1 


12:35 

Introduction to descriptive statistics and central tendency. Ways to measure the average of a set: median, mean, mode 
2 


06:42 

The difference between the mean of a sample and the mean of a population. 
3 


12:23 

Variance of a population. 
4 


11:18 

Using the variance of a sample to estimate the variance of a population 
5 


13:07 

Review of what we've learned. Introduction to the standard deviation. 
6 


12:17 

Playing with the formula for variance of a population. 
7 


12:04 

Introduction to random variables and probability distribution functions. 
8 


10:02 

Probability density functions for continuous random variables. 
9 


12:16 

Introduction to the binomial distribution 
10 


11:05 

More on the binomial distribution 
11 


13:26 

Basketball binomial distribution 
12 


10:46 

Using Excel to visualize the basketball binomial distribution 
13 


14:53 

Expected value of a random variable 
14 


16:55 

Expected value of a binomial distributed random variable 
15 


11:01 

Introduction to Poisson Processes and the Poisson Distribution. 
16 


12:41 

More of the derivation of the Poisson Distribution. 
17 


08:59 

Introduction to the law of large numbers 
18 


26:04 

(Long26 minutes) Presentation on spreadsheet to show that the normal distribution approximates the binomial distribution for a large number of trials. 
19 


26:24 

Exploring the normal distribution 
20 


10:53 

Discussion of how "normal" a distribution might be 
21 


07:48 

Zscore practice 
22 


10:25 

Using the empirical rule (or 689599.7 rule) to estimate probabilities for normal distributions 
23 


08:16 

Using the Empirical Rule with a standard normal distribution 
24 


05:57 

More Empirical Rule and Zscore practice 
25 


09:49 

Introduction to the central limit theorem and the sampling distribution of the mean 
26 


10:52 

The central limit theorem and the sampling distribution of the sample mean 
27 


13:20 

More on the Central Limit Theorem and the Sampling Distribution of the Sample Mean 
28 


15:15 

Standard Error of the Mean (a.k.a. the standard deviation of the sampling distribution of the sample mean!) 
Full curriculum

Statistics  good
Course was good and instructor was also good , but some topics are missing like correlation regression and kindly include some real world example to demonstrate topics will be really good
Excellent Coverage
The number of topics covered in this course is significant & the examples are clear.
The videos are incredible, great detail and in depth explanations, thank you very much you have made learning a joy for me.
Statistics
Good course. Well presented and well explained. Clear and sensible.