Data Analytics: Advanced Statistics in R
Description
This course continues and develops on the material from the Fundamentals of Probability and Statistic in R. It covers statistical inference and regression models. This course is presented for students who have already learned the fundamentals and want to move to the more advanced courses. This course covers commonly used statistical inference methods for numerical and categorical data. Students will learn how to set up and perform hypothesis tests, interpret p-values, and report the results of their analysis in a way that is interpretable for customers or the public. With the help of several examples using R, students will learn to report the uncertainty of the quantity of interest.
Inferential Statistics [4]
Concept of sample and population
Concept of statistics and parameter
Sample Distribution
Interval Estimation [4]
Confidence Intervals for Mean, Variance and Proportion
Hypothesis Testing [6]
Type of Hypotheses and two types of errors
The p-value and the level of significance
Inference for comparing Means, Variances and Proportions
Chi-Square GOF Test and Chi-Square Independence Test
ANOVA
Regression Models [4]
Linear Regression & Multivariable Regression
Residuals & Diagnostics
Model Selection
Regularization [2]
Ridge Regression
The Lasso
Course Schedule
Day/Date | Time | Topic |
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