Understanding Probability and Statistics
Gain a strong foundation in probability theory and inferential statistics with this comprehensive course. Perfect for university students or anyone wanting to understand and interpret statistical results better.
What you’ll learn
- A strong foundation in probability theory including the rules of probability, dependent and independent events, conditional probability and Bayes’ theorem
- Discrete probability distributions, expected value and variance of a variable, the binomial distribution and the Poisson distribution
- The properties of the normal distribution, the standard normal distribution, calculating probabilities using the standard normal tables and software
- Sampling distributions and the central limit theorem
- Point estimates and confidence intervals for the population mean, variance and proportion. The t-distribution and the Chi-square distribution
- Comprehensive introduction to hypothesis testing. Hypothesis testing for one and for two population parameters. The rejection region and the p-value methods.
Most students do not find it hard to understand descriptive statistics but struggle with probability and hypothesis testing. The aim of this course is to help students to gain a deeper understanding of these tougher topics. Since the course assumes a basic understanding of descriptive statistics it does not cover these topics but starts with a section on probability. It then proceeds with discrete and continuous distributions, including the binomial, the Poisson and the normal distributions. When students have developed a proper understanding of probability and distributions, specifically the normal distribution, they are well prepared to understand sampling distributions and the central limit theorem which plays essential roles in estimation and hypothesis testing. When students have mastered the first part of the course, they are well prepared to understand estimation and hypothesis testing which form the last part of the course. The section on estimation covers point estimates and confidence intervals. The hypothesis testing section covers both tests for one population parameter and for two population parameters. It also explains important concepts like type I and type II errors, as well as the p-value of a test.
Throughout the course the focus is on understanding. It makes use of many simulations and visual illustrations to effectively communicate the course content. Some of the examples that are covered in the lectures are also demonstrated in Excel.
Who this course is for:
- Any person who wants to have a strong foundation in probability and inferential statistics, including estimation and hypothesis testing
- University or college students who are doing a course in statistics and would like to have a better understanding of the content of the course
- Any person or student who wants to understand a little more of the theory so as to be able to interpret the results better
