SOkema Assignment 2 Data Solutions (1)

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School

University of Windsor *

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75-290

Subject

Health Science

Date

Oct 30, 2023

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xlsx

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Uploaded by CoachLyrebirdMaster991

Consumer Transportation Survey Type Gender Truck Domestic Yes Male Count - Gender Truck Domestic Yes Male Gender Truck Foreign No Male Female Truck Domestic No Male Male SUV Domestic Yes Male Total Result SUV Foreign Yes Male SUV Domestic Yes Male Joint Probability SUV Foreign Yes Male Female SUV Domestic Yes Male Male SUV Domestic Yes Male Grand Total SUV Foreign Yes Male SUV Domestic Yes Male P(Vehicle driven/Gend SUV Foreign No Female Condition probability SUV Foreign Yes Female Female SUV Foreign Yes Female Male SUV Domestic Yes Male SUV Domestic No Female Let, say SUV Domestic No Female Probility of Female SUV Domestic Yes Male Probility of Male Mini Van Domestic Yes Female Probility of Car Mini Van Domestic No Female Probility of Mini Van Mini Van Domestic Yes Female Probility of SUV Mini Van Foreign Yes Female Probility of Truck Mini Van Foreign Yes Female Mini Van Domestic No Male a Mini Van Domestic Yes Female b Mini Van Foreign Yes Female c Mini Van Foreign Yes Female d Car Domestic Yes Female e Car Domestic No Female f Car Domestic Yes Female Car Foreign Yes Male g) Female Car Domestic No Male Car Domestic Yes Female Car Domestic Yes Female Car Domestic No Male Car Domestic No Male g) Male Car Foreign No Female Car Foreign Yes Female Car Domestic No Female Vehicle Driven Satisfaction with vehicle

Car Domestic Yes Female Car Foreign Yes Female Car Foreign Yes Male Car Foreign Yes Male Car Domestic Yes Female Car Foreign Yes Female Car Foreign Yes Female Car Domestic No Female Car Domestic Yes Female Car Foreign No Female

Vehicle Driven Car Mini Van SUV Truck Total Result 16 8 5 29 6 1 10 4 21 22 9 15 4 50 Car Mini Van SUV Truck Grand Total 0.32 0.16 0.1 0 0.58 0.12 0.02 0.2 0.08 0.42 0.44 0.18 0.3 0.08 1 der) Car Mini Van SUV Truck 0.552 0.276 0.172 0 0.286 0.048 0.476 0.190 P(F) P(M) P(C) P(V) P(S) P(T) P(F) 0.58 P(S) 0.3 P(M / Van) 0.02 P(F / Tor S 0.1 P(C/F) 0.552 P(S/M) 0.476 P(C) 0.44 ≠ 0.552 P(C/F) P(V) 0.18 ≠ 0.276 P(V/F) P(S) 0.3 ≠ 0.172 P(S/F) P(T) 0.08 ≠ 0 P(T/F) P(C) 0.44 ≠ 0.286 P(C/M) P(V) 0.18 ≠ 0.048 P(V/M) P(S) 0.3 ≠ 0.476 P(S/M) P(T) 0.08 ≠ 0.190 P(T/M) Since the probabilitie equal to the probabil type, this mean that are not independent Gender. If the Proba driven are equal, the

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es of each gender are not lity of each vehicle driven t Gender and vehicle driven t or Vehicle driven depends on ability Gender and Vehicle en it will be independent.

