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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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