STT1001Fall 2023Mathematics
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Probability and Statistics
Detailed view of the course, studied concepts, technologies used, and major academic work associated with it.
Code
STT1001
Session
Fall 2023
Domain
Mathematics
Overview
Introductory course in probability and applied statistics focused on probabilistic models, descriptive statistics, and the foundations of statistical inference. The course provided a strong basis for data analysis, sampling, and the interpretation of statistical results.
Technologies used
ProbabilitésStatistique descriptiveInférence statistiqueTests d’hypothèses
Key concepts covered
- Descriptive statistics
- Statistical series, histograms, and polygons
- Measures of central tendency
- Measures of dispersion
- Statistical moments
- Probability and conditional probability
- Simultaneous events and multiplication rule
- Independence and total probability rule
- Bayes’ formula
- Discrete and continuous random variables
- Discrete distributions: binomial, geometric, negative binomial, hypergeometric, Poisson
- Continuous distributions: uniform, exponential, gamma, normal
- Binomial approximation by the normal distribution
- Central limit theorem
- Sampling and parameter estimation
- Point estimation and estimator quality
- Unbiased estimators and efficiency
- Confidence intervals
- Chi-square, Student’s t, and Fisher distributions
- Hypothesis tests on a mean, proportion, and variance
- Tests on two means, two variances, and two proportions
Coursework and evaluated components
- Exercises in descriptive statistics
- Applications on probability and probability distributions
- Analysis of discrete and continuous random variables
- Midterm exam
- Final exam