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Statistics is one of the most important areas of Math to understand. It has applications in science, engineering, business, economics, political science, and more.

In this 5 Hour Course, Jason Gibson teaches the fundamental concepts needed to truly Master Statistics with step-by-step video tutorials.

The lessons begin by studying the concept of a sampling distribution. We give several examples so that the student has a solid concept of sampling.

Next, we explore the central limit theorem of statistics in detail and solve several problems where the central limit theorem is needed to arrive at the answer.

Finally, we will spend considerable time the concept of a confidence interval and how to apply it to estimating the mean of a population parameter. We examine two central cases that depend on the number of samples that we have in our study and present how to calculate the confidence interval in either case. Statistics is a difficult subject for most students, but anyone can Master Statistics with our step-by-step teaching style!

Disk 1
Sect 1: Sampling Distributions
Sect 2: Central Limit Theorem, Part 1
Sect 3: Central Limit Theorem, Part 2
Sect 4: Apply Central Limit Theorem to Population Means, Part 1
Sect 5: Apply Central Limit Theorem to Population Means, Part 2
Sect 6: Apply Central Limit Theorem to Population Means, Part 3
Sect 7: Apply Central Limit Theorem to Population Proportions, Part 1
Sect 8: Apply Central Limit Theorem to Population Proportions, Part 2

Disk 2
Sect 9: Confidence Intervals for Population Means, Part 1
Sect 10: Confidence Intervals for Population means, Part 2
Sect 11: Estimating Population Means (Large Samples), Part 1
Sect 12: Estimating Population Means (Large Samples), Part 2
Sect 13: Estimating Population Means (Large Samples), Part 3
Sect 14: The Student t-Distribution
Sect 15: Using Student t-Distribution Statistical Tables
Sect 16: Estimating Population Means (Small Samples), Part 1
Sect 17: Estimating Population Means (Small Samples), Part 2
Sect 18: Estimating Population Means (Small Samples), Part 3