T 23 A negative relationship between two variables X and Y indicates that large

# T 23 a negative relationship between two variables x

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T23 A negative relationship between two variables X and Y indicates that largeof X are associated with small values of Y.F24 The sample correlation coefficient has possible values ranging from zero toT25 The possible values for the coefficient of determination range from zero toF26 We cannot commit a Type I error when the null hypothesis is true.T27 If we are interested in determining whether two variables are linearly related, it is necessary tT28 The least squares method for determining the best fit minimizes sum of squares for error.F29 A multiple regression is called "multiple" because it has several data pointT30 Given that the sum of squares for error is 60 and the sum of squares for regression is 140, then the coefficient of determination is 0.70.H0:b1= 0H1:b1< 0Reject null hypothesis if t < - t 0.02, 5perform the t-test of the slope b1.
MSIS 361A Final ExamPage 19 of 2712/04/2016Name:tron.p-value of a two tail test = 2 * p-value of a one tail testageesults.H1: m> 25000
MSIS 361A Final ExamPage 20 of 2712/04/2016r is positive for positive slope of line and visa versa.R sq = [cov(X,Y)] ^2/[var(X)*var(Y)]R sq = (1260)^2/(1600*1225) =0.81 b1 = cov(X,Y)/ var(X) = 250/10^2 =2.5 b0 = Y bar - b1 * X bar = (75/10) - 2.5 * (50-5 residual = errory hat = 3 + 2 * x=y-y hatsquared residuals11 -3 9 7 -2 4 5 -3 9 sum22
MSIS 361A Final ExamPage 21 of 2712/04/2016
MSIS 361A Final ExamPage 22 of 2712/04/2016ent variables,ues of y is 180.E)/(n-k-1)]= sqrt [(180)/(20-4-1)]=um of squarese F-test100 can not be calculated because n is not givenecause we can't calculate MSEcorrelated, then:th, which of the able?n is0s), and I is an r female ale professors ate = s e
MSIS 361A Final ExamPage 23 of 2712/04/201665 63 X ) andpeedsw.onshipults.
MSIS 361A Final ExamPage 24 of 2712/04/2016hip between X & Y.ail test.iatione valueso one.o one.tots.
SUMMARY OUTPUTRegression StatisticsMultiple R###R Square###Adjusted R Square###Standard Error###Observations7 ANOVAdfSSMSFRegression1 199.515625 ######Residual5 10.484375 2.096875 Total6 210 CoefficientsStandard Errort StatP-valueIntercept50.65625 #########X-0.353125 #########RESIDUAL OUTPUTObservationPredicted YResiduals1 41.828125 ###2 38.296875 ###3 34.765625 2.234375 4 33 0 5 29.46875 ###6 27.703125 ###7 25.9375 ###
ignificance F###Lower 95%Upper 95%Lower 95.0%Upper 95.0%########################20 30 40 50 -3 -2 -1 0 1 2 3 X Residual PXResiduals20 30 40 0 10 20 30 40 50 X LY
60 70 80 Plot50 60 70 80 Line Fit PlotYPredicted YX

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