## Introduction

What if you wanted to know the likelihood of a certain event like the likelihood that you would win a bike raffle? How could you determine your winning chances, or odds? After completing this chapter, you’ll be able to use probability to answer such questions. You’ll also be introduced to statistical ways of measuring a set of data. For example, how could you describe the data set {13, 5, 8 9, 20}? In addition, you’ll use graphical outputs like stem-and-leaf plots, histograms, and box-and-whisker plots to visually display and analyze such data.

## Chapter Outline

- 13.1. Measurement of Probability
- 13.2. Empirical Probability
- 13.3. Permutations
- 13.4. Probability and Permutations
- 13.5. Combinations
- 13.6. Probability and Combinations
- 13.7. Mutually Exclusive Events
- 13.8. Independence versus Dependence
- 13.9. Measures of Central Tendency and Dispersion
- 13.10. Measures of Spread/Dispersion
- 13.11. Stem-and-Leaf Plots and Histograms
- 13.12. Box-and-Whisker Plots
- 13.13. Sampling methods
- 13.14. Planning and Conducting Surveys

### Chapter Summary

## Summary

This chapter begins by differentiating between theoretical and experimental probabilities. It then moves into ways of arranging a subset of a set of items called permutations and combinations. The probabilities of such arrangements are also covered. Next, it distinguishes between mutually exclusive and independent events. From there, the chapter discusses ways of measuring a data set, called measures of central tendency and dispersion. Such measures include the mean, median, mode, range, variance, and standard deviation. Finally, the chapter concludes with methods of graphically representing a data set and statistical means of collecting and analyzing data.

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Oct 01, 2012## Last Modified:

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