Course Description
This course provides an elementary introduction to probability and statistics with applications. Topics include: basic probability models; combinatorics; random variables; discrete and continuous probability distributions; statistical estimation and testing; confidence intervals; and an introduction to linear regression.
Lecture Notes
LEC # | TOPICS | LECTURE NOTES |
---|---|---|
1 | Probability, Set Operations | (PDF) |
2 | Properties of Probability Finite Sample Spaces, Some Combinatorics | (PDF) |
3 | Multinomial Coefficients, Union of Events | (PDF) |
4 | Matching Problem, Conditional Probability | (PDF) |
5 | Independence of Events | (PDF) |
6 | Solutions to Problem Set 1 | (PDF) |
7 | Bayes' Formula | (PDF) |
8 | Random Variables and Distributions | (PDF) |
9 | Cumulative Distribution Function | (PDF) |
10 | Marginal Distributions | (PDF) |
11 | Conditional Distributions, Multivariate Distributions | (PDF) |
12 | Functions of Random Variables, Convolution | (PDF) |
13 | Functions of Random Variables: Sum, Product, Ratio, Maximum, Change of Variables | (PDF) |
14 | Linear Transformations of Random Vectors, Review of Problem Set 4 | (PDF) |
15 | Review for Exam 1 | (PDF) |
16 | Expectation, Chebyshev's Inequality | (PDF) |
17 | Properties of Expectation, Variance, Standard Deviation | (PDF) |
18 | Law of Large Numbers, Median | (PDF) |
19 | Covariance and Correlation, Cauchy-Schwartz Inequality | (PDF) |
20 | Poisson Distribution, Approximation of Binomial Distribution, Normal Distribution | (PDF) |
21 | Normal Distribution, Central Limit Theorem | (PDF) |
22 | Central Limit Theorem, Gamma Distribution, Beta Distribution | (PDF) |
23 | Estimation Theory, Bayes' Estimators | (PDF) |
24 | Bayes' Estimators | (PDF) |
25 | Maximum Likelihood Estimators | (PDF) |
26 | Chi-square Distribution, t-distribution, Confidence Intervals for Parameters of Normal Distribution | (PDF) |
27 | Confidence Intervals for Parameters of Normal Distribution | (PDF) |
28 | Review for Exam 2 | (PDF) |
29 | Hypotheses Testing, Bayes' Decision Rules | (PDF) |
30 | Most Powerful Test for Two Simple Hypotheses | (PDF) |
31 | t-test | (PDF) |
32 | Two-sample t-test, Goodness-of-fit Tests, Pearson's Theorem | (PDF) |
33 | Simple Goodness-of-fit Test, Composite Hypotheses | (PDF) |
34 | Contingency Tables, Tests of Independence and Homogeneity | (PDF) |
35 | Kolmogorov-Smirnov Goodness-of-fit Test | (PDF) |
36 | Review of Test 2 | (PDF) |
37 | Review for the Final Exam | (PDF) |
Exams and Solutions
PRACTICE TESTS SOLUTIONS
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