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Statistics 191: Introduction to Applied Statistics Jonathan Taylor Department of Statistics Stanford University Statistics 191: Introduction to Applied Statistics Review Jonathan Taylor Department of Statistics Stanford University January 6, 2010 1 / 1

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Statistics 191: Introduction to Applied Statistics Jonathan Taylor Department of Statistics Stanford University Outline What is a regression model? Descriptive statistics – numerical Descriptive statistics – graphical Inference about a population mean Difference between two population means 2 / 1
Statistics 191: Introduction to Applied Statistics Jonathan Taylor Department of Statistics Stanford University What is course about? It is a course on applied statistics. Hands-on: we use R, an open-source statistics software environment. We will start out with a review of introductory statistics to see R in action. Main topic is “(linear) regression models”: these are the bread and butter of applied statistics. What is a “regression” model? A regression model is a model of the relationships between some covariates (predictors) and an outcome . Specifically, regression is a model of the average outcome given the covariates. 3 / 1

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Statistics 191: Introduction to Applied Statistics Jonathan Taylor Department of Statistics Stanford University Heights of couples To study height of the wife in a couple, based on the husband’s height and her parents height: Wife is the outcome, and the covariates are Husband, Mother, Father . A mathematical model, using only Husband ’s height: Wife = f ( Husband ) + ε where f gives the average height of the wife of a man of height Husband and ε is “error”: not every man of height of Husband marries a woman of height f ( Husband ). A statistical question: is there any relationship between covariates and outcomes – is f just a constant? Here is some http://stats191.stanford.edu/review.htmldata using only 4 / 1
Statistics 191: Introduction to Applied Statistics Jonathan Taylor Department of Statistics Stanford University Heights data R code 5 / 1

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Statistics 191: Introduction to Applied Statistics Jonathan Taylor Department of Statistics Stanford University Heights data Linear regression models We might model the data as Wife = β 0 + β 1 Husband + ε.
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