**What we addressed in this unit**

- Exploring data – looking at things to see what of interest might strike your eye
- Bar charts, histograms, pie charts … each shows something different about the data.
- Differentiating between plots applicable for one variable vs. plots app
- "Weighting" scores to come up with a ranking system
- Comparing weighting schemes to see how closely related they are
- Association between variables
- "Seeing" it, and comparisons thereof
- Presentations of data (e.g., dotplots y by x, side-by-side box plots, scatter plots)
- Issues of consistency of scale (percents of largest score, ranks, z-scores)
- Measuring association
- Sums of differences between ranks
- Sums of absolute differences between ranks
- Sums of products of ranks
- Sums of products of scores
- Sums of products of z-scores
- Relative association
- Comparing measures of association between data sets having different numbers of cases
- Divide by maximum possible sum for that number of cases – no matter what the sum is summing.
- Project – predicting reputation scores for colleges that haven't been given one.
- Find formula of combinations that yield highest possible correlation for colleges having reputation scores using data common to all colleges
- Apply that formula to all colleges