Here you can find what has been done in the lectures, and from time to time also what is planned for upcoming lectures.
Chapter 12.4 Linear contrasts. t-test for linear contrasts. Bonferroni multiple-comparisons procedure. Unusual observations.
Chapter 12.1-12.4 One-way Analysis of variance (ANOVA). Sum of squares. F-test for ANOVA. t-test for comparison of pairs of means.
Chapter 11.11 - 11.12 Partial correlation. Multiple correlation. Spearman rank-correlation. t-test for Spearman rank-correlation.
Chapter 11.9 Model selection. Choosing variables in a model. Backward elimination. Forward selection. Akaike Information Criterion (AIC)
Chapter 11.9 Multiple regression. t-test for multiple linear regression. Effects of adding and dropping variables. Geometry of linear regression.
Chapter 11.9 Multiple regression. Partial-regression coefficient. Standardized regression coefficient. F test for multiple linear regression.
R file 090413_examples.R R commands for doing multiple linear regression.
R file bloodpressure.txt Data set
Chapter 11.7 - 11.8 (Pearson) Correlation coefficient. t-test for the correlation coefficient. Fisher's z-transformation. Normal test for the correlation coefficient. Confidence intervals for the correlation coefficient.
R file 090410_examples.R R commands for working with correlation coefficient.
Article Storks deliver babies (p = 0.0008) A cautionary tale about correlation and causation.
Chapter 11.4 - 11.5 Standard errors of regression parameters. t-test for linear regression. Confidence intervals for regression parameters. Standard errors and confidence intervals for predicted values and their means.
Chapter 11.4 Residual component. Regression component. Sum of squares (total, regression, residual). Mean squares (regression, residual). F-test for linear regression. R2.
R file 090406_examples.R R commands for doing the F test for linear regression.
Chapter 11.2 - 11.3 Linear regression. Regression line. Intercept. Slope. Error term (residuals). Method of Least squares. Estimated Least-Squares line. Prediction.
R file 090403_examples.R R commands for carrying out the method of least squares.
R file estriol.txt Data set
Introduction to regression analysis and analysis of variance (ANOVA).
R file 090401_lecture.R
R file cropyield.txt Data set
R file mathability.txt Data set
Chapter 10.8 - 11.1 The Kappa statistic. Introduction to regression analysis and analysis of variance (ANOVA). Information about the project.
Chapter 10.7 - 10.8 Chi-square goodness-of-fit test. The Kappa statistic.
Chapter 10.4 - 10.6 McNemar's test. Normal theory approximate test. Exact test. Sample size estimation and power for binomial proportion tests (self study, pp. 416-426). Chi-square test for trends in proportions.
Chapter 10.3 - 10.4 Fisher's exact test. Calculation of p-value for two sided Fisher tests. McNemar's test for paired binomial samples. Concordant pairs. Discordant pairs.
Chapter 10.6, 10.3 Chi-square test for RxC contingency tables. Fisher's exact test for 2x2 contingency tables. Hypergeometric distribution.
Chapter 10.1 - 10.2 Categorical data. Two-sample test for binomial proportions. Normal theory (self-study, pp. 387-390). Contingency tables. Observed table. Expected table. Yates continuity correction. Chi-square test for a 2x2 contingency table.
R file 090318_examples.R R commands for doing hypothesis tests on contingency tables.
Chapter 9.3 - 9.4 The Wilcoxon Signed-Rank test. Rank sum. The Wilcoxon Rank-Sum test.
Chapter 9.1 - 9.2 Cardinal data. Interval scale. Ratio scale. Ordinal data. Nominal data. The Sign test.
Chapter 8.6, 8.10 Test for equality of two variances. The F-distribution. Power and sample size estimation for comparing two samples.
Chapter 8.3 - 8.5, 8.7 Confidence intervals for paired data. Two sample test for independent samples with equal variances. Pooled variances. Two sample test for independent samples with unequal variances. Welch-Satterthwaite approximation.
R file 090302_examples.R R commands for doing two sample hypothesis tests.
Chapter 7.11, 8.1 - 8.2 Standardized mortality ration (SMR). Two sample hypothesis tests. Longitudinal studies. Cross-sectional studies. Paired t-tests.
Chapter 7.10 - 7.11 Power of the binomial test. Sample size estimation for binomial tests. One sample test for the Poisson distribution.
Chapter 7.7, 7.9 - 7.10 The relationship between hypothesis testing and confidence intervals. One sample test for the variance of a normal distribution. One sample test for a binomial proportion (normal approximation and exact methods).
In-class exam Chapters 1 - 6, 7.1 - 7.5.
Chapter 7.6 Determining sample size for hypothesis tests.
R file 090218_examples.R R commands for determining the sample size of hypothesis tests.
Chapter 7.5 The power of a hypothesis test.
R file 090216_examples.R R commands for calculating the power of hypothesis tests.
Chapter 7.4 Two-sided, one-sample hypothesis test for the mean of a normal distribution. Hypothesis tests for the mean of a normal distribution with know variance.
Chapter 7.3 Acceptance region. Rejection region. p-value. Statistical significance.
R file 090211_examples.R R commands for doing hypothesis tests.
Chapter 7.2 - 7.3 Type I error. Type II error. Significance level. Power of hypothesis test. One-sided, one-sample hypothesis test for the mean of a normal distribution. Critical value.
Chapter 6.9 - 6.10, 7.1 - 7.2 Estimation for the Poisson distribution. One-sided confidence intervals. Hypothesis testing. Null hypothesis. Alternative hypothesis.
Chapter 6.7 - 6.8 Interval estimates for the variance. Chi-squared distribution. Estimation for the binomial distribution.
R file 090204_examples.R R commands for estimating confidence intervals.
Chapter 6.5 - 6.7 Estimators for mean and variance. Unbiased estimators. Standard error of the mean (sem). Confidence intervals. Student t distribution. Interval estimates for the mean.
R file 090202_examples.R R commands for demonstrating estimators.
R file 090202_functions.R R functions used for the examples.
R file birthweights.txt A (made up) population of birth weights used for the examples.
Chapter 6.1 - 6.4 Random sample. Randomized clinical trial. Ways of choosing a random sample.
R file 090130_examples.R R commands for selecting random samples.
Chapter 5.6 - 5.8 Linear combinations of random variables. Covariance. Correlation. Normal approximation of Binomial and Poisson random variables.
Chapter 4.6 - 4.13, 5.2 - 5.5 Cumulative distribution function. Binomial distribution. Poisson distribution. Normal distribution.
R file 090126_examples.R R commands for dealing with probability distributions.
Chapter 3.7 - 3.10, 4.1 - 4.5, 5.1 - 5.2 Bayes' rule. ROC curves. Prevalence. Incidence. Random variables. Probability mass function. Probability density function. Expected value. Variance.
Chapter 3.1 - 3.7 Basic probability. Relative risk. Predictive value positive. Predictive value negative. Sensitivity. Specificity.
Martin Luther King's day No class.
Chapter 2 Quick review of basic descriptive statistics.
R file 090116_examples.R R commands for doing basic descriptive statistics.
Introduction to R (please bring your laptop with R installed).
R file 090114_lecture.R
Introduction to the course. A simple first example: Are men taller than women?
Data file heights.txt