1. Regression & Correlation
Analysis of Biological Data/Biometrics
Dr. Ryan McEwan
Department of Biology
University of Dayton

2. Correlation is a form of analysis that tests for a relationship between two factors.
In correlation you are NOT assuming that one causes the other, just that they are related.
Thus, there is no predictor and response

3. You would use a correlation analysis if you are not making assumptions about one factor driving another.
Pearson correlation for normally distributed data
Spearman (rank) correlation for non normally distributed data.

4. Simple linear regression is a standard technique in the Analysis of Biological Data:
The main idea is assessing the relationship between two variables, assuming that the relationship is direction and linear…and assuming that one variable is a driver of the relationship.
The Response variable (plotted on Y) is assumed to respond in a linear relationship to changes in the Predictor variable (plotted on YX.
The reverse is not assumed in this analysis (that Y drives X). Think heart rate and exercise.
Other examples?

5. But if you have a cloud of points…where do you put the line?

6. Best fit lines & “Least Squares” regression
The idea is to drive the line through the cloud in the area that minimizes the distance between the points and the line.

7. Regression residuals
You can generate a table of residuals..
a new data set!
How much does each point deviate from the
regression line?

8. Detrending…
a scientific siren song

9. Regression lines can have varying slopes from a single Y intercept.

10. Regression lines can have identical slopes, but different Y intercepts.

12. We will be running a test of this sort in R
We will be running a test of this sort in R. The thing I want to you to understand is that the statistical test…. The P-value generated… relates to the null hypothesis of NO SLOPE. That the line is indeed flat. That would mean the response variable is NOT changing in relation to the predictor.

14. …ruut row…

15. IMPORTANT! The P-value from a regression, tells you whether the line is statistically flat….it does not tell you how much variation is captured!

17. It may be more useful to calculate a confidence interval

18. You might wish to have replicate values

19. Your relationship might not be linear!
Polynomial Regression

20. Regression Diagnostics!
A stepwise process of adding factors to the regression. Testing P value, r2, etc.
If you are going to take this on, you need to grind! Read, analyze, read some more

21. Caution 1: Correlation is not causation!

22. Caution 2: Extrapolation is dangerous!!

23. Logistic regression:
To be used if your data are categorical……

24. Regression & Correlation
Analysis of Biological Data/Biometrics
Dr. Ryan McEwan
Department of Biology
University of Dayton