IMSE 317
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Syllabus
Lecture slides
Chapter 5. Joint random variables
Lecture slides
Chapter 1. Descriptive statistics
1.1 Course introduction
1.2 Population, sample, and variable
1.3 Four levels of measurement
1.4 Graphical integrity
1.5 Measures of location
1.6 Measures of variability
1.7 Data visualization
Chapter 2. Probability
2.1 Sample space, events
2.2 Set operations
2.3 Probability axioms
2.4 Product rule
2.5 Permutation
2.6 Combination
2.7 Conditional probability
2.8 Multiplication rule
2.9 Total probability theorem
2.10 Bayes’ rule
2.11 Independence
Chapter 3. Discrete random variables
3.1 Random variables
3.2 Probability mass function
3.3 Cumulative Distribution Function
3.4 Expected value
3.5 Variance
3.6 Uniform distribution
3.7 Bernoulli distribution
3.8 Binomial distribution
3.9 Geometric distribution
Chapter 4. Continuous random variables
4.1 Probability density function
4.2 Continuous CDF
4.3 Continuous expectation & variance
4.4 Continuous uniform distribution
4.5 Exponential distribution
4.6 Normal distribution
Chapter 5. Joint random variables
5.1 Joint & marginal PMF
5.2 Conditional PMF
5.3 Discrete independence
5.4 Joint & marginal PDF
5.5 Conditional PDF
5.6 Continuous independence
5.7 Joint expectation
5.8 Covariance
5.9 Correlation
Chapter 6. Limit theorems
6.1 Chebyshev inequality
6.2 Law of large numbers
6.3 Central limit theorem
Chapter 7. Point & interval estimations
7.1 Maximum likelihood estimate
7.2 Confidence interval
Chapter 8. Hypothesis testing
8.1 Hypothesis test
8.2
p
-value
8.3
z
-test
8.4
t
-test
8.5 Proportion test
8.6 Two-sample
z
-test
8.7 Two-sample
t
-test
Lecture slides
Chapter 5. Joint random variables
Chapter 5. Joint random variables