1. Review of Probability
1.1. Definitions (Experiment, Sample Space, Event)
1.2. Operations with Events
1.3. Probability of an Event
1.4. Additive Rule
1.5. Conditional Probability
1.6. Multiplicative Rule
1.7. Concept of Random Variables
1.7.1. Definition of Random Variable
1.7.2. Probability Density Function (pdf), Cumilative Density Function (cdf)
1.7.3. Mean of Random Variable
1.7.4. Variance of Random Variable
1.8. Some Discrete Random Variables (Bernoulli, Binomial, Uniform)
1.9. Some Continuous Random Variables (Uniform, Normal)
2. Review of Statistics
2.1. Population and Sample
2.2. Random Sample
2.2.1. Difinition of Random Sample
2.2.2. Measures of Central Location
2.2.3. Measure of Variation
2.3. Distribution of Sample Mean (Central Limit Theorem)
2.4. Distributions of Sample from Normally Distributed Population
2.4.1. Chi-Square Distribution
2.4.2. t (Student) Distribution
2.4.3. F (Fisher) Distribution
2.5. Estimation
2.5.1. Point Estimator
2.5.2. Interval Estimator
2.5.3. Estimation of Proportion
3. Test of Hypotheses
3.1. Statistical Hypotheses
3.2. Testing Statistical Hypothesis
3.3. One-Tailed and Two-Tailed Tests
3.4. Tests Concerning Means
3.5. Tests Concerning Variances
3.4. Tests Concerning Proportions
3.5. Goodness-Fit Test
3.6. Testing Several Proportions
3.7. Test Concerning Several Means (Analysis of Variance - ANOVA)
4. Experimental Designs for Single-Factor Experiments
4.1. Completely Randomized Design
4.2. Randomized Complete Block
4.3. Latin Square Design
4.4. Comparision of Treatment Means
4.4.1. Least Significant Difference (LSD)
4.4.2. Duncan Multiple Range Test (DMRT)
4.4.3. Orthogonal Contrasts
5. Factorial Experiments
5.1. Introduction
5.2. Factorial Experiments: An Example
5.3. Main Effects and Interactions
5.3. Two-Factor Experiment in Randomized Complete Block Design
5.5. Split-Plot Design
5.6. Split-Split-Plot Design
6. Regression
6.1. Introduction
6.2. Measures of Relation of Two Variables (Covariance, Correlation)
6.3. Simple Linear Regression
6.3.1. Least Squared Estimator of Regression Parameters
6.3.2. Assumptions about Simple Linear Regression Model
6.3.3. Inferences about the Slope of a Line
6.3.4. Analysis of Avriance of Regression Model
6.3.5. Prediction Using a Regression Line
7. Multiple Regression
7.1. Assumptions about Multiple Linear Regression Model
7.2. Least Squared Estimator of Multiple Regression Parameters
7.3. Inferences about the Parameter of Regression Line
7.4. Analysis of Avriance of Regression Model
7.5. Prediction Using a Regression Line
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K.A. Gomez, and A.A. Gomez. Statistical Procedures for Agricultural Research, 2nd ed. New York: John Wiley & Son, 1984.
William Mendenhall, and R.L. Scheaffer. Mathematical Statistics with Aplications. North Scituate: Duxbury Press, 1973.
Ronald E. Walpole. Introduction to Statistics, 3rd ed. New York: Macmillan Publishing Co., Inc., 1982.
George W. Snedecor, and W. G. Cochran. Statistical Methods, 7th ed. Ames: Iowa State University, 1980.