5. Regression

Cloud of points, axes, independent and dependent variables, point of averages
SD line, regression line, regression estimate: SD line versus regression line, and their slopes
Computation of regression estimates for a dependent variable, given the value of the independent variable --- how to figure out what is required in a given situation, and how to compute it.

Residuals, prediction errors

Residual = observed value minus predicted value
Rms error = SD of residuals: a measure of the likely size of the residuals
---Measures how well the line fits the cloud of points
Computing the rms error
Figuring the normal approximation in 'vertical strips'

When to use or not use a regression estimate

YES: a football shaped scatterplot, for subjects to whom this plot is applicable
NO: Outliers, non-linear relationships, heteroscedasticity.
NO: Extrapolation beyond the values or population. Interventions.

6. Chance variation and Sampling distributions

Chance variation
The Law of Averages
Box models
Probability Histograms
Expected Values and Standard Errors for counts and sums
Normal approximation for probability histograms of counts and sums
Sampling from large populations: relating to box models.
Expected Values and Standard Errors for sample percents and sample averages

7. Statistical Inference

Confidence intervals for population averages and percents.
Interpreting confidence intervals.
Hypotheses: tests of hypotheses -- decision making
The null hypothesis (*** Don't worry about the ``alternative hypothesis'')
The P-value
z-test for value of a population average or percent.
The SE of a difference
z-test for the difference of averages or of percents.