Chapter 17.docx
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Department
Operations Management and Information System
Course
OMIS 2010
Professor
Alan Marshall
Semester
Fall

Description
Chapter 17 Multiple RegressionModel and Required ConditionsAssume that k independent variables potentially related to dependent variable YB0B1x1B2x2 Bkxk eoY is the dependent variableoX1 x2 xk are independent variablesoE is error variableoB0 B1 Bk are coefficientsDeviations between additional independent variables included deviations between predicted values of y and actual values of y will occur Response surface graphical depiction of equation when more than independent variable is in regression model Required Conditions for Error VariableProbability distribution of the error variable is normalMean of error variable is 0Standard deviation of error variable is a constantErrors are independent Estimating Coefficients and Assessing the ModelDetermine how well model fits the dataIf fit is poor then no point in further analysis of coefficients of that model1Select variables that you think are linearly related to dependent variable 2Generate coefficients and stats used to assess the model3Diagnose violations of required conditions4Assess models fit use standard error of estimate coefficient of determination and Ftest of ANOVA5Interpret the coefficient use the model to predict value of dependent variable or estimate expectedvalue of dependent variableOnly screen independent variables and include those that affectdependent variable
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