'Regression residuals' diaporamas de présentation

Chapter 5

Chapter 5. Relationships: Regression. Chapter 5: Regression. 1. Objectives (BPS chapter 5). Regression Regression lines The least-squares regression line Facts about least-squares regression Residuals Influential observations Cautions about correlation and regression

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Multiple Regression Forecasts

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Residuals

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EQ: How do you use Residual Plots to assess how well a LSRL fits a data set?

AP STAT Section 3.2: Least Squares Regression Part 3: Interpreting Residual Plots. EQ: How do you use Residual Plots to assess how well a LSRL fits a data set?.

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DAILY GRADE #6 --- 20 minutes Test Ch 1 & 2 Mon THQ#2 Due Thurs

DAILY GRADE #6 --- 20 minutes Test Ch 1 & 2 Mon THQ#2 Due Thurs. AP STAT Section 3.2: Least Squares Regression Part 3: Interpreting Residual Plots. EQ: How do you use Residual Plots to assess how well a LSRL fits a data set?.

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Cointegration in Single Equations: Lecture 6

Cointegration in Single Equations: Lecture 6. Statistical Tests for Cointegration Thomas 15.2 Testing for cointegration between two variables Cointegration is useful since it identifies a long-run relationship between I(1) variables.

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Programming and Simulations

Programming and Simulations. Frank Witmer 6 January 2011. Outline. General programming tips Programming loops Simulation Distributions Sampling Bootstrapping. General Programming Tips. Use meaningful variable names Include more comments than you think necessary Debugging your code

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2.4: Cautions about Regression and Correlation

2.4: Cautions about Regression and Correlation. Cautions: Regression & Correlation. Correlation measures only linear association. Extrapolation often produces unreliable predictions. Correlation and least-squares regression are not resistant.

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Least-Squares Regression

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Regression residuals

Regression residuals. LS Residuals. Standardized residuals. s I is an estimate of the standard deviation of. s (I) is an estimate computed from the regression that excludes the ith observation. t residuals. Diagnostics for model assumptions. Linearity Residual plots: look for curvature

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