Why?
Inferential stats are the tool used to test hypotheses we have on the population, which practically enables data-driven decisions confidently supported by significant statistical evidence.
What?
This course extends basic statistical concepts to cover inferential statistics, hypothesis testing with multiple samples, analysis of variance, correlation, and regression analysis. Students will learn to select appropriate statistical tests, analyze various types of data, build and interpret regression models, and understand the statistical principles underlying machine learning techniques.
Curriculum:
Discrete Probability Distributions
Random variables, the binomial, hypergeometric and Poisson distributions.
Continuous Probability Distributions
The uniform, normal and exponential distributions.
Sampling Methods and the Central Limit Theorem
Sampling methods, sampling error and the central limit theorem.
Estimation and Confidence Intervals
Point estimates and confidence intervals for means and proportions, and sample size.
One-Sample Tests of Hypothesis
The hypothesis testing procedure, one- and two-tailed tests, and tests for means and proportions.
Two-Sample Tests of Hypothesis
Comparing two means or proportions, independent and paired samples.
Notes
The key is to know what to use, so focus on when each tool is used and for what. Practically, you will never have a work task "do Anova on this dataset please", rather you'll have to find "which of our techniques had better influence on customer retention" for example and you will need to figure out which statistical tool is useful and then apply it.