# Chapter 10: Analyzing Data

**Basic**Created by: CK-12

## Introduction

Here you will explore data and how to analyze data. You will begin with the measures of central tendency and disperson. You will learn how to find the mean, median, mode and range of a data set. Then you will explore the mean on a deeper level by learning how to calculate standard deviations from the mean. Next, you will learn how to display data in many different forms including frequency tables, stem-and-leaf plots, histograms, scatterplots and box-and-whisker plots. Then, you will continue to expand your knowledge of data by learning about statistics, misleading statistics, surveys, samples and bias. Finally, you will look at interpreting data.

## Chapter Outline

- 10.1. Mean, Median, Mode and Range
- 10.2. Understanding the Mean
- 10.3. Stem-and-Leaf Plots
- 10.4. Frequency Tables and Histograms
- 10.5. Box-and-Whisker Plots
- 10.6. Using Box-and-Whisker Plots to Understand Data
- 10.7. Make a Scatterplot to Represent Data
- 10.8. Use a Scatterplot to Interpret Data
- 10.9. Understanding Misleading Statistics
- 10.10. Using Data Displays
- 10.11. Understanding Bias
- 10.12. Understanding Survey Data
- 10.13. Interpreting Data

### Chapter Summary

## Summary

First, you learned all about the measures of central tendency. You learned how to find the mean, median, mode and range of a data set. You also learned how to understand the mean through the deviation from the mean and the mean absolute deviation.

Then you learned about different data displays. You learned how to organize data in new visual ways and how to apply the measures of central tendency to each different display. You also learned which data displays are best for different situations.

Finally, you learned how to work with data through surveys and samples. These surveys and samples were analyzed for bias. You saw how to recognize misleading statistics and how to adjust data to represent accurate findings. Finally, you learned to interpret data by using percents and the margin of error.