5:31
Introduction to R
Professor Heather Pierce
5:35
Sample Spaces
6:59
Sets and Set Operations
4:47
Set Laws
8:15
Venn Diagrams
4:01
Basic Probability
5:13
Fundamental Principle of Counting
9:56
Permutations and Combinations
2:13
Sorting into Multiple Groups
5:41
Probability Mass Functions
2:47
Axioms of Probability
9:19
Addition Rule of Probability
9:11
Conditional Probability
8:07
Multiplication Rule for Probability
9:14
Independent Events
2:43
Properties of Independent Events
7:06
System Reliability
6:42
Law of Total Probability
9:30
Bayes' Theorem
3:24
Random Variables
Cumulative Density Function
6:15
Continuous Random Variables
11:21
Probability Density Functions
3:30
Uniform Distribution in R
4:40
Expected Value of Continuous Variables
6:53
Mean Value of a Function
4:00
Expected Value in R
8:22
Variance and Standard Deviation
3:41
Linear Transformation Variance
8:36
Percentiles
4:11
Interquartile Range
2:52
Bernoulli Distributions
6:24
Binomial Distributions
3:25
Binomial Distributions in R
9:28
Hypergeometric Distributions
6:06
Geometric Distributions
5:32
Negative Binomial Distributions
Negative Binomial Distributions in R
9:10
Poisson Distributions
2:01
Poisson Distributions in R
6:26
Exponential Distributions
Exponential-Poisson Connection
3:48
Exponential Distribution in R
9:12
Gamma Distributions
4:17
Gamma Distribution in R
2:45
Chi-Squared Distributions
Chi Squared Distributions in R
3:33
Normal Distributions
Normal Distributions in R
3:38
Law of Large Numbers
3:01
Central Limit Theorem
5:08
Averages
7:49
Variance
6:51
5:58
Histograms
3:22
Stem and Leaf Plot
2:32
Boxplots
7:05
Correlation and Scatterplots
4:52
Regression
4:36
Idea of Confidence Intervals
4:59
t-Distribution
9:23
Confidence Intervals for the Mean
4:34
Confidence Intervals for Proportions
6:10
Introduction to Hypothesis Testing
5:25
Null and Alternative Hypotheses
