Course Principles
Fosters active explorations through activities that lead students to explore
statistical ideas
Introduces topics through genuine data from real studies
Emphasizes issues of data collection and scope of conclusions from beginning
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Highlights connections between data collection method and inference procedure
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Models pedagogy and content that prospective K-12 teachers will be expected
to teach
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Utilizes simulation frequently, as a problem-solving tool and pedagogical
device
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Presents hands-on, tactile simulations before technology-based ones
Uses a variety of computational tools, including statistical software and
spreadsheet packages
Incorporates Java applets specifically designed to accompany the learning
activity
Emphasizes development of students’ communication skills
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Presents probability ideas in context of statistical applications
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Repeatedly presents complete process of statistical investigation, from
data collection through interpretation of results
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Preliminary Content Outline
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Chapter 1: Comparisons and Conclusions – descriptive analyses of
2x2 tables (segmented bar graphs, conditional proportions, relative risk,
odds ratio), types of variables, observational studies vs. controlled experiments,
confounding variables, causation, randomization, Simpson’s paradox
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Chapter 2: Experiments and Significance – simulating p-value for
randomization test, probability, equal likeliness, combinations and permutations,
hypergeometric probabilities, Fisher’s exact test
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Chapter 3: Sampling – population vs. sample, parameter vs. statistic,
sampling bias, precision, simple random sampling, binomial distribution,
binomial tests, effect of sample size, type I and II error, power
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Chapter 4: Large-Sample Approximations – normal approximations to
binomial, hypergeometric distributions, terminology and structure of significance
tests, z-test for a population proportion, z-test for a difference
in population proportions
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Chapter 5: Estimation and Confidence – exact binomial confidence
intervals, approximate z-intervals for a population proportion,
effects of confidence level and sample size, interpretation of confidence,
alternative procedures, sample size determination, CI for odds ratio
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Chapter 6: Quantitative Variables – descriptive analyses (graphical
displays and numerical summaries), sampling distributions of sample mean
and other statistics, Central Limit Theorem, t-distribution, t-tests
and t-intervals for one- and two-samples, robustness, prediction
intervals, transformations
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Chapter 7: Association and Prediction – simple linear regression
(descriptive and inferential), logistic regression, one-way ANOVA, chi-square
tests of independence, homogeneity of proportions
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