STAT 270: Introduction to Probability and Statistics at Simon Fraser University is a comprehensive course that explores a wide range of topics in probability and statistics. Students will delve into graphical and numerical descriptive statistics, elementary probability rules, basic combinatorial formulae, conditional probability, Bayes' Theorem, independence, discrete and continuous distributions, as well as single and two-sample inference.

✔ (Summary) What You Will Learn:

• Graphical and Numerical Descriptive Statistics: Students will learn about various tools and techniques, such as histograms, boxplots, scatterplots, sample mean, sample median, sample standard deviation, sample coefficient of relative variation, and sample correlation coefficient, to effectively summarize and visualize data.
• Elementary Probability Rules, Basic Combinatorial Formulae, Conditional Probability, Bayes' Theorem, and Independence: These concepts are fundamental to understanding probability and how events relate to each other under different conditions.
• Discrete Distributions: Students will explore probability mass functions, expectation, binomial distribution, and Poisson distribution, which are essential in modeling random discrete events.
• Continuous Distributions: This topic covers probability density functions, expectation, variance, coefficient of variation, cumulative distribution functions, uniform distribution, gamma distribution, exponential distribution, normal distribution, normal approximation to the binomial distribution, and jointly distributed random variables. These are vital for modeling continuous random phenomena.
• Statistics and Their Distributions: The Central Limit Theorem, a key result in probability theory, will be covered. It states that the distribution of the sum of a large number of independent and identically distributed random variables tends towards a normal distribution.
• Single Sample Inference: Students will learn about estimation and hypothesis testing related to proportions and means.
• Two Sample Inference: This section covers estimation and hypothesis testing for differences in proportions and means, considering both paired and non-paired data.

By the conclusion of this course, students will have gained a strong grasp of fundamental concepts in probability and statistics. They will be well-equipped to apply these principles to solve real-world problems effectively.