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Chapter Outline

In this chapter, you’ll learn how to summarize and interpret data, which is essential for making sense of the information around us. You’ll explore different ways to collect, organize, and display data using tables, charts, and graphs. You’ll learn to calculate measures of central tendency, such as mean, median, and mode, which help you understand the typical value in a data set. Additionally, you’ll discover how to measure variability using range, variance, and standard deviation, giving you insight into how spread out the data points are. By mastering these skills, you’ll be able to interpret data accurately, draw meaningful conclusions, and make informed decisions based on the data you encounter in everyday life.

6.1

Describing Data

Learning Objectives

By the end of this section, you will be able to:

  • Explore data collection and data displays

  • Differentiate between a population and a sample

  • Classify a sample as random or biased

  • Interpret a data display in terms of the shape of the data distribution

  • Identify the different ways to collect data

6.2

Measures of Center

Learning Objectives

By the end of this section, you will be able to:

  • Calculate the mean and median, and determine the effect of outliers on the measures of center

  • Represent data on a dot plot and explore the shape of the distribution

  • Represent data on a histogram and explore the shape of the distribution

6.3

Box Plots (Five-Number Summaries)

Learning Objectives

By the end of this section, you will be able to:

  • Display and analyze data using box plots

  • Identify outliers and measures of variability

  • Create and interpret box plots

  • Compare box plots

6.4

Standard Deviation

Learning Objectives

By the end of this section, you will be able to:

  • Analyze quantitative data

  • Analyze histograms for symmetry and skewness

  • Calculate the variance and standard deviation of a data set

  • Interpret a normal distribution curve

6.5

Analyzing Residuals

Learning Objectives

By the end of this section, you will be able to:

  • Determine residuals for a given set of data points and a line of best fit

  • Analyze the residual plot to determine whether the model is a good fit for the data

  • Create a residual plot

6.6

Strength of Correlation

Learning Objectives

By the end of this section, you will be able to:

  • Distinguish between correlation and causation

  • Calculate the correlation coefficient for a linear model using technology

  • Analyze the strength of the linear model from the correlation coefficient

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