Statistics

Introduction to statistics. Will eventually cover all of the major topics in a first-year statistics course.
Instructed by The Khan Academy

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  • Lectures 28
  • Video 6 Hours
  • Skill Level All Levels
  • Languages English
  • Includes Lifetime access
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    Available on iOS and Android

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Course Description

Introduction to statistics. Will eventually cover all of the major topics in a first-year statistics course (not there yet!)

What am I going to get from this course?

  • Over 28 lectures and 6 hours of content!

What you get with this course?

Not for you? No problem.
30 day money back guarantee.

Forever yours.
Lifetime access.

Learn on the go.
Desktop, iOS and Android.

Get rewarded.
Certificate of completion.

Curriculum

12:35
Introduction to descriptive statistics and central tendency. Ways to measure the average of a set: median, mean, mode
06:42
The difference between the mean of a sample and the mean of a population.
12:23
Variance of a population.
11:18
Using the variance of a sample to estimate the variance of a population
13:07
Review of what we've learned. Introduction to the standard deviation.
12:17
Playing with the formula for variance of a population.
12:04
Introduction to random variables and probability distribution functions.
10:02
Probability density functions for continuous random variables.
12:16
Introduction to the binomial distribution
11:05
More on the binomial distribution
13:26
Basketball binomial distribution
10:46
Using Excel to visualize the basketball binomial distribution
14:53
Expected value of a random variable
16:55
Expected value of a binomial distributed random variable
11:01
Introduction to Poisson Processes and the Poisson Distribution.
12:41
More of the derivation of the Poisson Distribution.
08:59
Introduction to the law of large numbers
26:04
(Long-26 minutes) Presentation on spreadsheet to show that the normal distribution approximates the binomial distribution for a large number of trials.
26:24
Exploring the normal distribution
10:53
Discussion of how "normal" a distribution might be
07:48
Z-score practice
10:25
Using the empirical rule (or 68-95-99.7 rule) to estimate probabilities for normal distributions
08:16
Using the Empirical Rule with a standard normal distribution
05:57
More Empirical Rule and Z-score practice
09:49
Introduction to the central limit theorem and the sampling distribution of the mean
10:52
The central limit theorem and the sampling distribution of the sample mean
13:20
More on the Central Limit Theorem and the Sampling Distribution of the Sample Mean
15:15
Standard Error of the Mean (a.k.a. the standard deviation of the sampling distribution of the sample mean!)

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