The outline below lists all the lectures for Section 3 of the Aspire Mountain Academy course in elementary statistics.  Once the lecture video becomes available, the title will become a link to the video and a brief description will appear below the title.

Section 3 - Describing and Comparing Data

Section 3.1 Measures of Center

Lecture Video 3.1.1: Arithmetic Mean

This mini-lecture introduces what most people call the average as the arithmetic mean and explores the advantages and disadvantages of using the mean as a measure of center.

Lecture Video 3.1.2: The Median and the Mode

This mini-lecture describes the median and the mode and compares their use with the mean as measures of center.

Lecture Video 3.1.3: The Midrange

This mini-lecture introduces the midrange, describes how to calculate it, and compares its use with other measures of center.

Lecture Video 3.1.4: Calculating Measures of Center in StatCrunch

This mini-lecture demonstrates how to calculate measures of center in StatCrunch.

Lecture Video 3.1.5: Interpreting Measures of Center

This mini-lecture explains how the values of measures of center translate into the "real world" and why some values are meaningless in certain situations.

Lecture Video 3.1.6: Frequency Distribution Means

This mini-lecture demonstrates how to calculate means for frequency distributions using the "old school" method as well as in StatCrunch.

Lecture Video 3.1.6: Weighted Means

This mini-lecture defines a weighted mean and demonstrates how to calculate it using the "old school" method as well as in StatCrunch.

Section 3.2 Measures of Variation

Lecture Video 3.2.1: Standard Deviation

This mini-lecture defines the standard deviation for samples and populations and demonstrates how to calculate them using the "old school" method as well as in StatCrunch.

Lecture Video 3.2.2: Range and the Range Rule of Thumb

This mini-lecture defines the range as a measure of variation, describes its place in the Range Rule or Thumb, and shows how to apply the Range Rule of Thumb to solve problems.

Lecture Video 3.2.3: Range and the Range Rule of Thumb: Exercises

This mini-lecture provides additional practice applying the Range Rule of Thumb to solve problems.

Lecture Video 3.2.4: Variance

This mini-lecture introduces the variance as a measure of variation, describes its relationship with the standard deviation, and shows how to calculate it using StatCrunch.

Lecture Video 3.2.5: The Empirical Rule & Chebyshev's Theorem

This mini-lecture describes the Empirical Rule and Chebyshev's Theorem and shows how to apply both in solving problems.

Lecture Video 3.2.6: The Coefficient of Variation

This mini-lecture defines the coefficient of variation and shows how both to calculate it and interpret it in solving problems.

Section 3.3 Measures of Relative Standing

Lecture Video 3.3.1: Z-scores

This mini-lecture defines the z score, describes how to calculate it, and shows its usefulness as a measure of relative standing.

Lecture Video 3.3.2: Z-scores - Exercises

This mini-lecture provides additional practice using z scores to solve problems.

Lecture Video 3.3.3: Percentiles

This mini-lecture describes percentiles and demonstrates how to calculate them using both the "old school" method and StatCrunch, including a brief introduction to sorting data in StatCrunch.

Lecture Video 3.3.4: Quartiles

This mini-lecture explains what quartiles are and then uses example problems to demonstrate how to find percentiles and quartiles.  It also includes an in-depth primer about sorting data in StatCrunch as well as a tutorial on finding the data value that corresponds to a given percentile.

Lecture Video 3.3.5: Five-Number Summaries

This mini-lecture defines the five-number summary and provides a tutorial on how to calculate them in StatCrunch.

Lecture Video 3.3.6: Boxplots

This mini-lecture describes boxplots, including the use of outliers in modified boxplots, and shows how to construct boxplots in StatCrunch.  The StatCrunch tutorial also shows how modify the presentation of your boxplot (or other graphical representation) to match better the answer options in multiple choice questions.