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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