Khan Academy: Sampling Distribution: Sampling Distribution of the Sample Mean 2

So the mean is not always the best center? By working through this exploratory activity, the class comes to realize that depending upon the shape of a distribution, different centers should be chosen. Learners continue to explore...

Get your learners ready for distributing with this two-page worksheet full of problems to simplify. The problems include positive and negative numbers that test integer skills, as well as the ability to simplify expressions. Answer key...

Video reviews the central limit theorem and uses a simulation to illustrate sampling distributions and how they approach a normal distribution with larger sample sizes even if the original distribution was not normal.

Video first briefly reviews the effect on the sampling distribution when the sample size increases then gives the formula for variance of the sample mean of the sampling distribution and formula for standard error of the mean.

Video shows working a problem from the IIT JEE test which involves algebraic manipulation and using what the argument of complex numbers is to visually reason if two given equations about arguments are true. [7:26]

Video showing how to paramatrize a torus using two parameters by first visually showing the parameters without using equations. [19:02]

Part 2 of a 3-part video series showing how to calculate the surface integral of a torus. Video continues to find the surface area of the torus by calculating a cross product. [10:29]

Normal distributions come up time and time again in statistics. A normal distribution has some interesting properties: it has a bell shape, the mean and median are equal, and 68% of the data falls within 1 standard deviation.

(Long-26 minutes) Presentation on spreadsheet to show that the normal distribution approximates the binomial distribution for a large number of trials.

Using the empirical rule (or 68-95-99.7 rule) to estimate probabilities for normal distributions.