.

• Become familiar with several descriptive statistics and data visualization spreadsheet operations.

• Addressing cells: relative versus absolute, on the same sheet versus across sheets.

• Use a spreadsheet to explore data.

2.1.2. Learning Objectives

• Become adept at importing, organizing, and visualizing data using Google Sheets.

• Extrapolate measures of central tendencies and measures of variability of a given data set.

• Combine different datasets and use them to extract new data.

• Compare and contrast data from various datasets using visual representations.

• Learn to create and use spreadsheet pivot tables.

2.1.3. Reading List

Be sure to read each link below entirely and watch for key references before moving on:

• Intro to Descriptive Statistics

• Data Types in Statistics

• Google Sheets 101

• Google Sheets 101 Video

• Data Science behind the Facebook/Cambridge Analytica Scandal

• Why Correlation does not Imply Causation

2.1.4. Time Required

These readings and exercises could take up to four hours (or more) to complete. But they could take less time depending on how quickly you are able to work through the material.

2.1.5. Summary Statistics

Q-1: Match the Term on the left with the description on the right. Check the Reading list for the introduction to summary statistics
• Normal Distribution
• Visualization of data is bell-shaped, symmetrical, centered, and unimodal.
• Range
• The difference between the largest and the smallest point in the data.
• Variance
• The summation of the square differences between every data point and the mean.
• Standard Deviation
• The square root of the summation of the square differences between every data point and the mean.

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