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Against All Odds Video Series: Timings

Against All Odds is a video series teaching introductory statistics distributed by Annenberg/CPB http://www.learner.org. Most Against All Odds programs are organized as a series of vignettes plus a short introduction and conclusion. Each vignette deals with a particular practical application. This guide gives the duration in minutes of each vignette and identifies the area of application and the statistical methods introduced in each one. Programs are in most cases 27 minutes long, of which the first two minutes is credits and preview.

Keywords describing the applied context are given in italics. Keywords describing the statistical concepts explicitly introduced are in roman.

I made these timings in 1991 while preparing to teach an introductory biostatistics subject at the University of Queensland. In most lectures I used an 8-10 minute segment of video comprising one or two vignettes. The timings were essential for planning each lecture.

  1. What is Statistics?
    Creativity of childrens' drawings, historical anecdotes, contemporary applications: This video provides an introduction to the nature and applicability of statistics, but no specific statistical methods are discussed. I do not use this program for teaching.
    0 - 1
    1 - 4   Introduction: Creativity of childrens' drawings etc
    4 - 14   Use of statistics in business decisions: Domino's Pizza
    14 - 17   Describing data
    17 - 22   Producing data
    22 - 26   Conclusions from data
    26 - 27   Conclusion
  2. Picturing Distributions
    0 - 2
    2 - 4   The power of visual display: map of Napoleon's retreat from Moscow from Tufte's book
    4 - 14   Predicting lightning: histogram, symmetry, outliers. Heights of woman undergraduates
    14 - 21   TV programming: skew distributions
    21 - 22   Rush hour traffic: choosing bin sizes
    22 - 27   Health care costs and diagnosis related groups: spread, stemleaf plots, back to back stemleaf
  3. Numerical Description of Distributions
    0 - 2
    2 - 16   Earnings: median (US), men vs women (Colorado), mean, quartiles, 5 number summary
    16 - 21   Calories in hot dogs: boxplots, interquartile range
    21 - 27   Urine samples: tunes, standard deviation, variance
    27 - 28   Conclusion
  4. Normal Distributions
    0 - 2
    2 - 3   Introduction with building blocks
    3 - 11   Demand for social security in 2030: population distributions now and in 50 years, density curves
    11 - 14   Home video: normal is everywhere
    14 - 16   Finding mu and sigma on the normal density curve
    16 - 20   Beanstalk club and women's heights: standard deviation as a measuring stick, 68-95-99.7 rule
    20 - 27   Baseball and decreasing batting averages: standardized scores
  5. Normal Calculations
    0 - 2
    3 - 7   Women's heights, mother and daughter heights: standard normal z = (x-µ)/sigma, proportion above and below
    7 - 14   NO_X emissions of GM cars: calculate proportion above
    14 - 20   Blood cholesterol: calculate proportion between
    20 - 24   Head sizes of army recruits: percentiles x=µ+z\sigma
    24 - 27   Women's heights, CO_2, NO_X: quantile plot
  6. Time Series
    0 - 2
    2 - 6   Commuting times: control chart
    6 - 10   Body cycle: seasonal pattern
    10 - 12   Ozone: trend
    12 - 15   Running times: running medians
    15 - 18   Surprise: average of time series for pattern
    18 - 26   Stockmarket: average to reduce variance, existence of cycles
    26 - 27   Significance
  7. Models for Growth
    0 - 2
    2 - 3   Introduction
    3 - 10   Sarah's growth deficiency: piecewise linear growth
    10 - 14   Ticket sales: a+bx formula, residuals
    14 - 20   Gipsy moths: exponential growth
    20 - 23   The courtier & the King: geometric series
    23 - 24   Compound interest:
    24 - 27   Crude oil: log-transformation, residuals
  8. Describing Relationships
    0 - 2
    2 - 3   Introduction
    3 - 9   Manatees: scatterplot, response, explanatory variable, cases, positive association, negative association, linear association, outliers
    9 - 11   Flouride in drinking water: colour for categorical variable in scatterplot
    11 - 15   Draft lottery: median trace, least squares regression
    15 - 25   Obesity: least squares calculations
    25 - 27   Wages: beware extrapolation, outliers, influence, lurking variables
  9. Correlation
    0 - 2
    2 - 5   Cakes: correlation
    5 - 16   Twins: genetics vs environment, correlation calculations, curvilinear relationship
    16 - 21   Baseball home runs: r^2 is fraction of variance explained
    21 - 27   Inequality in education: causation
  10. Multidimensional Data Analysis
    0 - 2
    2 - 3   Chesapeake Bay, introduction
    3 - 16   Abundance of life in Chesapeake Bay sediments: regression, seasonal trend
    16 - 17   Forgery: Chernoff faces
    17 - 18   Cars: trees, stars
    18 - 28   Voice synthesis, epicentres of earthquakes: point clouds, brushing, rotating at Bell Communications
  11. The Question of Causation
    0 - 2
    2 - 3   Introduction
    3 - 5   Icecream and drownings, 55mph limit and accidents, Louisiana land loss: association isn't causation
    5 - 12   Admission at City University: 2x2 table, lurking variable, correlation for quantitative variables vs table for categorical variables, Simpson's paradox
