This blog provided over 150 math video tutorials. There is a complete set for beginning algebra and brief calculus. There will soon be a complete set for trigonometry. The video are organized by category and are also searchable. All videos are hosted on YouTube and are less the 10 minutes each.
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Students can expect to learn how to discuss basic ideas of linear regression and correlation; create and interpret a line of best fit; and calculate and interpret the correlation coefficient and outliers.
Students can expect to learn how to interpret the chi-square probability distribution as the sample size changes; conduct and interpret chi-square goodness-of-fit hypothesis tests; conduct and intepret chi-square test of independence tests; and conduct and interpret chi-square single variance hypothesis tests.
VIDEO LECTURE
Students can expect to learn how to classify hypothesis tests by type; conduct and interpret hypothesis tests for two population means with standard deviations that are both known and unknown; conduct and interpret hypothesis tests for two population proportions; and conduct and interpret hypothesis tests for matched or paired samples.
VIDEO LECTURE
Students can expect to learn how to differentiate between Type I and Type II Errors; to conduct and interpret hypothesis tests for single population means, known and unknown population standard deviation, and also proportions.
VIDEO LECTURE
Students can expect to learn how to calculate and interpret confidence intervals for one populations average and one population proportion; interpret the student-t probability distribution as the sample size changes; and discriminate between problems applying the normal and then student-t distributions.
VIDEO LECTURE
In this chapter, students can expect to learn how to recognize the Central Limit Theorem problems; classify continuous word problems by their distributions, and apply and interpret the Central Limit Theorem for Averages and the Central Limit Theorem for Sums.
VIDEO LECTURE
Students can expect to learn how to recognize the normal probability distribution and the standard normal probability distribution and apply each appropriately, and compare normal probabilities by converting to the standard normal distribution.