Applied Regression Analysis for Business and Economics
Designed for undergraduate and MBA courses in regression analysis for business and economics, this text requires very little mathematical expertise beyond college algebra. Terry Dielman emphasizes the importance of understanding the assumptions of the regression model, knowing how to validate a selected model for these assumptions, knowing when and how regression might be useful in a business setting, and understanding and interpreting output from statistical packages and spreadsheets.
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Accept H0 Adjusted R Square Analysis of Variance ANOVA assumption average BONUS column computed confidence interval confidence interval estimate cost decision rule degrees of freedom denotes an observation dependent variable dialog box Durbin-Watson statistic example Excel output explanatory variables F statistic FIGURE MINITAB file with prefix Fit Residual St Fit StDev Fit forecast indicator variables interval estimate large standardized residual level of significance linear MINITAB and Excel MINITAB Output MINITAB Plot MINITAB Regression Output normally distributed null hypothesis P-value Plot of Standardized population mean population regression prediction prediction interval Predictor Coef StDev R-Sq R-Sq(adj regression equation regression line Regression Statistics Multiple Reject H0 relationship residual plots Residual St Resid SALARY sample mean sampling distribution scatterplot shown in Figure Source DF SS SS MS F standard deviation Standard Error Standardized Residuals Versus StDev Fit Residual sum of squares test statistic Total Unusual Observations Obs Upper 95 Variance Source DF