| unit | date | topics | class datasets | hw datasets |
| Introduction | 5 Feb F | Welcome | ||
| Confirmatory vs. Exploratory statistics | Mammalian brains | |||
| Logistics | ||||
| 8 Feb M | Principles | |||
| Confirmatory vs. Exploratory statistics | Mammalian brains | |||
| Pattern vs. Deviation | ||||
| Presentation vs. Discovery | Who makes lunch? | |||
| Statistics | Sampling distributions | |||
| review | Population vs. samples | |||
| parameters vs. statistics | ||||
| Sampling distributions | Larry, Mary… | Used cars | ||
| 10 Feb W | Sampling distributions, cont'd | Unemployment rate | ||
| The expected value and variance | Men's weights | |||
| Hypothesis tests | ||||
| Hypothesis testing and p-values | ESP | CD mastering | ||
| 12 Feb F | Testing and confidence intervals | |||
| The one sample t-test | Body temperature | |||
| Confidence intervals (t) for the mean | ||||
| Intervals and tests | ||||
| 15 Feb M | Lab assignment: Data Desk tutorial | |||
| The Data Desk interface and principles | Forbes 500 companies | |||
| Univariate | 17 Feb W | Extending the histogram | ||
| displays | Histograms and choice of settings | Tipping | CEO golf handicaps | |
| Density estimation: basic ideas | Web times | |||
| 22 Feb M | Density estimation | |||
| Adaptive and nearest-neighbor methods | Web times | |||
| Testing for multiple modes | ||||
| 26 Feb F | Displaying distributions | |||
| Stem-and-leaf plots | Mantle and Mays | |||
| Principles: Multifunctioning plot elements | ||||
| Principles: Display vs. Reduction | ||||
| Quantiles and percentiles | ||||
| Boxplots | Salaries by majors | |||
| 1 Mar M | Comparing distributions | |||
| Order statistics | CEO golf handicaps, | |||
| Normal probability plots | stock ratings | Simulated NPP's | ||
| 3 Mar W | Comparing distributions | Math 143 survey | ||
| Q-Q plots for two datasets | GRE scores | GRE scores | ||
| Displaying categorical data | ||||
| Pie charts and bar charts | ||||
| 8 Mar M | Displaying categorical data | Education by state | ||
| Triaxial plots [Jorge Calvo] | British elections | |||
| 10 Mar W | Displaying categorical data | |||
| Jittering | Titanic survivors | Siskel and Ebert | ||
| Plot symbols | ||||
| Bivariate | 12 Mar F | Smoothing | ||
| displays | Running medians: 4253H,twice | Cow temperatures | ||
| 15 Mar M | Smoothing | |||
| Lowess | Cow temperatures | |||
| 17 Mar W | Time series | Investment dartboard | Baseball standings | |
| Time series and smoothing | Carbon dioxide | |||
| Banking to 45 | Sunspots, Canadian lynx | |||
| 19 Mar F | Time series | |||
| The parallel lines optical illusion | Investment dartboard | |||
| Cory, Patty: Siskel and Ebert | ||||
| 5 Apr M | The regression model | |||
| Regression as least squares line | US cities | Chromatography | ||
| Regression as conditional mean | ||||
| The simple linear regression model | ||||
| 7 Apr W | Assessing the regression model | |||
| Residual plots | US cities | |||
| Correlation and R-squared | Pizza prices | |||
| Conditional SD | Girls' heights | |||
| 9 Apr F | Time Series | |||
| Kate, Tom: Baseball standings | ||||
| 12 Apr M | The regression model | |||
| Inference for regression coefficients | ||||
| The need for graphics | Anscombe data | |||
| Regression: an example | 100 meters | |||
| 14 Apr W | Transformations | |||
| When data don't fit the model | Math 143 survey | |||
| Transforming right-skewed distributions | Medicare | |||
| Logs and exponential growth | US population | |||
| 16 Apr F | Transformations | Mammalian brains | ||
| Theoretical models | Forbes 500 companies | |||
| More on log transformations | Tree volumes | |||
| Power transformations | Gas mileage | |||
| 19 Apr M | Regression and residuals | |||
| Arjuna, Sarah: Chromatography | ||||
| 21 Apr W | Multiple regression | |||
| The multiple regression model | Heptathlon | Airplane prices | ||
| Geometry: fitting planes | ||||
| Building a multiple regression model | ||||
| 23 Apr F | Multiple regression | |||
| Collinearity | Heptathlon | |||
| Model building | ||||
| Interpreting regression coefficients | ||||
| Indicator variables | ||||
| 26 Apr M | Multiple regression | |||
| Partial regression plots | Galapagos species | |||
| Model building: an example | ozone hole | |||
| Outliers | ||||
| Multivariate | 30 Apr F | Projection-based methods | ||
| displays | 3D rotating plots | Companies | Colleges | |
| Scatterplot matrices: brushing/slicing | Random numbers | |||
| Trellis displays (coplots) | NYC ozone | |||
| 3 May M | Parallel axis-based methods | |||
| Parallel axis plots | Cars | |||
| The data image | ||||
| Finding clusters | ||||
| 5 May W | Icon-based methods | |||
| Color, plot symbols | Companies | |||
| Hugo: Airplane prices | ||||
| 7 May F | Icon-based methods | |||
| Classification and clustering | Cars | |||
| Stars, profiles | ||||
| Chernoff faces | ||||
| Large datasets [Matt Hutcheson] | ||||
| Collecting large datasets | Non-white US population | |||
| Bandwidth-location plots | ||||
| Animating trends over time | ||||
| Graphical | 10 May M | Graphical and information density | ||
| principles | Data density | examples from ads, | ||
| Data-ink ratio | USA Today, Tufte, etc. | |||
| Chartjunk and ducks | ||||
| 12 May W | Graphical deception | |||
| Misleading and lying graphics | examples from ads, | |||
| Marlin, Sherman: Colleges | Tufte, Wainer, etc. | |||
| 14 May F | Graphical insights | |||
| Two examples | Napoleon's march | |||
| Chris: Good and bad graphics | The Challenger disaster |
Email: scwang@swarthmore.edu
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