Chapter 4.1: Scatter Diagrams and Linear Correlation - Healthcare Perspective
Channel: Monika Wahi
Date: 2015-10-12

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In this video, Monika Wahi lectures to Laboure College students in her Essentials of Statistics class. 
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I listed the links I mention below as they come up in the lecture. Enjoy!

00:14 Learning objectives for lecture
00:38 Topics covered in the lecture
01:06 Introduction to scatter grams (or scatter plots)
01:20 Discussion of x-axis: Independent (explanatory) and dependent (response) variables in the x,y pairs
02:48 Trick to remembering which axis is y and which is x
03:20 Trick to remembering that x is the hypothesized cause of y (and not the other way around)
04:20 Presentation of example set of x,y pairs we are going to put on the scattergram
05:24 Placing points on our scatter gram
06:47 Description of the concept of linear correlation. Example of perfect linear correlation from algebra.
07:30 Example of using a scatterplot to diagnose a problem with data: liver weight vs. total weight of patient
08:42 Example of a scatter plot depicting positive (or direct) correlation, negative (or inverse) correlation, and no correlation
12:11 Introduction to two attributes of correlation: Strength and direction
12:38 Explanation of strength of correlation
13:18 Visual example of a strong negative and positive correlation in a scatter plot
13:48 Visual example of a moderate and weak positive correlation in a scatter plot
14:38 Problems with outliers having an outsized influence in correlation, and using the scatter plot to diagnose them
15:31 Introduction to correlation coefficient r
16:17 Trick to remembering that r is the correlation coefficient
16:38 Discussion of sample vs. population correlation coefficient
17:13 Explanation of r as a numerical expression of correlation seen on a scatter plot. We will demonstrate the computational formula.
18:04 Explanation of how to interpret r, and how 1.0 = perfect positive correlation, and -1.0 = perfect negative correlation
19:57 Visual examples of various negative r’s, and recommended cutpoints for negative r for weak, moderate, and strong. Link to article, “Evolutionary principles of modular gene regulation in yeasts” with the original scatter plots: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3687341/
22:21 Visual examples of positive r’s, and recommended cutpoints for positive r for weak, moderate, and strong. Link to article “Obesity is associated with macrophage accumulation in adipose tissue” with the original scatter plots: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC296995/
23:38 Presentation of the computational formula for r, and review of approach we used to calculate variance and standard deviation.
24:27 Presentation of scenario behind the example computation of r
25:41 Presentation of blank r computation table with just the x and y filled in.
27:01 Difference between sum of x, sum of y, and sum of xy
27:44 Difference between sum of x squared depending upon where the parentheses are placed in the equation
29:18 Breakdown of terms in the computational r formula – how to use the table to calculate them and fill them in.
31:56 Filling in the equation from the table, and calculating and interpreting r.
35:31 Facts and attributes of r
38:36 Beware of lurking variables – correlation is not necessarily causation 
39:58 Example of how a lurking variable causes both the independent and dependent variable
42:32 Review and conclusion

Chapter 4.1: Scatter Diagrams and Linear Correlation - Healthcare Perspective