    12 - 27   Smoking and lung cancer: establishing causation when an experiment isn't possible
  12. Experimental Design
    0 - 2
    2 - 3   Introduction, granpa and whisky: producing data
    3 - 8   Lobsters: observation
    8 - 15   Physicians' aspirin study: randomization, placebo, double-blind
    15 - 16   Ribaviron
    16 - 19   Random numbers to select sample
    19 - 25   Domestic violence: random assignment
    25 - 26   Dr Confound: poor design
    26 - 27   Conclusion: random assignment, controlled experiments
  13. Experiments and Samples
    0 - 2
    2 - 5   Washing: blocking
    5 - 13   Strawberries: randomized complete block
    13 - 21   Census. Hite Report.
    21 - 27   Frito-Lay potato crisps: sampling
  14. Sampling and Sampling Distributions
    0 - 2
    2 - 5   Day care: stratified survey
    5 - 9   Fishing: stratified survey
    9 - 10   Roosevelt election: Gallup poll, bias
    10 - 12   Street survey: biased questioning
    12 - 22   NORC General Social Survey: survey design, multistage sample
    22 - 28   Sampling bias: precision vs sample size
  15. What is probability?
    0 - 2
    2 - 5   Dice, introduction
    5 - 8   Car accidents
    8 - 10   Assessing risk
    10 - 11   Tossing coin
    11 - 15   Persi Diaconis: the meaning of probability
    15 - 18   Tossing coins: sample space. Dice: equal probabilities
    18 - 28   New York traffic: simulation, addition rule
  16. Random Variables
    0 - 2
    2 - 4   Independence, introduction: sex of children, `Stand and Deliver' movie
    4 - 13   Challenger disaster: multiplication rule, O-rings not independent
    13 - 14   Multiplication and addition rules
    14 - 19   Random variables, toss of 4 coins. Continuous vs discrete: weight of babies etc. Basketball scores: probability histogram. Continuous density: time.
    19 - 25   Earthquakes: 20yr intervals, normal
    25 - 27   Ice cubes in coke: mean and stdev of discrete rv
  17. Binomial Distributions
    0 - 2
    2 - 4   Insurance: law of large numbers
    4 - 7   Basketball: successive throws are independent
    7 - 10   Rates of return from stocks and treasury bonds: rules for means
    10 - 16   College class investment: diversification less variable, rules for variances
    16 - 17   The binomial distribution: independent trials
    17 - 25   Sickle cell anemia: recessive genetic inheritance, mean and variance of binomial
    25 - 27   Quincox: normal approximation for binomial, n large or p near 0.5
  18. The Sample Mean and Control Charts
    0 - 2
    2 - 16   Casinos: expected value, variance of single bet, Central Limit Theorem for sample mean, \sigma/\sqrt{n}
    16 - 23   Frito-Lay potato crisps: SPC, control charts
    23 - 27   Deming
  19. Confidence Intervals
    0 - 2
    2 - 4   Blood pressure: introduction
    3 - 7   Bush vs Dukakis polls: margin of error
    7 - 12   Blood pressure: CI for normal mean
    12 - 18   Battery life: 95% CI, \bar x\pm z^*\sigma/\sqrt n
    18 - 27   Primates for experiments: determining sample size
  20. Significance testing
    0 - 2
    2 - 4   Seat belts: null and alternative hypotheses
    4 - 19   Poem by Shakespeare?: Thisted, z={\bar x-µ}\over{\sigma/\sqrt n}
    19 - 28   FBI discrimination case
  21. Inference for One Mean
    0 - 2
    2 - 4   Introduction, \sigma not known, s
    4 - 6   Gosset: t-distribution, degrees of freedom, \bar x\pm t\frac{s}{\sqrt n}, \frac{\bar x-µ}{s/\sqrt n}
    6 - 13   National Institute of Standards and PCBs: t-distribution, confidence interval, t^*
    13 - 21   Nutra-Sweet: paired comparisons, paired t-test
    21 - 27   Autism and Vineland Adaptive Scores: CI for difference between living and social scores
  22. Comparing Two Means
    0 - 2
    2 - 3   Introduction
    3 - 15   Option welfare program: 2 sample t-statistic, conservative for unequal variances, confidence interval
    15 - 21   Bouncing foam: 2 sample t test
    21 - 28   Coaching effectiveness: confidence interval
  23. Inference for Proportions
    0 - 2
    2 - 3   Introduction: Woburn water contamination
    3 - 12   Unemployment rate: µ_{\hat p}, \sigma_{\hat p}, confidence interval, test
    12 - 20   Woburn water contamination: confidence interval for p_1-p_2
    20 - 28   Salem witch trials: hypothesis test of p_1-p_2=0
  24. Inference for Two Way Tables
    0 - 2
    2 - 3   Introduction
    3 - 4   Veteran's study: outcomes in 3 x 2 table
    4 - 13   Dental anthropology: chi-square statistic
    13 - 22   Breast cancer and age bias: age x cancer, chi-square distribution
    22 - 27   Mendel's pea experiment: subjectivity in classification
  25. Inference for Relationships
    0 - 2
    2 - 3   Astronomy, introduction: least squares regression, correlation
    3 - 20   Hubble 100in telescope and Hubble constant: simple linear regression, s^2, confidence interval for slope
    20 - 27   Birth defects and ultrasound: confidence interval for fitted value, prediction interval
  26. A Case Study
    0 - 2
    2 - 27   Aids and AZT: statistical thinking discussed in general terms - data collection, analysis, more data collection - but no methods given explicitly

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Copyright © Gordon Smyth 1996-2004. Last modified: 20 April 2004