1. (a.k.a: The statistical bare minimum I should take along from STAT 101)

2. Definitions and relationships as presented on the sheet Anatomy of the Basics: Statistical Terms and Relationships Identification of variables and their characteristics Careful review of data and their presentation Providing a context for the data Why percentages and not numeric counts when making comparisons

3. General types of data collection Importance of randomization in obtaining samples Sampling Error Difference between non-probability sampling and probability sampling Different types of random samples and how each is obtained Ability to obtain samples using probability sampling approaches

4. Definitions: Permutation; Factorial; Combination. What a Factorial is and how to use it. Ability to determine the number of permutations or combinations resulting from a stated situation. Extras here: Tree diagrams & the multiplication rule.

5. Characteristics of qualitative variables. Building a qualitative frequency table. Appropriate charts/graphs for qualitative data (and how to make them).

6. Characteristics of quantitative variables. Building a quantitative frequency table. From within a quantitative frequency table, be able to identify: classes, class widths, class midpoints, class limits, boundaries (cutpoints) Identify and construct appropriate charts/graphs for quantitative data.

7. Understand what Sigma (  means and how it is used. Be able to interpret what  is telling you to do in a given formula. When you think you’ve got it, practice some more.

8. Be able to identify the characteristics of the median, mean and mode, and to which types of data each applies. Be able to calculate the median, mean and mode, as appropriate, for a set of data. Affected by vs. resistant to extreme values. What are the implications for the mean and median?.

9. Be able to explain what constitutes a distribution. Be able to identify Left, Right and Normal distributions (and a Uniform distribution). Be able to determine if a distribution is normally distributed or skewed through use of a formula or computer software and, be able to interpret the results of this process.

10. Know the types of measures used to look at variation and the type data to which they apply. Be able to calculate the range, standard deviation and inter-quartile range. Be able to determine the distance away from the mean a given value lies in terms of standard deviations (think z-score). Be able to apply the Empirical Rule and Chebychev’s Theorem to specific situations.

11. Know the types of measures used to look at specific positions within a data distribution. Be able to calculate the inter-quartile range, three quartiles, Pearson’s Index of Skewness, z-score, Coefficient of Variation. Be familiar with symmetry vs. skewness and distribution shapes. Be able to build both traditional and modified box plots (aka: box-and- whiskers plot).

12. Correlation – potential relationships, not causality. Know the steps one might employ before obtaining a correlation. Know the characteristics of the Pearson Product Moment Correlation Coefficient (for us the correlation). Be able to calculate a correlation and determine if it is statistically significant. Be able to create a scatter plot of the paired data being studied. Be able to determine the directionality of a correlation and its strength via formula and observation of plotted data.

13. Understand what the regression process does - prediction. Be able to state the steps we use leading up to the decision to conduct regression. Be able to calculate the slope of a line and the y-intercept. Be able to calculate a regression equation and apply it to the prediction of other values. Know that these are estimates, not necessarily the actual values that might occur. Know what the Least Squares Property and Line of Best Fit. Residual – what’s that?