Session 1 - Fundamental Principles of Statistics
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Purpose of Statistics
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Definitions: Samples and Population
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Variable Types: Nominal, ordinal, interval/ratio, dependent, independent, continuous, discrete
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Sampling: sampling plans and errors involved in sampling
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Session 2 - Numerical Descriptive Measures
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Measures of central tendency (mean, median, mode)
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Measures of Spread (range, variance, standard deviation, percentiles)
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Measures of Association (covariance, correlation coefficient)
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Session 3 - Graphical Descriptive Techniques
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Graphical techniques for categorical data (pie charts, bar charts, line charts)
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Graphical techniques for quantitative data (stem-leaf plots, histograms, boxplots)
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Bivariate plots: Scatterplots
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Session 4 - Estimation
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Concepts of Estimation; estimator bias and precision
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Confidence interval estimation of the mean; variance known/unknown
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Standard error; selecting sample size
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Session 5 - Hypothesis Testing
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Logic of Hypothesis Testing; tests on the mean (z-test and t-test)
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Type I/II errors and p-values
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Statistical power analysis and sample size determination
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Statistical significance and practical importance
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Why is the significance criterion typically set at .05?
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Testing for mean differences between groups
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Session 6 - Comparing Groups: Categorical Data
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A Basic two-way table
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Chi-Square test of independance
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Small sample considerations
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Session 7 - Introduction to Regression
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Introduction and Basic Concepts
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The Regression Equation and Fit Measure
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Assumptions
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Simple Regression and Multiple Regression
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Stepwise Regression
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Residual and Outlier Results
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Summary of Regression Results
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Session 8 - A look forward
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Cluster analysis
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Principal component analysis
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Discriminant analysis
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Analysis of variance
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