# Statistics

Introduction to statistics. Will eventually cover all of the major topics in a first-year statistics course.
• Lectures 28
• Video 6 Hours
• Skill level all level
• Languages English
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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!

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### Curriculum

 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 (Long-26 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 Z-score practice Lecture 22 10:25 Using the empirical rule (or 68-95-99.7 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 Z-score 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!)

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### Reviews

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• Sudhir Singh

#### 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

• Ashok Anbalan

#### Excellent Coverage

The number of topics covered in this course is significant & the examples are clear.