Textbook Notes (368,070)
York University (12,801)
OMIS 2010 (32)
Chapter 16

7 Pages
43 Views

School
Department
Operations Management and Information System
Course
OMIS 2010
Professor
Alan Marshall
Semester
Fall

Description
Chapter 16 Sample Linear Regression and CorrelationRegression analysis used to predict value of one variable on basis of other variablesDependent variable variable to be forecastIndependent variable variable that practitioner believes are related to dependent variables x1 x2 xkCorrelation analysis determines whether relationship existsModelDeterministic models equation that allow us to determine value of the dependent variable from values of independent variablesProbabilistic Model method to represent randomnesse is the error variable accounts for all variables measurable and immeasurable that are not part of the modeloIts value varies from one sale to the next even if x remains constant FirstOrder Linear Model simple linear regression model y dependent variableB0 yint yint B1xindependent variablee error variable X and y must be intervalEstimating the CoefficientsDraw random samples from population of interestCalculate sample statistics to estimate B0 and B1Estimators based on drawing straight line though sample data least squares line comes closest to sample data pointsoYhat predictedfitted value of yb0 b1xoB0 and b1 calculated so that sum of squared deviations is minimizedoYhat on average comes closest to observed values of yoLeast squares method produces straight line that minimizes the sum of the squared difference between the points and the lineob0 and b1 are unbiased estimators of B0 and B1oResiduals deviations between the actual data pints and the line eioEiyiyhatResiduals are observations of the error variable
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