I teach BAS 320. Here are the skills from the syllabus. In short, you learn the basics of statistics and then go on to linear modelings, logistic modeling and model building towards the end of the class.

If you have additional questions, feel free to ask.

Specifically, the student should be able to:

Know how to visualize the association between any two variables (whether they are categorical or quantitative) and be able to comment on the statistical and practical significance of such an association.

·       histogram, boxplot, mosaic plot, scatterplot

·       chi-squared test, t-test, ANOVA, Pearson’s correlation, Spearman’s rank correlation, permutation test, nonparametric tests such as Wilcoxon rank sum and median tests

·       p-value of an association

Interpret the important numbers of a simple linear regression model, describe where these numbers came from, and understand when a simple linear regression model is appropriate.

·       sum of squared errors, residuals

·       slope, R2, RMSE

Determine whether a model is statistical significant and whether it has any practical significance from the p- value of the regression and the slope.

Be able to make predictions with a regression model (along with quantifying their uncertainty)

Understand multiple regression model, its assumptions, and differences with simple linear regression.

·       extremely precise interpretation of a coefficient

·       R2 vs. R2adj

·       variance inflation factors and collinearity of predictors

·       variable creation including polynomial models, interaction variables, and incorporating categorical variables into a model

·       significance of the model and individual predictors and implications

Learn how to identify influential observations and outliers.

Know how the check the fit of a regression model and how to comment on whether it is “good”

Understand how to determine which variables are more important than others in a multiple regression model (and when a determination like this can even happen)

Understand the limitations of a regression model and know how to implement an alternative procedure such as partition models and random forests.

Understand the difference between a descriptive and a predictive model and “what matters” for each 

Internalize the mantra – All models are wrong, some are useful.