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© 2014 Pacific Crest
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10. How would you reduce the confusion that many have with respect to correlation and causation?
A
Successful Performance
Successful application of your learning looks like this
As you begin to apply what you’ve learned, you should have a good idea of what success looks like.
A SUCCESSFUL
PERFORMANCE
When given two variables, I determine the linear regression equation. I...
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Ensure that the linear regression equation is valid
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Achieve accuracy in the correlation coefficient, slope, and the y-intercept
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Use the equation for prediction only when appropriate
D
emonstrate Your Understanding
Apply it and show you know in context!
1. Imagine pairs of data that represent the high temperature for a given day followed by the low tem-
perature for that same day. Imagine this data set consists of 365 pairs (one for each day of the year).
a. Would the correlation be positive or negative?
b. What is the expected correlation?
c. Does the correlation change based upon location?
d. Explain why it may be inappropriate to use correlations/regression methods for this type of data
set.
2. Imagine you are given the high temperatures for 50 cities on a given day. The first data point is the
temperature in degrees Celsius and the second is in degrees Fahrenheit.
a. What is the correlation coefficient for this data set?
b. Without performing any computations, what is the linear regression equation?
c. Is causation reasonable in this context?
7.4 Simple Linear Regression