| 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 |
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