Topic Outline
1. 1) Fundamental terminology and concepts
2. a) Population vs. sample
3. b) Parameter vs. statistic
4. c) Variables
5. d) Data – quantitative vs. qualitative; discrete vs. continuous
6. e) Sampling methods
7.
8. 2) Intro to Design of Experiments
9. a) Single vs. Double Blind
10. b) Designs of experiments
11. i) Randomized
12. ii) Matched pairs
13. iii) Randomized block
14.
15. 3) Compute and interpret measures of central tendency and dispersion
16. a) Mean/Median
17. b) Variance, standard deviation, and IQR
18.
19. 4) Probability distributions of random variables
20. a) Random variable: discrete vs. continuous
21. b) The concept of a probability distribution
22. c) Representations of a probability distribution
23. d) Compute the expected value of a probability distribution
24. e) Binomial probability distribution
25. i) Properties of a binomial experiment
26. ii) Compute binomial probabilities
27. iii) Compute the mean and standard deviation of a binomial probability distribution
28.
29. 5) Normal Distribution
30. a) Compute and interpret standard score (z-score)
31. b) Empirical rule
32. c) Techniques for identifying outliers
33. d) Properties of a normal distribution
34. e) Compute the probability of a random variable
35. f) Calculate the value of a random variable from proportion/probability
36. g) Assess the normality of a sample
37.
38. 6) Bivariate Data
39. a) Scatter plot
40. b) Types of correlation (IE: linear, quadratic, exponential, etc.)
41. c) Correlation vs. causation
42. d) Linear (main focus)
43. i) Identify the direction of the relationship
44. ii) Measure the strength of the relationship
45. iii) Use linear regression to make predictions
46. iv) Restrictions on using linear regression
47. v) Calculate and interpret of residuals
48.
49. 7) Sample Variability
50. a) Sampling distributions for sample means, sample proportions, and sample standard deviations
51. i) Using simulation to model sampling variability
52. ii) Central limit theorem with applications (normal theory)
53.
54. 8) Introduction to Statistical Inference
55. a) Estimation of population parameters using confidence intervals
56. b) Conduct hypothesis tests using Probability Value approach
57. c) Comparison of simulation-based and normal theory methods, including underlying assumptions
58.
59. 9) Inferences Involving One Population (using simulation-based and normal theory methods)
60. a) Create and interpret confidence intervals for the population
61. i) Mean
62. ii) Proportion
63. iii) Standard deviation/variance
64. b) Conduct hypothesis tests for the population
65. i) Mean
66. ii) Proportion
67. iii) Standard deviation/variance
68. c) Determine the sample size needed for a given margin of error at a given level of confidence for both means and proportions.
69.
70. 10) Inferences Involving Two Populations (using simulation-based and normal theory methods)
71. a) Independent and dependent samples
72. b) Create and interpret confidence intervals for the:
73. i) difference between two independent means
74. ii) difference between two dependent means
75. iii) difference between two population proportions
76. c) Conduct hypothesis tests for
77. i) two independent means
78. ii) two dependent means
79. iii) two population proportions
80. iv) two population variances/standard deviations
81.
82. 11) Additional Applications of Chi-square
83. a) Goodness of fit test.
84. b) Test for Independence.
85.
86. 12) Analysis of Variance
87. a) Introduction to the Analysis of Variance technique
88. b) The logic behind ANOVA
89. c) Applications of Single-Factor ANOVA
90.
91.