1. Review
1. Describing variables.
2. Probability, up to sampling distributions.
3. Statistical inference. Techniques.

2. Describing Variables 1. Data tables columns = variables
independent rows
2. Population vs. sample
random variables
3. Describing one discrete variable:
frequency table = one-way table
pie charts and bar charts

3. Describing Variables 4. Describing one continuous variable:
a. measures of center
mean, median, rms, trimmed means
b. measures of spread
standard deviation, variance, IQR
c. frequency tables (with bins)
d. histograms, density curves
unimodal? skewed? normal?
e. percentiles. ogives.

4. Describing Variables 5. Describing two discrete variables:
two-way tables
independence
6. Describing two continuous variables:
scatterplots
associations and correlations
positive-negative? strong-weak? linear?
independent?
7. Standardizing variables

5. Probability – Random Variables
1. Discrete variables
mean
variance
standard deviation
2. Density curves

6. Probability – Sampling Distributions
1. Sampling distribution of a sample mean
2. Sampling distribution of the z statistic
3. Sampling distribution of the t statistic
4. Sampling distribution of a sample proportion
5. Sampling distribution of the chi-square statistic

7. Inference - Techniques
1. Standard errors
mean
proportion
2. Critical values
3. Confidence intervals
4. Hypothesis tests
mean – compare to conjectured value
proportion – compare to conjectured value
difference of means
difference of proportions
one-way tables: do the data match the theory?
two-way tables: are the variables independent?

8. Regressions
The pictures
The least-squares principle
Diagnostics
s standard error (of the residuals)
R2 -- how much variance is “explained”
standard errors of the coefficients