Workshop in Probability and Statistics
- 21.5 hours on-demand video
- 1 article
- 42 downloadable resources
- Full lifetime access
- Access on mobile and TV
- Certificate of Completion
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- By the end of this workshop you should be able to pass any introductory statistics course
- This workshop will teach you probability, sampling, regression, and decision analysis
- Knowledge of basic algebra
- Microsoft Excel (recommended)
This workshop is designed to help you make sense of basic probability and statistics with easy-to-understand explanations of all the subject's most important concepts. Whether you are starting from scratch or if you are in a statistics class and struggling with your assigned textbook or lecture material, this workshop was built with you in mind.
- Current students who are (or will soon be) taking a course in introductory statistics with their home institutions
- People in business who want a better grasp of probability and statistics.
Basic introduction to probability. Examples using the fundamental probability equation.
How to find the probability of multiple events all taking place when we know the probability of each event.
Introduction to conditional probability and how to solve using the fundamental probability equation.
Popular real estate website Wozill has developed an algorithm for predicting the eventual sales price of any house before it goes on the market. Sometimes the estimate provided by the algorithm is high, and sometimes it is low, but overall the expected difference between the prediction given by the algorithm and the actual sales price of the home is zero--meaning that the averages of all predictions and recorded sales are the same.
Unfortunately, the standard deviation of the difference between the algorithm's predictions and the actual sales prices of the homes is rather large: $100k, normally distributed around $0. Approximately what percentage of estimates provide by the Wozill algorithm will be $200k or more below the actual sales price of the home?
Portfolio Analysis has to do with how to calculate the joint variance (and standard deviation) of multiple random variables. This video includes the equation to calculate joint variances when there may be multiple instances of two random variable and the variables may be correlated.
Introduction to Sampling and the Central Limit Theorem. Also how the size of a sample relates to the accuracy of a prediction for a population parameter.
More on simple linear regression including how to analyze the output of regression analysis using example data. Definitions of R-squared, coefficients, and standard errors. Also how to test the significance of the relationship between an independent and dependent variable using hypothesis testing.
A grab bag of additional regression concepts including how to calculate confidence intervals for predicted changes to a dependent variable based on a change to an independent variable, degrees of freedom with multiple independent variables, standardized coefficients, and the F-statistic.
Overview of the four main assumptions of linear regression: linearity, independence of errors, homoscedasticity, and normality of residual distribution.