Early Inference: Using Bootstraps to Introduce Confidence Intervals Robin H. Lock, Burry Professor of Statistics Patti Frazer Lock, Cummings Professor.

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Presentation transcript:

Early Inference: Using Bootstraps to Introduce Confidence Intervals Robin H. Lock, Burry Professor of Statistics Patti Frazer Lock, Cummings Professor of Mathematics St. Lawrence University Joint Mathematics Meetings New Orleans, January 2011

Intro Stat at St. Lawrence Four statistics faculty (3 FTE) 5/6 sections per semester students per section Only 100-level (intro) stat course on campus Students from a wide variety of majors Meet full time in a computer classroom Software: Minitab and Fathom

Stat Traditional Topics Descriptive Statistics – one and two samples Normal distributions Data production (samples/experiments) Sampling distributions (mean/proportion) Confidence intervals (means/proportions) Hypothesis tests (means/proportions) ANOVA for several means, Inference for regression, Chi-square tests

When do current texts first discuss confidence intervals and hypothesis tests? Confidence Interval Significance Test Moorepg. 359pg. 373 Agresti/Franklinpg. 329pg. 400 DeVeaux/Velleman/Bockpg. 486pg. 511 Devore/Peckpg. 319pg. 365

Stat Revised Topics Descriptive Statistics – one and two samples Normal distributions Data production (samples/experiments) Sampling distributions (mean/proportion) Confidence intervals (means/proportions) Hypothesis tests (means/proportions) ANOVA for several means, Inference for regression, Chi-square tests Data production (samples/experiments) Bootstrap confidence intervals Randomization-based hypothesis tests Normal distributions Bootstrap confidence intervals

Prerequisites for Bootstrap CI’s Students should know about: Parameters / sample statistics Random sampling Dotplot (or histogram) Standard deviation and/or percentiles

What is a bootstrap? and How does it give an interval?

Example: Atlanta Commutes Data: The American Housing Survey (AHS) collected data from Atlanta in What’s the mean commute time for workers in metropolitan Atlanta?

Sample of n=500 Atlanta Commutes Where might the “true” μ be?

“Bootstrap” Samples Key idea: Sample with replacement from the original sample using the same n. Assumes the “population” is many, many copies of the original sample.

Atlanta Commutes – Original Sample

Atlanta Commutes: Simulated Population

Creating a Bootstrap Distribution 1. Compute a statistic of interest (original sample). 2. Create a new sample with replacement (same n). 3. Compute the same statistic for the new sample. 4. Repeat 2 & 3 many times, storing the results. 5. Analyze the distribution of collected statistics. Important point: The basic process is the same for ANY parameter/statistic. Bootstrap sample Bootstrap statistic Bootstrap distribution

Bootstrap Distribution of 1000 Atlanta Commute Means

Using the Bootstrap Distribution to Get a Confidence Interval – Version #1 The standard deviation of the bootstrap statistics estimates the standard error of the sample statistic. Quick interval estimate : For the mean Atlanta commute time:

Quick Assessment HW assignment (after one class on Sept. 29): Use data from a sample of NHL players to find a confidence interval for the standard deviation of number of penalty minutes.

Example: Find a confidence interval for the standard deviation, σ, of Atlanta commute times. Original sample: s=20.72 Bootstrap distribution of sample std. dev’s SE=1.76

Quick Assessment HW assignment (after one class on Sept. 29): Use data from a sample of NHL players to find a confidence interval for the standard deviation of number of penalty minutes. Results: 9/26 did everything fine 6/26 got a reasonable bootstrap distribution, but messed up the interval, e.g. StdError( ) 5/26 had errors in the bootstraps, e.g. n=1000 6/26 had trouble getting started, e.g. defining s( )

Using the Bootstrap Distribution to Get a Confidence Interval – Version # Keep 95% in middle Chop 2.5% in each tail

Using the Bootstrap Distribution to Get a Confidence Interval – Version # Keep 95% in middle Chop 2.5% in each tail For a 95% CI, find the 2.5%-tile and 97.5%-tile in the bootstrap distribution 95% CI=(27.33,31.00)

90% CI for Mean Atlanta Commute Keep 90% in middle Chop 5% in each tail For a 90% CI, find the 5%-tile and 95%-tile in the bootstrap distribution 90% CI=(27.52,30.68)

99% CI for Mean Atlanta Commute Keep 99% in middle Chop 0.5% in each tail For a 99% CI, find the 0.5%-tile and 99.5%-tile in the bootstrap distribution 99% CI=(27.02,31.82)

Intermediate Assessment Exam #2: (Oct. 26) Students were asked to find a 95% confidence interval for the correlation between water pH and mercury levels in fish for a sample of Florida lakes – using both SE and percentiles from a bootstrap distribution.

Example: Find a 95% confidence interval for the correlation between time and distance of Atlanta commutes. Original sample: r =0.807 (0.72, 0.87)

Intermediate Assessment Exam #2: (Oct. 26) Students were asked to find a 95% confidence interval for the correlation between water pH and mercury levels in fish for a sample of Florida lakes – using both SE and percentiles from a bootstrap distribution. Results: 17/26 did everything fine 4/26 had errors finding/using SE 2/26 had minor arithmetic errors 3/26 had errors in the bootstrap distribution

Transitioning to Traditional Intervals AFTER students have seen lots of bootstrap distributions (and randomization distributions)… Introduce the normal distribution (and later t) Introduce “shortcuts” for estimating SE for proportions, means, differences, slope…

Advantages: Bootstrap CI’s Requires minimal prerequisite machinery Requires minimal conditions Same process works for lots of parameters Helps illustrate the concept of an interval Explicitly shows variability for different samples Possible disadvantages: Requires good technology It’s not the way we’ve always done it

What About Technology? Possible options? Fathom R Minitab (macro) JMP (script) Web apps Others? xbar=function(x,i) mean(x[i]) b=boot(Margin,xbar,1000)

Miscellaneous Observations We were able to get to CI’s (and tests) sooner More issues using technology than expected Students had fewer difficulties using normals Interpretations of intervals improved Students were able to apply the ideas later in the course, e.g. a regression project at the end that asked for a bootstrap CI for slope Had to trim a couple of topics, e.g. multiple regression

Final Assessment Final exam: (Dec. 15) Find a 98% confidence interval using a bootstrap distribution for the mean amount of study time during final exams Results: 26/26 had a reasonable bootstrap distribution 24/26 had an appropriate interval 23/26 had a correct interpretation

Support Materials? or We’re working on them… Interested in class testing?