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
18 Sep F Linear transformations 1.3
  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
  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  
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  

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