
Lecture 1 

12:35 

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

Lecture 2 

06:42 

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

Lecture 3 

12:23 

Variance of a population. 

Lecture 4 

11:18 

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

Lecture 5 

13:07 

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

Lecture 6 

12:17 

Playing with the formula for variance of a population. 

Lecture 7 

12:04 

Introduction to random variables and probability distribution functions. 

Lecture 8 

10:02 

Probability density functions for continuous random variables. 

Lecture 9 

12:16 

Introduction to the binomial distribution 

Lecture 10 

11:05 

More on the binomial distribution 

Lecture 11 

13:26 

Basketball binomial distribution 

Lecture 12 

10:46 

Using Excel to visualize the basketball binomial distribution 

Lecture 13 

14:53 

Expected value of a random variable 

Lecture 14 

16:55 

Expected value of a binomial distributed random variable 

Lecture 15 

11:01 

Introduction to Poisson Processes and the Poisson Distribution. 

Lecture 16 

12:41 

More of the derivation of the Poisson Distribution. 

Lecture 17 

08:59 

Introduction to the law of large numbers 

Lecture 18 

26:04 

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

Lecture 19 

26:24 

Exploring the normal distribution 

Lecture 20 

10:53 

Discussion of how "normal" a distribution might be 

Lecture 21 

07:48 

Zscore practice 

Lecture 22 

10:25 

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

Lecture 23 

08:16 

Using the Empirical Rule with a standard normal distribution 

Lecture 24 

05:57 

More Empirical Rule and Zscore practice 

Lecture 25 

09:49 

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

Lecture 26 

10:52 

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

Lecture 27 

13:20 

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

Lecture 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.