Please answer the 3 part file questions. 4(1), 5(1), and 6. Thanks

Please answer the 3 part file questions. 4(1), 5(1), and 6. Thanks


Microsoft Word - Assignment#4.docx 1.The weight and systolic blood pressure of 26 randomly selected males in the age group 25 to 30 are shown in the following table. Assume that weight and blood pressure are jointly normally distributed. Subject Weight Systolic BP Subject Weight Systolic BP 1 165 130 14 172 153 2 167 133 15 159 128 3 180 150 16 168 132 4 155 128 17 174 149 5 212 151 18 183 158 6 175 146 19 215 150 7 190 150 20 195 163 8 210 140 21 180 156 9 200 148 22 143 124 10 149 125 23 240 170 11 158 133 24 235 165 12 169 135 25 192 160 13 170 150 26 187 159 (a) Find a regression line relating systolic blood pressure to weight. (b) Test for significance of regression using α = 0.05 (c) Estimate the correlation coefficient. 2.An engineer at a semiconductor company wants to model the relationship between the device HFE (y) and three parameters: Emitter-RS (x1), Base-RS (x2), and Emitter-to- Base RS (x3). The data are shown in the following table. X1 X2 X3 y X1 X2 X3 y 14.620 226.000 7.000 128.400 15.500 230.200 5.750 97.520 15.630 220.000 3.375 52.620 16.120 226.500 3.750 59.060 14.620 217.400 6.375 113.900 15.130 226.600 6.125 111.800 15.000 220.000 6.000 98.010 15.630 225.600 5.375 89.090 14.500 226.500 7.625 139.900 15.380 229.700 5.875 101.000 15.250 224.100 6.000 102.600 14.380 234.000 8.875 171.900 16.120 220.500 3.375 48.140 15.500 230.000 4.000 66.800 15.130 223.500 6.125 109.600 14.250 224.300 8.000 157.100 15.500 217.600 5.000 82.680 14.500 240.500 10.870 208.400 15.130 228.500 6.625 112.600 14.620 223.700 7.375 133.400 (a) Fit a multiple linear regression model to the data. (b) Predict HFE (y) when x1 = 14.5, x2 = 220, and x3 = 5.0. Microsoft Word - Assignment#5.docx Q1 ASixSigmateamistestingpilottestresultstoseeiftheyhavemadeaststisticallysignificantimprovement. Theyneedtotestthishypothesisstatisticallyanddeterminethe95%ConfidenceIntervalfortheimprovement. Datafromthebaselineandpilottestarereportedinthetablebelow: Defect Defects Good Sample Rate Baseline 6,290 613,710 620,000 0.01015 PilotTest 466 48,534 49,000 0.00951 Q1.1 Statethehypothesis. Q1.2 Testthehypothesisandstatetheresultsinstatisticalandbusinessstatements. Q1.3 Statethe95%confidenceintervalforthebaselineprocessdefectratein%. Q1.4 Statethe95%confidenceintervalfortheimprovedprocessdefectratein%. Q1.5 ComparetheresultsfromtheConfidenceIntervaltothehypothesistest.Dotheyagree?Whyorwhynot? Q2. Conductaregressionanalysisforthefollowingdata. Q2.1 TestthehypothesisthatXisagoodpredictorofYandexplainwhyorwhynot. Q2.2 Explainthevariousparametersofthelinearequation. Q2.3 Explainthestrengthofthemodelusingtheregressionmodelandalsousingacorrelationanalysis. Q2.4 Showthelinearlineonthescatterplotgraphandthelinearequation. Q2.5 DeterminethepredictedvalueofYgivenXis93.1 Q2.6 PlotthenormalprobabilityplotandtheresidualplotvsX.Whatdoyouinferfromthem? X Y 78.4 -9 89.9 -18.2 54.2 -33.5 58.3 -25.6 98.3 -1.5 57.8 -9 63.7 4.1 66 -35.2 67.1 -20.3 97.3 -40.3 76.5 0.3 86.1 -16.1 62.7 -8.7 81.9 -5.5 88 -14.9 60.9 -19.8 60.7 0.9 70.1 -38.5 86.7 -5.8 94.4 -33.7 61.5 -38.4 72.4 -26.2 63.9 -3.1 97.4 -43.6 Q3 Q3.1 Explainhownuisance(noise)factorscanaffectdataanalysisandexperimentsintheImprovephase. Q3.2 Explainhowyouwouldcombatnoisewhenitcanbecontrolled. Q3.3 Explainhowyouwouldcombatnoisewhenitcan’tbecontrolled. Q3.4 ExplainhowyouwoulduseresidualanalysisinanANOVAexperiment. Microsoft Word - Assignment#6.docx 1. Thefollowingmeasurementsrepresentthelength(incm)oftheelectricalcontacts ofrelaysinsamplesofsize5,takenhourlyfromtheoperatingprocess.CreatetheX- barandRchartsanddecideiftheprocessisincontrol. Houri X1 X2 X3 X4 X5 1 1.9890 2.1080 2.0590 2.0110 2.0070 2 1.8410 1.8900 2.0590 1.9160 1.9800 3 2.0070 2.0970 2.0440 2.0810 2.0510 4 2.0940 2.2690 2.0910 2.0970 1.9670 5 1.9970 1.8140 1.9780 1.9960 1.9830 6 2.0540 1.9700 2.1780 2.1010 1.9150 7 2.0920 2.0300 1.8560 1.9060 1.9750 8 2.0330 1.8500 2.1680 2.0850 2.0230 9 2.0960 2.0960 1.8840 1.7800 2.0050 10 2.0510 2.0380 1.7390 1.9530 1.9170 11 1.9520 1.7930 1.8780 2.2310 1.9850 12 2.0060 2.1410 1.9000 1.9430 1.8410 13 2.1480 2.0130 2.0660 2.0050 2.0100 14 1.8910 2.0890 2.0920 2.0230 1.9750 15 2.0930 1.9230 1.9750 2.0140 2.0020 16 2.2300 2.0580 2.0660 2.1990 2.1720 17 1.8620 2.1710 1.9210 1.9800 1.7900 18 2.0560 2.1250 1.9210 1.9200 1.9340 19 1.8980 2.0000 2.0890 1.9020 2.0820 20 2.0490 1.8790 2.0540 1.9260 2.0080 2. Followingisthenumberofdefectsindailysamples(n=100)inJanuary.CreatetheP andCchartsanddecideiftheprocessisincontrol. SampleDay NumberofDefects SampleDay NumberofDefects i Xi i Xi 1 6 16 6 2 8 17 4 3 8 18 6 4 13 19 8 5 6 20 2 6 6 21 7 7 9 22 4 8 7 23 4 9 1 24 2 10 8 25 1 11 5 26 5 12 2 27 15 13 4 28 1 14 5 29 4 15 4 30 1 31 5