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Inferential Statistics

Approx. 23 hours to complete

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Course Summary

Inferential Statistics is a course that teaches you how to use data and statistical methods to make inferences and draw conclusions about populations based on sample data. This course covers topics such as hypothesis testing, confidence intervals, and regression analysis.

Key Learning Points

Learn how to use statistical methods to draw conclusions about populations based on sample data
Understand hypothesis testing and how to perform it
Master confidence intervals and regression analysis

Learning Outcomes

Understand how to use statistical methods to make inferences about populations
Be able to perform hypothesis testing and interpret the results
Master confidence intervals and regression analysis

Prerequisites or good to have knowledge before taking this course

Basic knowledge of statistics
Familiarity with programming in R

Course Difficulty Level

Intermediate

Course Format

Online and Self-paced
Video Lectures
Quizzes and Assignments

Similar Courses

Introduction to Probability and Data
Statistics with R
Data Science Essentials

Related Education Paths

Notable People in This Field

Nate Silver
Edward Tufte

Related Books

Description

Inferential statistics are concerned with making inferences based on relations found in the sample, to relations in the population. Inferential statistics help us decide, for example, whether the differences between groups that we see in our data are strong enough to provide support for our hypothesis that group differences exist in general, in the entire population.

We will start by considering the basic principles of significance testing: the sampling and test statistic distribution, p-value, significance level, power and type I and type II errors. Then we will consider a large number of statistical tests and techniques that help us make inferences for different types of data and different types of research designs. For each individual statistical test we will consider how it works, for what data and design it is appropriate and how results should be interpreted. You will also learn how to perform these tests using freely available software. For those who are already familiar with statistical testing: We will look at z-tests for 1 and 2 proportions, McNemar's test for dependent proportions, t-tests for 1 mean (paired differences) and 2 means, the Chi-square test for independence, Fisher’s exact test, simple regression (linear and exponential) and multiple regression (linear and logistic), one way and factorial analysis of variance, and non-parametric tests (Wilcoxon, Kruskal-Wallis, sign test, signed-rank test, runs test).

Outline

Before we get started...
Welcome to Inferential Statistics!
Hi there
How to navigate this course
How to contribute
General info - What will I learn in this course?
Course format - How is this course structured?
Requirements - What resources do I need?
Grading - How do I pass this course?
Team - Who created this course?
Honor Code - Integrity in this course
Useful literature and documents

Comparing two groups
1.01 Null hypothesis testing
1.02 P-values
1.03 Confidence intervals and two-sided tests
1.04 Power
1.05 Two independent proportions
1.06 Two independent means
1.07 Two dependent proportions
1.08 Two dependent means
1.09 Controlling for other variables
Comparing two groups - Drawing inferences
Comparing two groups - Independent groups
Comparing two groups - Dependent groups
Comparing two groups - Controlling for other variables
Comparing two groups - Transcripts
Comparing two groups

Categorical association
2.01 Categorical association and independence
2.02 The Chi-squared test
2.03 Interpreting the Chi-squared test
2.04 Chi-squared as goodness-of-fit
2.05 The Chi-squared test - sidenotes
2.06 Fisher's exact test
Categorical association - Chi-squared test for association
Categorical association - Chi-squared test for goodness of fit
Categorical association - Sidenotes and an alternative to the Chi-squared test
Categorical association - Transcripts
Categorical association

Simple regression
3.01 The regression line
3.02 The regression equation
3.03 The regression model
3.04 Predictive power
3.05 Pitfalls in regression
3.06 Testing the model
3.07 Checking assumptions
3.08 CI and PI for predicted values
3.09 Exponential regression
Simple regression - Describing quantitative association
Simple regression - Drawing inferences
Simple regression - Exponential regression
Simple regression - Transcripts
Simple regression

Multiple regression
4.01 Regression model
4.02 R and R-squared
4.03 Overall test
4.04 Individual tests
4.05 Checking assumptions
4.06 Categorical predictors
4.07 Categorical response variable
4.08 Interpreting results
Multiple regression - Model
Multiple regression - Tests
Multiple regression - Categorical predictors, categorical response variable and example
Multiple regression - Transcripts
Multiple regression

Analysis of variance
5.01 One-way ANOVA
5.02 One-way ANOVA - Assumptions and F-test
5.03 One-way ANOVA - Post-hoc t-tests
5.04 Factorial ANOVA
5.05 Factorial ANOVA - Assumptions and tests
5.06 ANOVA and regression
Analysis of variance - Basics and one-way ANOVA
Analysis of variance - Factorial ANOVA and regression
Analysis of variance - Transcripts
Analysis of variance

Non-parametric tests
6.01 Non-parametric tests - Why and when
6.02 The sign test
6.03 One sample - Wilcoxon signed rank test
6.04 Two samples - Wilcoxon/Mann-Whitney test
6.05 Several samples - Kruskal-Wallis test
6.06 Spearman correlation
6.07 The runs test
Non-parametric tests - The basics
Non-parametric tests - Comparing groups with respect to mean rank
a note about test-names
Non-parametric tests - Rank-based correlation & randomness
Non-parametric tests - Transcripts
Non-parametric tests

Exam time!
Practice exam
Final exam

Summary of User Reviews

This course on inferential statistics has received high praise from users. Many have commented on the in-depth and engaging lectures by the instructor, making it easy to understand the concepts.

Key Aspect Users Liked About This Course

in-depth and engaging lectures

Pros from User Reviews

Well-structured course content
Practical examples provided to understand complex concepts
Interactive quizzes and assignments that enhance understanding
Instructor is knowledgeable and approachable
Good for beginners and those with some statistical background

Cons from User Reviews

Some users found the pace too fast
The course requires a lot of time commitment
Limited opportunities for interaction with other learners
Some videos are quite long and may be difficult to follow
More focus on theory than on practical applications

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