Models and Modeling in Introductory Statistics Robin H. Lock Burry Professor of Statistics St. Lawrence University 2012 Joint Statistics Meetings San Diego,

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Presentation transcript:

Models and Modeling in Introductory Statistics Robin H. Lock Burry Professor of Statistics St. Lawrence University 2012 Joint Statistics Meetings San Diego, August 2012

What is a Model?

A simplified abstraction that approximates important features of a more complicated system

Traditional Statistical Models Population Y  N(μ,σ) Often depends on non-trivial mathematical ideas.

Traditional Statistical Models Relationship Predictor (X) Response (Y)

“Empirical” Statistical Models A representative sample looks like a mini-version of the population.  Model a population with many copies of the sample. Bootstrap Sample with replacement from an original sample to study the behavior of a statistic.

“Empirical” Statistical Models Hypothesis testing: Assess the behavior of a sample statistic, when the population meets a specific criterion.  Create a Null Model in order to sample from a population that satisfies H 0 Randomization

Traditional vs. Empirical Both types of model are important, BUT Empirical models (bootstrap/randomization) are More accessible at early stages of a course More closely tied to underlying statistical concepts Less dependent on abstract mathematics

Example: Mustang Prices Estimate the average price of used Mustangs and provide an interval to reflect the accuracy of the estimate. Data: Sample prices for n=25 Mustangs

Original Sample Bootstrap Sample

Original Sample Bootstrap Sample Bootstrap Statistic Sample Statistic Bootstrap Statistic Bootstrap Distribution

Bootstrap Distribution: Mean Mustang Prices

Background? What do students need to know about before doing a bootstrap interval? Random sampling Sample statistics (mean, std. dev., %-tile) Display a distribution (dotplot) Parameter vs. statistic

Traditional Sampling Distribution Population µ BUT, in practice we don’t see the “tree” or all of the “seeds” – we only have ONE seed

Bootstrap Distribution Bootstrap “Population” What can we do with just one seed? Grow a NEW tree! µ

Golden Rule of Bootstraps The bootstrap statistics are to the original statistic as the original statistic is to the population parameter.

Round 2

Course Order Data production Data description (numeric/graphs) Interval estimates (bootstrap model) Randomization tests (null model) Traditional inference for means and proportions (normal/t model) Higher order inference (chi-square, ANOVA, linear regression model)

Traditional models need mathematics, Empirical models need technology!

Some technology options: R (especially with Mosaic) Fathom/Tinkerplots StatCrunch JMP StatKey

Three Distributions One to Many Samples Built-in data Enter new data

Interact with tails Distribution Summary Stats

Smiles and Leniency Does smiling affect leniency in a college disciplinary hearing? Null Model: Expression has no affect on leniency LeFrance, M., and Hecht, M. A., “Why Smiles Generate Leniency,” Personality and Social Psychology Bulletin, 1995; 21:

Smiles and Leniency Null Model: Expression has no affect on leniency

StatKey p-value = 0.023

Traditional t-test H 0 :μ s = μ n H 0 :μ s > μ n

Round 3

Assessment? Construct a bootstrap distribution of sample means for the SPChange variable. The result should be relatively bell-shaped as in the graph below. Put a scale (show at least five values) on the horizontal axis of this graph to roughly indicate the scale that you see for the bootstrap means. Estimate SE? Find CI from SE? Find CI from percentiles?

Assessment? From 2009 AP Stat: Given summary stats, test skewness Find and interpret a p-value Given 100 such ratios for samples drawn from a symmetric distribution Ratio=1.04 for the original sample

Implementation Issues Good technology is critical Missed having “experienced” student support the first couple of semesters

Round 4

Why Did I Get Involved with Teaching Bootstrap/Randomization Models? It’s all George’s fault... "Introductory Statistics: A Saber Tooth Curriculum?" Banquet address at the first (2005) USCOTS George Cobb

Introduce inference with “empirical models” based on simulations from the sample data (bootstraps/randomizations), then approximate with models based on traditional distributions. Models in Introductory Statistics