5: Summarizing Data With Numbers
- Page ID
- 35643
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Descriptive statistics often involves using a few numbers to summarize a distribution. One important aspect of a distribution is where its center is located. Measures of central tendency are discussed first. A second aspect of a distribution is how spread out it is. In other words, how much the numbers in the distribution vary from one another. The second section describes measures of variability. Distributions can differ in shape. Some distributions are symmetric whereas others have long tails in just one direction. The third section describes measures of the shape of distributions. The final two sections concern (1) how transformations affect measures summarizing distributions and (2) the variance sum law, an important relationship involving a measure of variability.
- 5.1: Central Tendency
- entral tendency is a loosely defined concept that has to do with the location of the center of a distribution.
- 5.2: What is Central Tendency
- What is "central tendency," and why do we want to know the central tendency of a group of scores? Let us first try to answer these questions intuitively. Then we will proceed to a more formal discussion.
- 5.3: Measures of Central Tendency
- In the previous section we saw that there are several ways to define central tendency. This section defines the three most common measures of central tendency: the mean, the median, and the mode. The relationships among these measures of central tendency and the definitions given in the previous section will probably not be obvious to you. Rather than just tell you these relationships, we will allow you to discover them in the simulations in the sections that follow.
- 5.4: Median and Mean
- The center of a distribution could be defined three ways: (1) the point on which a distribution would balance, (2) the value whose average absolute deviation from all the other values is minimized or (3) the value whose average squared difference from all the other values is minimized.
- 5.5: Measures of the Location of the Data
- The values that divide a rank-ordered set of data into 100 equal parts are called percentiles and are used to compare and interpret data. For example, an observation at the 50th percentile would be greater than 50 % of the other obeservations in the set. Quartiles divide data into quarters. The first quartile is the 25th percentile, the second quartile is 50th percentile, and the third quartile is the the 75th percentile. The interquartile range is the range of the middle 50 % of the data values
- 5.6: Additional Measures
- Although the mean, median, and mode are by far the most commonly used measures of central tendency, they are by no means the only measures. This section defines three additional measures of central tendency: the trimean, the geometric mean, and the trimmed mean.
- 5.10: Shapes of Distributions
- We saw in the section on distributions in Chapter 1 that shapes of distributions can differ in skew and/or kurtosis. Distributions with positive skew normally have larger means than medians. This section presents numerical indexes of these two measures of shape.
- 5.12: Variance Sum Law I - Uncorrelated Variables
- There are many occasions in which it is important to know the variance of the sum of two variables.
- 5.14: Case Study- Using Stents to Prevent Strokes
- Section 1.1 introduces a classic challenge in statistics: evaluating the efficacy of a medical treatment. Terms in this section, and indeed much of this chapter, will all be revisited later in the text. The plan for now is simply to get a sense of the role statistics can play in practice.