Cost of Living Adjustments City Comparative Salary Groceries Housing Utilities Atlanta $60,482.00 15% 25% -10% Austin $57,530.00 -8% 13% -1% Boston $85,904.00 16% 141% 43% Charleston $60,904.00 18% 22% 11% Charlotte $58,012.00 11% 5% 2% Chicago $70,000.00 23% 73% 1% Columbus $54,578.00 1% -1% -5% Dallas $58,072.00 12% -4% -3% DC $87,892.00 20% 214% -6% Denver $65,843.00 7% 64% -8% Detroit $57,590.00 -2% 15% 0% Indianapolis $55,120.00 3% 4% -11% Los Angeles $83,795.00 16% 168% 10% Lousiville $55,602.00 1% 2% -13% Minneapolis $65,060.00 16% 43% -10% New Orleans $57,530.00 10% 24% -17% New York $136,024.00 37% 479% 26% Philadelphia $72,048.00 26% 72% 17% Phoenix $57,651.00 7% 21% -8% Pittsburgh $59,578.00 9% 21% -3% Portland $77,349.00 25% 108% -18% San Diego $86,446.00 18% 187% 18% San Francisco $105,241.00 38% 304% 3% Seattle $83,253.00 33% 133% 2% St. Louis $56,084.00 14% -9% 13% The function for comparable salary for the city adjustments (Regression Mo 55278.32+12020.56xGroceries+14337.28xHousing+6711.18xUtilities+5162.17xT ( Intercept Groceries Housing Utilities Coefficients 55,278.32 12,020.56 14,337.28 6,711.18 City Adjestment (B) 4% 9% 2% (B x A) 55,278.32 480.82 1,290.36 134.22 comparable salary for the city would be The true comparable salary for the city lie bettween limit of confidence 95% Lower limit 95%

( Intercept Groceries Housing Utilities Coefficients 54,297.75 3,864.00 13,401.14 1,381.38 City Adjestment (B) 4% 9% 2% (B x A) 54,297.75 154.56 1,206.10 27.63 comparable salary for the city would be Upper limit 95% ( Intercept Groceries Housing Utilities Coefficients 56,258.89 20,177.12 15,273.43 12,040.98 City Adjestment (B) 4% 9% 2% (B x A) 56,258.89 807.08 1,374.61 240.82 comparable salary for the city would be

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Transportation Healthcare 6% 5% SUMMARY OUTPUT -1% 6% 11% 33% Regression Statistics -7% 10% Multiple R 0.998025184 -2% 6% R Square 0.9960542678 18% 2% Adjusted R Square 0.995015917 -6% -1% Standard Error 1362.845367 1% 5% Observations 25 13% -3% 1% 11% ANOVA 6% -1% df -6% 1% Regression 5 29% 13% Residual 19 5% -8% Total 24 10% 8% -5% 1% Coefficients 30% 19% Intercept 55278.317535 10% 3% Groceries 12020.560178 2% 1% Housing 14337.282874 14% -1% Utilities 6711.1804883 19% 15% Transportation 5162.170869 27% 15% Healthcare 12307.680282 26% 22% 21% 24% 1% 4% RESIDUAL OUTPUT odel) Transportation+12307.68xHealhcare Observation ted Comparative 1 60919.7185 (A) 2 56800.2468 Transportation Healthcare 3 84932.35691 5,162.17 12,307.68 4 62203.86652 1% 8% 5 58086.88431 51.62 984.61 6 69751.71904 7 54486.78423 $58,219.96 8 56612.96375 9 88263.3959 10 66164.18989 % 11 57375.15221 12 55287.54235

(A) 13 85056.38843 Transportation Healthcare 14 54086.30945 - 3,350.70 3,316.36 15 64196.35226 1% 8% 16 58645.38902 - 33.51 265.31 17 134033.5272 18 70752.85503 $55,917.84 19 58820.01193 20 59769.28906 21 75386.6751 (A) 22 88700.68801 Transportation Healthcare 23 107682.6598 13,675.04 21,299.00 24 82485.81138 1% 8% 25 57087.222885 136.75 1,703.92 $60,522.07

