Figure 2-7 (p. 47) A bar graph showing the distribution of personality types in a sample of college students. Because personality type is a discrete variable measured on a nominal scale, the graph is drawn with space between the bars.

Table 2.2 (p. 42) A grouped frequency distribution table showing the data from Example 2.3. The original scores range from a high of X = 94 to a low of X = 53. This range has been divided into 9 intervals with each interval exactly 5 points wide. The frequency column (f) lists the number of individuals with scores in each of the class intervals.

Figure 2-2a (p. 44) An example of a frequency distribution histogram Figure 2-2a (p. 44) An example of a frequency distribution histogram. The same set of data is presented in a frequency distribution table and in a histogram.

Figure 2-5 (p. 46) An example of a frequency distribution polygon Figure 2-5 (p. 46) An example of a frequency distribution polygon. The same set of data is presented in a frequency distribution table and in a polygon. Note that these data are shown in a histogram in Figure 2.2(a).

Figure 2-2 (p. 44) An example of a frequency distribution histogram for grouped data. The same set of data is presented in a grouped frequency distribution table and in a histogram.

Figure 2-6 (p. 47) An example of a frequency distribution polygon for grouped data. The same set of data is presented in a grouped frequency distribution table and in a polygon. Note that these data are shown in a histogram in Figure 2.2(b).

Figure 2-3 (p. 45) A frequency distribution histogram showing the heights for a sample of n = 20 adults.

Table 2.3 (p. 59) A set of N = 24 scores presented as raw data and organized in a stem and leaf display.

Figure 2-15 (p. 59) A grouped frequency distribution histogram and a stem and leaf display showing the distribution of scores from Table 2.3. The stem and leaf display is placed on its side to demonstrate that the display gives the same information provided in the histogram.

Table 2.4 (p. 60) A stem and leaf display with each stem split into two parts. Note that each stem value is listed twice: The first occurrence is associated with the lower leaf values (0-4), and the second occurrence is associated with the upper leaf values (5-9). The data shown in this display are taken from Table 2.3.

Figure 2-9 (p. 48) The population distribution of IQ scores: an example of a normal distribution.

Figure 2-11 (p. 50) Examples of different shapes for distributions.

Table 3.1 (p. 78) Statistics quiz scores for a section of n = 8 students.

Table 3.5 (p. 91) Amount of time to complete puzzle.

Figure 3-12 (p. 94) Median cost of a new, single-family home by region.

Figure 3-13 (p. 95) Measures of central tendency for three symmetrical distributions: normal, bimodal, and rectangular.

Figure 3-14 (p. 96) Measures of central tendency for skewed distributions.

Figure 4-1 (p.105) The statistical model for defining abnormal behavior. The distribution of behavior scores for the entire population is divided into three sections. Those individuals with average scores are defined as normal, and individuals who show extreme deviation from average are defined as abnormal.

Figure 4-3 (p. 108) Frequency distribution for a population of N = 16 scores. The first quartile is Q1 = 4.5. The third quartile is Q3 = 8.0. The interquartile range is 3.5 points. Note that the third quartile (Q3) divides the two boxes at X = 8 exactly in half, so that a total of 4 boxes are above Q3 and 12 boxes are below it.

Figure 4-2 (p. 106) Population distributions of adult heights and adult weights.

Figure 4-6 (p. 116) The graphic representation of a population with a mean of µ = 40 and a standard deviation of  = 4.

Figure 4-7 (p. 117) The population of adult heights forms a normal distribution. If you select a sample from this population, you are most likely to obtain individuals who are near average in height. As a result, the scores n the sample will be less variable (spread out) than the scores in the population.