Macalester MATH 155 - Lab 9 – Interaction and Logistic Regression (5 pages)

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Lab 9 – Interaction and Logistic Regression



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Lab 9 – Interaction and Logistic Regression

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5
School:
Macalester College
Course:
Math 155 - Intro to Statistical Modeling

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http lab10 pdf 1 Math 155 Spring 2005 Introduction to Statistical Modeling Daniel Kaplan Macalester College Lab 9 Interaction and Logistic Regression This lab is about two topics in modeling Interaction refers to a particular way of structuring models it is a concept that applies to a wide variety of models Logistic Regression is a form of nonlinear modeling that is particularly useful when the response variable is binomial e g yes or no alive or dead success or failure The two concepts are brought together in this lab only because we want to cover both of them in the course and in part to show how interaction applies both in linear modelling and nonlinear modelling Interaction score a quantitative variable will be the response variable Group and noise level group and noise will be the explanatory variables Although group could obviously not be manipulated experimentally the noise level could be See Exercise 1 To start we read in the data a read csv noise csv It s often helpful to look at the data graphically In this case a boxplot of the test score as a function of the explanatory variables is appropriate boxplot score group data a The data set noise csv is from an experiment to study how noise affects the performance of children In the experiment second graders were given a test of math problems and their score was recorded The children were either normal or hyperactive and the test conditions were either high noise or low noise Each child took just one test boxplot score noise data a We want to see how the noise level affects the test score and whether hyperactivity plays a role as well Test score It looks like the hyperactive children score lower and that high noise levels increase the spread of scores compared to low noise levels but don t alter the median score by very much We can confirm these visual impressions by constructing linear models Math 155 Introduction to Statistical Modeling April 26 2005 http lab10 pdf 2 There are 400 cases altogether evenly



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