This means that all slopes and intercept are statistically significant since P-Value<0.05 SS MS F Significance F 8908450325.91781690065 959.26587284 3.8376502E-22 35289602.389 1857347.49 8943739928.24 Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% 468.49327002 117.9917 1.082615E-28 54297.74985 56258.8852 54297.74985 56258.88522 3897.0214513 3.08455068 0.006101555 3864.00054 20177.1198 3864.00054 20177.11982 447.26919181 32.0551541 5.25963E-18 13401.1377 15273.4281 13401.1377 15273.42805 2546.46017 2.635494 0.0163000501 1381.378099 12040.9829 1381.378099 12040.98288 4067.2572595 1.26920196 0.21969676494 -3350.696411 13675.0381 -3350.69641 13675.03815 4295.8523097 2.865014762 0.00991102003 3316.3580639 21299.0025 3316.358064 21299.0025 PROBABILITY OUTPUT Residuals andard Residuals Percentile mparative Salary -437.7184974 -0.360975 2 54578 729.75320247 0.6018084 6 55120 971.64308569 0.80128867 10 55602 -1299.86652 -1.071966 14 56084 -74.88430741 -0.0617551 18 57530 248.2809593 0.20475082 22 57530 91.215771543 0.07522326 26 57590 1459.0362506 1.20322908 30 57651 -371.3958959 -0.3062805 34 58012 -321.1898873 -0.2648769 38 58072 214.84778835 0.17717936 42 59578 -167.5423521 -0.1381678 46 60482 This means that 99.6% of the variation in comparable salary can be explained by spending on Groceries, Housing, Utilities transport & healthcare

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-1261.388429 -1.0402341 50 60904 1515.6905486 1.2499504 54 65060 863.64774026 0.71222773 58 65843 -1115.389019 -0.9198322 62 70000 1990.4727917 1.64149092 66 72048 1295.1449713 1.06807223 70 77349 -1169.011932 -0.9640536 74 83253 -191.2890585 -0.1577511 78 83795 1962.3248973 1.61827808 82 85904 -2254.688006 -1.8593823 86 86446 -2441.659842 -2.0135731 90 87892 767.18862406 0.63268042 94 105241 -1003.22288454 -0.82733171 98 136024

-20% -10% 0% 10% 20% 30% 40% -3000 -2000 -1000 0 1000 2000 3000 Groceries Residual Plot Groceries Residuals -30% -20% -10% 0% 10% 20% 30% 40% -3000 -2000 -1000 0 1000 2000 3000 Utilities Residual Plot Utilities Residuals -20% -10% 0% 10% 20% 30% -3000 -2000 -1000 0 1000 2000 3000 Healthcare Residual Plot Healthcare Residuals

50% -100% 0% 100% 200% 300% 400% 500% 600% -3000 -2000 -1000 0 1000 2000 3000 Housing Residual Plot Housing Residuals 50% -10% -5% 0% 5% 10% 15% 20% 25% 30% 35% -3000 -2000 -1000 0 1000 2000 3000 Transportation Residual Plot Transportation Residuals 40%

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Job Satisfaction 1=Low, 10=High Department Years Maintenance Satisfaction Management Administrative 16 0 3 0 Administrative 2 0 9 0 Administrative 14 0 6 0 Maintenance 17 1 8 0 Maintenance 15 1 9 0 Management 1 0 9 1 Management 3 0 8 1 Management 3 0 3 1 Production 16 0 5 0 Production 15 0 4 0 Production 13 0 8 0 Production 3 0 10 0 Production 6 0 4 0 Production 1 0 9 0 Production 3 0 7 0 Production 2 0 8 0 Production 3 0 6 0 Production 2 0 6 0 Production 2 0 8 0 Production 15 0 7 0 Production 5 0 7 0 Production 8 0 8 0 Production 17 0 6 0 Production 15 0 9 0 Production 5 0 3 0 Quality Control 1 0 10 0 Quality Control 11 0 7 0 Shipping / Receiving 21 0 5 0 Shipping / Receiving 8 0 4 0 Shipping / Receiving 32 0 5 0 Shipping / Receiving 2 0 10 0 Shipping / Receiving 18 0 8 0 Regression Model Years of Service Overall Job satisfaction

