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Stats lectures that help you learn (and smile a bit as well)

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

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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.
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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.
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Lecture Video 2.1.3: Creating Frequency Distributions

This mini-lecture explains how to create frequency distributions, with emphasis on the "old school" method.
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Lecture Video 2.1.4: Creating Frequency Distributions in StatCrunch

This mini-lecture explains how to create frequency distribution, with emphasis on using StatCrunch.
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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.
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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.
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Lecture Video 2.1.7: Relative Frequency Distributions

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

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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.
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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.
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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

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Lecture Video 2.3.1: Scatterplots

This mini-lecture defines scatterplots, explains how they are used, and demonstrates how to produce them in StatCrunch.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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Lecture Video 2.3.9: Ogives

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

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Lecture Video 2.4.1: Correlation

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

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