AGAINST ALL ODDS VIDEOS

“Against All Odds” is a set of videotapes that cover a variety of introductory topics in statistics.  The College of Business purchased these tapes and they are available for student viewing in the Media Center of the Library.  “Against All Odds” is an excellent review of material in both MGT 216 and MGT 217.  The following topics are covered in the series.

PROGRAM 1: (introduction--216)

What is Statistics?  An overview of the nature and impact of statistics using historical anecdotes and short views of contemporary applications.

PROGRAM 2:  (lectures 2 and 3--216)  

Picturing Distributions Presenting and interpreting the distribution of a single variable. Techniques taught include stem plots, frequency tables and histograms.

PROGRAM 3:   (lectures 1 and 2--216)

Describing Distributions: Numerical Description of Distributions   Numerical measures of specific aspects of a distribution: center (mean, median, mode), spread (percentiles, five number summary, boxplots, and standard deviation).  Resistance and its lack.

PROGRAM 4:   (lecture 8--216)

Normal Distributions Topics Density curves as smoothed histograms: mean, median, percentiles for density curves: the normal distributions (general shape, locating the mean and standard deviation the 68-95-99.7 rule).

PROGRAM 5:  

Normal Calculations Standardization and calculation of normal relative frequencies from tables; assessing normality by normal quartile plots.

PROGRAM 6:   (lectures 11, 12, 13--217)

Time Series From the distribution of a single variable we move to an examination of change over time. Topics: Statistical control, inspecting time series for trend, seasonal variation, cycles; smoothing by averaging either over many units per time or over time by running medians.

PROGRAM 7:    (lecture 11--217)

Models for Growth Mathematical Models for the overall pattern of simple lands of growth over time. Topics: Linear growth with review of the geometry of straight lines and an introduction to the least squares idea; exponential growth, and straightening an exponential growth curve by logarithms: prediction and extrapolation.

PROGRAM 8:   (lectures 5 and 6--217)

Describing Relationships   Topics: scatterplots and their variations; smoothing scatterplots of response vs. explanatory variable by median trace; linear relationships, least squares regression lines and comment on outliers and influential observations.

PROGRAM 9:  (lecture 4--217)

Correlation  Correlation and it properties: the relation between correlation and regression.

PROGRAM 10:

Multidimensional Data Analysis The impact of computing technology on statistics especially graphics for displaying multidimensional data. A case study in data analysis will employ techniques discussed in previous  program.

PROGRAM 11:

The Question of Causation   Association between categorical variables displayed in a two-way table; Simpson’s paradox: the varied relations among variables that can underlie an observed association; how evidence For causation is obtained.

Program 12:

Experimental Design  Advantages of planned data collection over anecdotal evidence or available data.  The idea of an experiment.  Basic principles of design: comparison, randomization, replication.

PROGRAM 13:

Blocking and Sampling Experiments and samples further principles of design: two or more factors and blocking. Introduction to sample surveys, the danger of bias, random sampling

PROGRAM 14:

Samples and Surveys: Sampling and Sampling Distributions    More elaborate sample designs: stratified and multistage designs. The practical difficulties of sampling human populations. The idea of a sampling  distribution.

PROGRAM 15:   (lecture 5--216)

What is Probability? Probability as a model for long term relative frequencies or personal assessment of chance. Sample space, basic rules of assigning probability: addition rule for disjoint events.

PROGRAM 16:   (lecture 6--216)

Random Variables independence and the multiplication rule for independent events. Discrete and continuous random variables. Mean and variance of a random variable.

PROGRAM 17:   (lecture 6--216)

Binomial Distributions The law of large numbers. Addition rules for means and variances of random variables. The binomial distributions for sample counts. Norman approximation to binomial.

PROGRAM 18:   (lecture 16--217)

The Sample Mean and Control  Charts The sampling distribution of x-bar, the central limit theorem, x-bar control charts and statistical process control.

PROGRAM 19:   (lecture 10-216)

Confidence Intervals   The reasoning behind confidence intervals. z-intervals for the mean of a normal distribution. Behavior of confidence intervals.

PROGRAM 20:   (lecture 12--216)

Significance Tests The reasoning behind significance tests illustrated by the sample case of tests on a normal mean with known standard deviation. Null and alternative hypotheses and p-values and cautions on the limited information provided by tests.

PROGRAM 21:    (lectures 13 and 16--216)

Inference for One Mean  Inference about the mean of a single distribution with emphasis on paired samples as the most important practical use of these procedures. The t confidence interval and test.

PROGRAM 22:   (lecture 14 and 15-216)

Comparing Two Means  The two-sample t confidence intervals and tests for comparing means; brief mention of the sensitivity of the corresponding procedures for variances to non-normality and their consequent impracticality.

PROGRAM 23:   (lectures 12 and 16--216)

Inference for Proportions Confidence intervals and tests for a single proportion and for comparing proportions based on paired and independent samples.

PROGRAM 24:   (lecture 17--216)

Inference for Two-Way Tables Chi-square test for independence/equal distributions in two-way tables.

PROGRAM 25:    (lecture 5--217)

Inference for Relationships Inference for simple linear regression, emphasizing slope and prediction.

PROGRAM 26: