List of Figures

1. Left: Required. Right: Recommended.
2. Stem weight data. (o: the with nitrogen data. x: the without nitrogen data.
3. Fundamental relationship between probability and inferential statistics.
4. The Cycle of Statistical Procedure.
5. Corrosion results for Example 1.3.
6. Sample mean as a centroid of the ``with nitrogen'' stem weight.
7. Different data sets. Difference in the means is roughly the same.
8. Plot of tensile strength and cotton percentages.
9. Table of Car Battery Life (in years).
10. Relative frequency histogram.
11. Estimating frequency distribution.
12. Skewness of data.
13. Tree diagram for Example 2.2.
14. Tree diagram for Example 2.3.
15. Events represented by various regions.
16. Tree diagram for Example 2.14.
17. Additive rule of probability.
18. Tree diagram for Example 2.36.
19. An electrical system for Example 2.38.
20. Venn diagram for the events $A$, $E$, and $E'$.
21. Tree diagram for the data.
22. Partitioning the sample space S.
23. Tree diagram for Example 2.41.
24. Bar chart and probability histogram
25. Discrete cumulative distribution.
26. Typical density functions.
27. $P(a< X < b)$
28. Continuous cumulative distribution function.
29. Distributions with equal means and unequal dispersions.
30. Variability of continuous observations about the mean.
31. Variability of discrete observations about the: mean.
32. Histogram for the tossing of a die.
33. Binomial Probability Sums $B(r;n,p)=\sum_{x=0}^r b(x;n,p)$.
34. Poisson Probability Sums $P(x;\mu)=\sum_{x=0}^r p(x;\mu)$.
35. Poisson density functions for different means.
36. The density function for a random variable on the interval $[1,3]$.
37. The normal curve.
38. Normal curves with $\mu _1 < \mu _2$ and $\sigma _1 = \sigma _2$.
39. Normal curves with $\mu _1 =\mu _2$ and $\sigma _1 < \sigma _2$.
40. Normal curves with $\mu _1 < \mu _2$ and $\sigma _1 < \sigma _2$.
41. $P(x_1 < X < x_2)$ : area of the shaded region.
42. $P(x_1 < X < x_2)$ for different normal curves.
43. The original and transformed normal distributions.
44. Areas for Example 6.2.
45. Areas for Example 6.3.
46. Area for Example 6.4.
47. Area for Example 6.5.
48. Areas for Example 6.6.
49. Area for Example 6.7.
50. Area for Example 6.8.
51. Area for Example 6.9.
52. Specifications for Example 6.10.
53. Area for Example 6.11.
54. Area for Example 6.13.
55. Normal approximation of $b(x; 15,0.4)$.
56. Normal approximation of $b(x; 15,0.4)$ and $\sum_{x=7}^9 b(x; 15,0.4)$.
57. Histogram for $b(x; 6, 0.2)$.
58. Histogram for $b(x; 15, 0.2)$.
59. Area for Example 6.15.
60. Area for Example 6.16.
61. Gamma Distributions.
62. Lognormal Distributions.
63. Box-and-Whisker plot.
64. Box-and-Whisker plot for nicotine data.
65. Stem-and-leaf plot for the nicotine data.
66. Data for Example 8.4.
67. Box-and-whisker plot for thickness of paint can ``ears''.
68. Quantile plot for paint data.
69. Normal quantile-quantile plot for paint data.
70. Data for Example 8.5.
71. Standard Normal Quantile, $q_{0,1}(f)$.
72. Illustration of the central limit theorem (distribution of $\bar{X}$ for $n =1$, moderate $n$, and large $n$).
73. Area for Example 8.6.
74. Area for Example 8.7.
75. Area for Example 8.8.
76. Area for Example 8.9.
77. The chi-squared distribution.
78. The $t$-distribution curves for $\nu = 2,5$, and $\infty$
79. Symmetry property of the $t$-distribution.
80. The $t$-values for Example 8.13.
81. Typical F-distributions.
82. Illustration of the $f_{\alpha }$ for the $F$-distribution.
83. Data from three distinct samples.
84. Data that easily could have come from the same population.