The outline below lists all the lectures for Section 2 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 2 - Exploring Data with Tables and Graphs

Section 2.1  Frequency Distributions

Lecture Video 2.1.1: Frequency Distribution Basics

This mini-lecture introduces the concept of a frequency distribution and defines the different basic characteristics of frequency distributions, including lower and upper class limits, class boundaries, class midpoints, and class width.

Lecture Video 2.1.2: Frequency Distribution Basics – Exercises

This mini-lecture provides additional practice determining the value of the different characteristics of frequency distributions.

Lecture Video 2.1.3: Creating Frequency Distributions

This mini-lecture explains how to create frequency distributions, with emphasis on the "old school" method.

Lecture Video 2.1.4: Creating Frequency Distributions in StatCrunch

This mini-lecture explains how to create frequency distribution, with emphasis on using StatCrunch.

Lecture Video 2.1.5: Using Frequency Distributions

This mini-lecture explains how aspects like distribution shape and gaps can reveal information about the data underlying the frequency distribution.

Lecture Video 2.1.6: Converting Frequency Distributions in StatCrunch

This mini-lecture demonstrates how to convert frequency distributions in StatCrunch from a tabular format into a graphical format.

Lecture Video 2.1.7: Relative Frequency Distributions

This mini-lecture defines relative frequency distributions and demonstrates how to produce them in StatCrunch.

Lecture Video 2.1.8: Cumulative Frequency Distributions

This mini-lecture defines cumulative frequency distributions and demonstrates how to produce them in StatCrunch.

Section 2.2  Histograms

Lecture Video 2.2.1: Histogram Basics

This mini-lecture introduces the concepts of histogram and relative frequency histograms before concluding with a demonstration of how to create histograms in StatCrunch.

Lecture Video 2.2.2: Interpreting Histograms

This mini-lecture describes how features such as distribution shape, gaps, bin width, and skewness can reveal information about the data underlying the distribution represented by a histogram.  It also shows how to adjust the bin width for histograms in StatCrunch.

Lecture Video 2.2.3: Assessing Normality

This mini-lecture shows how to use a normal quantile plot to assess normality in the data used to make a histogram.  It also shows to construct a normal quantile plot in StatCrunch.

Section 2.3 Graphs That Enlighten or Deceive

Lecture Video 2.3.1: Scatterplots

This mini-lecture defines scatterplots, explains how they are used, and demonstrates how to produce them in StatCrunch.

Lecture Video 2.3.2: Time-Series Graphs

This mini-lecture defines time-series graphs, explains how they are used, and demonstrates how to produce them in StatCrunch.

Lecture Video 2.3.3: Dot Plots

This mini-lecture defines dot plots, explains how they are used, and demonstrates how to produce them in StatCrunch.

Lecture Video 2.3.4: Stem (and Leaf) Plots

This mini-lecture defines stem plots (also called stem-and-leaf plots), explains how they are used, and demonstrates how to produce them in StatCrunch.

Lecture Video 2.3.5: Bar Graphs

This mini-lecture defines bar charts, explains how they are used, and demonstrates how to produce them in StatCrunch.

Lecture Video 2.3.6: Pareto Charts

This mini-lecture defines Pareto charts, explains how they are used, and demonstrates how to produce them in StatCrunch.

Lecture Video 2.3.7: Pie Charts

This mini-lecture defines pie charts, explains how they are used, and demonstrates how to produce them in StatCrunch.

Lecture Video 2.3.8: Frequency Polygons

This mini-lecture defines frequency polygons, explains how they are used, and demonstrates how to produce them in StatCrunch.

Lecture Video 2.3.9: Ogives

This mini-lecture defines ogives, explains how they are used, and demonstrates how to produce them in StatCrunch.

Lecture Video 2.3.10: Graphs That Deceive

This mini-lecture explains how some graphs deceive their audiences because of how they are constructed and explores how presenters can more effectively convey technical information.

Section 2.4 Correlation and Regression

Lecture Video 2.4.1: Correlation

This mini-lecture describes the statistical concept of correlation, with special emphasis on linear correlation.

Lecture Video 2.4.2: The Linear Correlation Coefficient

This mini-lecture introduces the linear correlation coefficient and describes how it measures the strength of a linear correlation.

Lecture Video 2.4.3: Critical R Values

This mini-lecture describes what critical R values are and how they can be used to determine whether a data set is linearly correlated.

Lecture Video 2.4.4: Critical R Values– Exercises

This mini-lecture provides practice with using critical R values in determining whether a data set is linearly correlated.

Lecture Video 2.4.5: P-values

This mini-lecture describes what P-values are and they can used in place of critical R values to determine whether a data set is linearly correlated.

Lecture Video 2.4.6: Introduction to Linear Regression

This mini-lecture introduces the student to linear regression and demonstrates the sensitivity of the linear correlation coefficient to outliers.
More video titles are on the way.  Your patience is appreciated.