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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