STA110A: Course Overview

Below is a broad outline of topics to be covered in the first half of STA110A; I will continue to update it as we move along. I have tried to provide the chapter numbers where appropriate, but please note that some topics will be introduced in lecture which are not included in the text. For this reason, it will be necessary to keep up with assigned readings, work through homework problems, and learn the material discussed in lecture.

1. Basics of experimental design (Freedman, et al., Chp. 1-2)

Discuss concepts such as: treatment, response, controlled, randomized, placebo, double-blind, etc.
Identify the confounding variable in relevant examples.
Compare and contrast controlled experiments and observational studies.
Know the difference between association and causation.
2. Descriptive Statistics (Freedman, et al., Chp. 3-5)

Draw and interpret a histogram; understand skew, symmetric, etc.
Find the average, median, and standard deviation of a data set.
Understand the concepts of center and spread; know which types of statistics are typically used to convey these two ideas.
Know what the normal curve is and how to find areas under it.
Use the normal approximation for data.
Find and interpret percentile ranking, interquartile range, etc.
3. Correlation and regression (Freedman, et al., Chp. 8-12)

Draw and interpret scatter diagram.
Understand and compute the correlation coefficient.
Understand the concept of linear regression, and know how to use it to make predictions.
Understand the regression effect.
Calculate and use the RMS error of the regression line.
Use the residuals to diagnose possible cases in which linear regression may not be appropriate.
Find the slope and intercept of the regression line.
4. Basic probability (Freedman, et al., Chp. 13-14)

Define what is meant by the chance, or probability, of an event occurring.
Differentiate between drawing with and without replacement.
Understand what is meant by conditional probability.
Define independence; relate independence to the multiplication rule.
Define mutual exclusiviity; relate this condition to the addition rule.
Understand and apply Bayes Theorem.
5. (Freedman, et al., Chp. 13-14) Define what is meant by the chance, or probability, of an event occurring. Differentiate between drawing with and without replacement. Understand what is meant by conditional probability. Define independence; relate independence to the multiplication rule. Define mutual exclusiviity; relate this condition to the addition rule. Understand and apply Bayes Theorem.