![]() ![]() Population living in metropolitan areas ( pctmetro), the percent of the population People ( crime), murders per 1,000,000 ( murder), the percent of the Variables are state id ( sid), state name ( state), violent crimes per 100,000 Sciences, Third Edition by Alan Agresti and Barbara Finlay (Prentice Hall, 1997). This dataset appears in Statistical Methods for Social How can we identify these three types of observations? Let’s look at an example datasetĬalled crime. Substantially changes the estimate of coefficients. Influence: An observation is said to be influential if removing the observation These leverage points can have an effect on the estimate of Leverage is a measure of how far an observation Leverage: An observation with an extreme value on a predictor variable is calledĪ point with high leverage. Or may indicate a data entry error or other problem. An outlier may indicate a sample peculiarity Given its values on the predictor variables. In other words, it is an observation whose dependent-variable value is unusual Outliers: In linear regression, an outlier is an observation with large Want to know about this and investigate further. Observation (or small group of observations) substantially changes your results, you would Make a large difference in the results of your regression analysis. Regression assumptions and detect potential problems using Stata.Ī single observation that is substantially different from all other observations can We will explore these methods and show how to verify Of Durham) has produced a collection of convenience commands which can beĭownloaded from SSC ( ssc install commandname). That can be downloaded over the internet. Stata has many of these methods built-in, and others are available Many graphical methods and numerical tests have been developed over the years for ![]() Related, can cause problems in estimating the regression coefficients. Collinearity – predictors that are highly collinear, i.e., linearly.Influence – individual observations that exert undue influence on the coefficients.Speaking are not assumptions of regression, are none the less, of great concern to Variables, and excluding irrelevant variables)Īdditionally, there are issues that can arise during the analysis that, while Model specification – the model should be properly specified (including all relevant.Errors in variables – predictor variables are measured without error (we will cover this.Independence – the errors associated with one observation are not correlated with the.Homogeneity of variance (homoscedasticity) – the error variance should be constant.That the errors be identically and independently distributed Necessary only for hypothesis tests to be valid,Įstimation of the coefficients only requires Normality – the errors should be normally distributed – technically normality is.Linearity – the relationships between the predictors and the outcome variable should be. ![]() This chapter will explore how you can use Stata to check on how well yourĭata meet the assumptions of OLS regression. Without verifying that your data have met the assumptions underlying OLS regression, your results mayīe misleading. In the previous chapter, we learned how to do ordinary linear regression with Stata,Ĭoncluding with methods for examining the distribution of our variables. ![]()
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