Course Description
As you may or may not know, statistics can be dangerous. They can be used to manipulate public opinion, sell you products or services you don't need, change policy decision, and significantly affect our lives. Therefore, it is important to view statistics with a critical and knowledgeable eye. We need to understand what statistics mean, how they are generated, and what their limitations are, to know when they are appropriate and when they are not.
This online statistics course will introduce you to the discipline of statistics as a science of understanding and analyzing data. If you want to simultaneously learn R and applied statistics, you can count on this course to take you from beginner to an advanced level! Start off with the basics of R and exploring data, then move on to understanding uncertainty, confidence intervals and hypothesis testing, then modeling with simple and multiple regression to learn how to use data to make inferences and conclusions about real world phenomena.
What is R?
R is a popular programming language for statistical computing, and has seen an exponential growth in adaptation in recent years. R is growing rapidly because you can use it for free and users can submit new packages to expand functionality. You can use text interfaces or download your own graphical interfaces making R highly customizable! Learning how to use R will give you a great long term advantage for any of your work in statistics or data science as R continues to grow worldwide!
UNITS
Each Unit includes an instructional component, R tutorial videos, and Section exercises to give you an opportunity to see statistics in action and to help you learn how to apply the statistics to answer a real world question.
Each Section contains several practice problems to reinforce concepts from the Units and demonstrate how R can be used to solve real word statistical problems.
What am I going to get from this course?
This course will introduce you to the discipline of statistics as a science of understanding and analyzing data. If you want to simultaneously learn R and applied statistics, you can count on this course to take you from beginner to an advanced level.
Curriculum
Module 1: Graphing and Summarizing Data
41:45
Lecture 1
Section 0: Introduction to R
06:53
Lecture 2
Graphing and Summarizing Data Part 1
13:32
Lecture 3
Graphing and Summarizing Data Part 2
14:02
Lecture 4
Graphing and Summarizing Data: Section Video and Additional Practice Problems
07:18
Module 2: Measures of Association and Regression
43:50
Lecture 5
Measures of Association and Regression Part 1
18:03
Lecture 6
Measures of Association and Regression Part 2
18:33
Lecture 7
Measures of Association and Regression: Section Video and Additional Practice Problems
07:14
Module 3: Collecting Data
36:50
Lecture 8
Collecting Data Part 1
17:02
Lecture 9
Collecting Data Part 2
12:44
Lecture 10
Collecting Data: Section Video and Additional Practice Problems
07:04
Module 4: Basic Probability
55:35
Lecture 11
Basic Probability Part 1
22:18
Lecture 12
Basic Probability Part 2
18:11
Lecture 13
Basic Probability Part 3
06:04
Lecture 14
Basic Probability: Section Video and Additional Practice Problems
09:02
Module 5: Random Variables
54:32
Lecture 15
Random Variables Part 1
09:47
Lecture 16
Random Variables Part 2
06:23
Lecture 17
Random Variables Part 3
07:55
Lecture 18
Random Variables Part 4
12:27
Lecture 19
Random Variables: Section Video and Additional Practice Problems
18:00
Module 6: Sampling Distributions
35:56
Lecture 20
Sampling Distributions Part 1
17:07
Lecture 21
Sampling Distributions Part 2
03:40
Lecture 22
Sampling Distributions Part 3
02:39
Lecture 23
Sampling Distributions Part 4
05:07
Lecture 24
Sampling Distributions: Section Video and Additional Practice Problems
07:23
Module 7: Confidence Intervals
43:53
Lecture 25
Confidence Intervals Part 1
09:18
Lecture 26
Confidence Intervals Part 2
08:08
Lecture 27
Confidence Intervals Part 3
10:09
Lecture 28
Confidence Intervals Part 4
10:03
Lecture 29
Confidence Intervals: Section Video and Additional Practice Problems
06:15
Module 8: Hypothesis Testing
01:10:35
Lecture 30
Hypothesis Testing Part 1
19:48
Lecture 31
Hypothesis Testing Part 2
14:22
Lecture 32
Hypothesis Testing Part 3
09:28
Lecture 33
Hypothesis Testing Part 4
19:36
Lecture 34
Hypothesis Testing: Section Video and Additional Practice Problems
07:21
Module 9: Analysis of Two-way Tables
29:50
Lecture 35
Analysis of Two-way Tables Part 1
15:24
Lecture 36
Analysis of Two-way Tables Part 2
11:27
Lecture 37
Analysis of Two-way Tables: Section Video and Additional Practice Problems
02:59
Module 10: Inference for Regression
37:53
Lecture 38
Inference for Regression Part 1
16:58
Lecture 39
Inference for Regression Part 2
15:17
Lecture 40
Inference for Regression: Section Video and Additional Practice Problems
05:38