# Réponse finale obtenue du bot:
response = « Here is a course outline on Statistics and Probability:
**Course Title:** Statistics and Probability
**Course Description:** This course covers the fundamental concepts of probability theory, descriptive statistics, inferential statistics, and stochastic models. Students will learn to analyze data, make inferences, and model uncertain events.
**Course Outline:**
**Week 1-2: Theory of Probability**
* Introduction to probability
* Basic concepts: sample space, event, probability measure
* Rules of probability: commutativity, associativity, distributivity
* Conditional probability and independence
* Bayes’ theorem
**Week 3-4: Descriptive Statistics**
* Introduction to descriptive statistics
* Measures of central tendency: mean, median, mode
* Measures of variability: range, variance, standard deviation
* Data visualization: histograms, box plots, scatter plots
* Exploratory data analysis (EDA)
**Week 5-6: Inferential Statistics**
* Introduction to inferential statistics
* Hypothesis testing: one-sample and two-sample t-tests, ANOVA
* Confidence intervals: one-sample and two-sample confidence intervals
* Non-parametric tests: Wilcoxon rank-sum test, sign test
**Week 7-8: Stochastic Models**
* Introduction to stochastic models
* Random variables: discrete and continuous
* Probability distributions: Bernoulli, binomial, Poisson, normal
* Stochastic processes: Markov chains, random walks
* Applications of stochastic models: queuing theory, reliability engineering
**Week 9-10: Advanced Topics**
* Bayesian statistics
* Monte Carlo methods
* Bootstrap resampling
* Time series analysis
**Assessment:**
* Quizzes and assignments (40%)
* Midterm exam (20%)
* Final project (30%)
* Class participation and attendance (10%)
**Textbook:**
* « Probability and Statistics for Engineers and Scientists » by Ronald E. Walpole and Raymond H. Myers
* « Introduction to Probability and Statistical Inference » by George Casella and Roger L. Berger
**Software:**
* R or Python programming languages for statistical analysis
* Excel or other spreadsheet software for data visualization and manipulation
**Prerequisites:** None, but a background in mathematics and computer science is recommended.
This course outline should provide a comprehensive coverage of statistics and probability, including the theoretical foundations, descriptive statistics, inferential statistics, stochastic models, and advanced topics. The assessment will be based on quizzes, assignments, midterms, finals, and class participation. »