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In this course, you will learn :
- Learn about the binomial distribution, in which each observation represents one of two possible outcomes, and how to calculate the probability of a binomial distribution.
- Use conditional probability concepts to deduce the Bayes rule and the Bayes theorem.
- Normal distributions are used to determine probabilities, and the Z-table is used to find the proportions of observations that are above, below, or in between values.
- Use critical values to determine whether a treatment has altered the value of a population parameter.
- When we have a limited sample size, we can test the effect of a therapy or examine the difference in means between two groups.
- Use logistic regression results to make a prediction about the relationship between categorical dependent variables and predictors
Syllabus :
- Simpson’s Paradox
- Binomial Distribution
- Bayes Rule
- Sampling Distributions and Central Limit Theorem
- Hypothesis Testing
- T-Tests and A/B Tests
- Logistic Regression