Y = 7.332-0.125(Year)+3.166(Maintanance)-0.374(Management)+0.395(Product

Production Shipiping/Receiving Quality Control 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 0 0 1 0 0 1 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0

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tion)+1.917(Quality Control)+1.091(Shipping/Receiving)

SUMMARY OUTPUT Regression Statistics Multiple R 0.487659 R Square 0.237811 Adjusted R Square 0.054886 Standard Error 2.096873 Observations 32 ANOVA df SS MS F ignificance F Regression 6 34.29685 5.716142 1.300046 0.293435 Residual 25 109.9219 4.396876 Total 31 144.21875 Coefficients tandard Erro t Stat P-value Lower 95%Upper 95% Intercept 7.331549 1.36211 5.382494 1.39E-05 4.526231 10.13687 Years -0.12483 0.058526 -2.13296 0.042929 -0.24537 -0.0043 Maintenance 3.165775 1.939457 1.6323 0.11515 -0.82861 7.160161 Management -0.37361 1.780201 -0.20987 0.8354721 -4.04 3.292787 Production 0.395103 1.324497 0.298304 0.767936 -2.33275 3.122955 Quality Control 1.917447 1.933561 0.991666 0.330862 -2.0648 5.89969 Shipiping/Receiving 1.0907412 1.5652074 0.6968668 0.4923157 -2.132864 4.3143462 SUMMARY OUTPUT Regression Statistics Multiple R 0.48628 R Square 0.236469 Adjusted R Square 0.089636 Standard Error 2.057963 Observations 32 ANOVA df SS MS F ignificance F

Regression 5 34.10319 6.820638 1.610459 0.192428 Residual 26 110.1156 4.235214 Total 31 144.21875 Coefficients tandard Erro t Stat P-value Lower 95%Upper 95% Intercept 7.122873 0.913675 7.795848 2.86E-08 5.244787 9.00096 Years -0.12147 0.055242 -2.19883 0.036986 -0.23502 -0.00792 Maintenance 3.32061 1.760366 1.886317 0.070469 -0.29788 6.939095 Production 0.577848 0.979508 0.589937 0.5603254 -1.43556 2.591257 Quality Control 2.105933 1.680547 1.253123 0.221314 -1.34848 5.560347 Shipiping/Receiving 1.2449035 1.3564824 0.917744 0.3671884 -1.543386 4.033193 It appears that Maintencnce are the significant variables. SUMMARY OUTPUT Regression Statistics Multiple R 0.475656 R Square 0.226248 Adjusted R Square 0.111618 Standard Error 2.032965 Observations 32 ANOVA df SS MS F ignificance F Regression 4 32.62923 8.157307 1.973727 0.12707 Residual 27 111.5895 4.132945 Total 31 144.21875 Coefficients tandard Erro t Stat P-value Lower 95%Upper 95% Intercept 7.533597 0.584493 12.8891 4.75E-13 6.334315 8.732878 Years -0.11925 0.054445 -2.19032 0.037315 -0.23096 -0.00754 Maintenance 2.874425 1.5703 1.830494 0.078236 -0.34757 6.096415 Quality Control 1.681911 1.500634 1.1208 0.272243 -1.39714 4.760959 Shipiping/Receiving 0.7982749 1.1118837 0.7179483 0.4789558 -1.483122 3.0796718 SUMMARY OUTPUT Regression Statistics Multiple R 0.459866 R Square 0.211477 Adjusted R Square 0.126992

