Course Schedule

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Jan. 14 - Introduction to Course, Looking at Data

• Overview of STA110C: the course goals, objectives, and logistics.
• Computing: Class home page, newsgroup, JMP-In
• Variables
• Distributions, graphing distributions

Assignment: Read Ch. 1.1.

Jan. 19, 21 – Numerical Measures of Central Tendency, Spread; Normal Distributions

• Numerical descriptors of central tendency and spread, boxplots.
• Effects of transformations on measures of central tendency and spread.
• Normal distribution, standardizing variables, percentile rankings

Assignment: Read Ch. 1.2, 1.3. HW 1 due Monday, Feb. 1 at beginning of Section.

Jan. 26, 28 - Looking at Relationships Between Two Variables: Scatterplots & Correlation

• Scatterplots
• Adding categorical variables to scatterplots
• Scatterplot smoothers
• Categorical explanatory variables
• Correlation
• Properties of correlation
• Least-squares regression line

Assignment: Read Ch. 2.1 –2.3.

Feb. 2, 4 - Looking at Relationships Between Two Variables: Regression

• Interpreting the regression line
• r² in regression
• Understanding residuals and residual plots
• Cautions about regression and correlation: outliers, regression diagnostics, lurking variables, correlations based on summary data (ecological correlations), restricted range.
• Regressions involving exponential growth.
• Relations in categorical data
• Simpson’s paradox; the perils of aggregating data

Assignment: Read Ch. 2.4 - 2.6, HW 2 due Feb. 8

Feb. 9, 11 - Looking at Relationships (continued), Systematic Empiricism and Experimental Design

• Explaining Association: Causation, confounding, common response
• Experimental versus observational designs
• Anecdotal evidence
• Experimental design
• Randomization, double-blind, control groups, confounding
• Matched pair designs
• Ecological validity (realism)

Assignment: Read Ch. 2.7, Ch. 3.1, 3.2, HW 3 due Friday, Feb. 12

Feb. 16 Exam 1

Feb. 18 – Experimental Designs

• Sampling: population vs. sample, convenience sampling (voluntary response), simple random samples, probability samples, stratified random sample, multistage samples, capture-recapture sampling (p. 275)
• Nonresponse bias, response bias
• Sampling variability, sampling distributions, biased and unbiased estimators
• Bias versus variability
• Randomness and Probability
• Frequentist and Bayesian approaches
• Sample spaces, probability models
• Rules of probability: complementarity, disjoint, independence, addition and multiplication rules, conditional probability, law of total probability, equally likely outcomes

Assignment: Read Ch. 3.3 - 3.4, 4.1, 4.2, 4.5 (through multiplication rules, p.354), HW 4 due February 22.

Feb. 23, 25 - Introduction to Inferential Statistics and Introduction to Randomness

• Random variables
• Continuous versus discrete random variables
• Means and variances of random variables
• Law of large numbers, “law of small numbers”
• Tree diagrams, decision analysis
• Bayes' rule, more Bayesian concepts, prior and posterior probabilities

Assignment: Read 4.2 - 4.4, 4.5 (beginning p. 354) and additional Bayesian readings, HW 5 due March 1.

March 2 -4- Sampling Distributions

• Sampling distribution for counts and proportions
• Binomial distribution, mean and sd of the binomial distribution
• Normal approximation to the binomial, continuity correction

• Sampling distribution of a sample mean
• Central limit theorem
• Confidence intervals

Assignment: Read Ch. 5.1, 5.2, 6.1, HW 6 due March 8.

Mar. 9, 11 – Hypothesis Testing

• Tests of Significance
• Use and abuse of significance testing
• Power, Type I, Type II errors

Assignment: Read Ch.6.2 – 6.4, HW 7 due Friday, March 12.

Mar. 23, 25 –T-tests

• t-distribution
• Single mean t-test and confidence interval
• Matched pair t-test
• Assumptions behind a t-test and robustness
• Inference for non-normal distributions: nonparametric procedures (sign test), data transformations
• Comparing two means: two-sample z and two-sample t.
• Assuming equal variances (pooled estimation) versus unequal variances (t with approximated degrees of freedom)

Assignment: Read 7.1 (skip “power of a t-test”), 7.2, HW 8 due Friday, March 26

Mar. 30 - Exam 2

April 1 – Inferences for proportions

• Inference for a single proportion: significance tests and confidence intervals
• Determining sample size for a desired margin of error
• Comparing two proportions: significance tests and confidence intervals

• Relative risk

Assignment: Read Ch. 8.1, 8.2, HW 9 due April 5.

April 6 - 8 - - Inferences for two-way tables, inferences for simple linear regression

• Describing relations in two-way tables
• Chi-square for goodness of fit and association
• Chi-square vs. z test for 2 x 2 tables

Assignment: Read Ch. 9.1, 9.2, 10.1, 10.2, HW 10 due April 12.

April 13 - 15 - Inference for multiple regression

· Statistical model for linear regression

· Estimating the regression parameters

· Interpreting regression output

Assignment: Read Ch.11 (starting p. 719, skip multiple logistic regression section, pp. 730-731), HW 11 due April 19.

April 20 – 22 – One- and Two-way ANOVA

· Statistical model for multiple linear regression

· Estimating the regression parameters

· ANOVA hypotheses

· F-distribution

· Multiple comparisons

Assignment: Read, Ch. 12 (skip contrasts section 762-769), Ch. 13, HW 12 due April 26

April 27 – Three Volunteer Project Presentations

Projects due April 28.

Final- Thursday, May 6, 2 – 5 p.m.