Course Weekly Lecture Plan

Week	Topic
1	Pretest, Introduction to Probability and Statistic, Course Objectives
2	The Definition of Statistics. Classification of Statistics; Descriptive and Inferential Statistics Data Description and Graphical Methods:
3	Histogram, Stem and Leaf Diagrams, Frequency distributions, box plots.
4	Numerical Methods of Describing Data: (for Ungrouped and Grouped data) Measure of Central Tendency, Measure of Variability, Measure of Relative Standing.
5	Normal distribution, Moments-Parameters of Distribution; Skewness, Kurtosis, Some computer applications.
6	Approaches to the theory of probability, definition of probability. Fundamentals of Probability, basic concepts: experiment, outcome, sample space, event, elementary event, event space.
7	Computing Probabilities: Set algebra, Combinatorial analysis: Basic principle of counting, Permutations, Combinations.
8	Conditional Probability and Independence. Bayes formula. Conditional probability applications and examples,
9	Bernoulli trials, Binomial distribution Random Variables, Probability Distributions Functions
10	Random Variables, Probability Distributions Functions
11	Probability Distributions Functions, Probability Density Function, Expectations and Moments
12	Sampling Distribution, Sampling Theory, Random Sampling, Central Limit Theorem, Statistical Inference: Small Sample Results
13	Statistical Inference: Small Sample Results Statistical Inference: Large Sample
14	General Linear Models and Applications Correlation Theory, Computer Applications, The Chi-Square Test; Time Series

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