### Math 143 Fall 1998 topics covered

 Describing Data 11 Sep F What is statistics, and can you lie with it? Introduction 14 Sep M Displaying data distributions: Histograms 1.0, 1.1, 1.2 , Averages 1.3 up to p. 59 16 Sep W Measuring variability 1.2 Percentiles Boxplots 18 Sep F Linear transformations 1.3 Standardizing The Normal (Gaussian) distribution 21 Sep M Calculating normal probabilities 1.3 Using Data Desk 23 Sep W Categorical and quantitative data 2.0, 2.1, 2.2 Looking at bivariate relationships: scatterplots Positive and negative association Linear relationships Correlation: measuring linear association 25 Sep F Response and explanatory variables 2.0, 2.3 Marginal and conditional distributions Smoothing: mean and median traces Regression as conditional mean Regression: least squares criterion Fitted values and residuals 28 Sep M The regression model 2.3 Residual plots Transforming variables: taking logs Regression in Data Desk 30 Sep W Unusual values: outliers and influential points 2.4, 157-159 Extrapolation Ecological correlations Aggregating data and Simpson’s paradox 2 Oct F Conditional distributions in regression Regression effect 5 Oct M Predicting Y from X vs. predicting X from Y Correlation and regression Displaying data visually Collecting Data 7 Oct W Correlation vs. causation 2.4, 3.0, 3.2 Lurking variables Observational studies Design of experiments 9 Oct F Randomization 3.2 Treatment and control groups Blocking and matched pairs 12 Oct M Practical issues in experiments Ethics and experimentation AIDS placebo trials in developing nations 14 Oct W review 16 Oct F Midterm 1 21 Oct W Ethics and experimentation video: Milgram's experiment on obedience 23 Oct F Population vs. samples, parameters vs. statistics 3.1 Representative sampling: quotas vs. random Simple random samples Other sampling designs 26 Oct M Biases in sampling 3.1 28 Oct W Overestimating rare events False positives and false negatives Conditional probability Describing Sampling Variability 30 Oct F Sampling distributions 4.0, 4.1 2 Nov M Bias and variability 4.1 Calculating probabilities 4 Nov W The sampling distribution of a proportion 4.3 6 Nov F Linear combinations of random variables Expected value and variance of linear combinations 9 Nov M The sampling distribution of a sample mean 4.5 Central Limit Theorem Drawing Inferences 11 Nov W Confidence intervals (s known) 5.0, 5.1 13 Nov F Interpreting confidence intervals 5.1 16 Nov M More on confidence intervals review 18 Nov W Midterm 2 20 Nov F Hypothesis testing and p-values 5.2 (omit 370-373) The one sample z-test 23 Nov M The t distribution 6.0, 6.1 The one sample t-test 25 Nov W One sample t-test: Baby boom example 6.1 30 Nov M Confidence intervals (t-intervals) 6.1 Confidence intervals and hypothesis tests 2 Dec W Type I and type II errors 5.3, 5.4 up to p390 Interpreting hypothesis tests 4 Nov F Paired comparisons 6.1 pp419-424 Comparing two means: two independent sample t-test 6.2 up to p450 7 Dec M Comparing two means, cont. 6.2 up to p450 Using Data Desk for projects 9 Dec W Comparing proportions 7.0, 7.1, 7.2 11 Dec F Projects Final exam 17 Dec 9:30