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Standard Error 2.015297 Observations 32 ANOVA df SS MS F ignificance F Regression 3 30.4989 10.1663 2.503138 0.079696 Residual 28 113.7198 4.061423 Total 31 144.21875 Coefficients tandard Erro t Stat P-value Lower 95%Upper 95% Intercept 7.525009 0.579293 12.99 2.24E-13 6.338382 8.711636 Years -0.10239 0.048693 -2.1028 0.0445981 -0.20213 -0.00265 Maintenance 2.613254 1.514308 1.725708 0.0954216 -0.48867 5.715174 Quality Control 1.5893397 1.4820918 1.0723625 0.2927133 -1.446588 4.6252672 It appears that year and Maintencnce are the significant variables. SUMMARY OUTPUT Regression Statistics Multiple R 0.423193 R Square 0.179092 Adjusted R Square 0.122478 Standard Error 2.020501 Observations 32 ANOVA df SS MS F ignificance F Regression 2 25.82842 12.91421 3.163368 0.057183 Residual 29 118.3903 4.082425 Total 31 144.21875 Coefficients tandard Erro t Stat P-value Lower 95%Upper 95% Intercept 7.675546 0.563478 13.62174 3.93E-14 6.523105 8.827987 Years -0.10748 0.048586 -2.21207 0.034995 -0.20685 -0.00811 Maintenance 2.5440816 1.5168405 1.6772243 0.1042467 -0.558205 5.6463688 Maintanance has the heigghest impact on satisfaction

Lower 95.0% Upper 95.0% 4.526231 10.13687 -0.24537 -0.0043 -0.82861 7.160161 -4.04 3.292787 -2.33275 3.122955 -2.0648 5.89969 -2.132864 4.3143462

Lower 95.0% Upper 95.0% 5.244787 9.00096 -0.23502 -0.00792 -0.29788 6.939095 -1.43556 2.591257 -1.34848 5.560347 -1.543386 4.033193 Lower 95.0% Upper 95.0% 6.334315 8.732878 -0.23096 -0.00754 -0.34757 6.096415 -1.39714 4.760959 -1.483122 3.0796718

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Lower 95.0% Upper 95.0% 6.338382 8.711636 -0.20213 -0.00265 -0.48867 5.715174 -1.446588 4.6252672 Lower 95.0% Upper 95.0% 6.523105 8.827987 -0.20685 -0.00811 -0.558205 5.6463688

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Auto Survey Gender Type Purchased Vehicle AgePurchased Interaction Male Mid-size Used 127233 15 0 0 Female Mid-size New 23970 1 1 1 Male Small New 77392 7 1 7 Female Large SUV Used 185397 14 0 0 Female Small New 26001 2 1 2 Female Minivan New 180643 9 1 9 Male Small Used 72083 6 0 0 Male Small New 165353 11 1 11 Male Small Used 205288 13 0 0 Female Small New 142897 7 1 7 Male Minivan Used 182584 14 0 0 Male Small SUV Used 140479 13 0 0 Female Small New 22114 2 1 2 Female Mid-size New 3454 0.25 1 0.25 Female Large SUV New 130905 7 1 7 Female Small Used 105628 10 0 0 Female Small New 48678 5 1 5 Male Mid-size New 6849 0.5 1 0.5 Female Small Used 137941 10 0 0 Female Small SUV New 29823 4 1 4 Male Small SUV Used 85763 14 0 0 Female Small Used 134172 12 0 0 Male Mid-size Used 86387 12 0 0 Mileage

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MPG 28.7 a) SUMMARY OUTPUT 43.4 24 Regression Statistics 15.2 Multiple R 0.471677857 37 R Square 0.22248 20 Adjusted R Square 0.144728 45.7 Standard Error 8.187515374 42 Observations 23 33 31 ANOVA 12 df SS MS 20 Regression 2 383.6309704 191.8154852 28 Residual 20 1340.70816 67.035408 28.3 Total 22 1724.33913 21 35 Coefficients Standard Error t Stat 30.4 Intercept 36.18090465 3.45283692 10.47860223 40.2 -8.0575E-06 4.8631E-05 -0.16568604 30 Vehicle Age -0.76640597 0.630580457 -1.2153976 24.9 21 31 b) SUMMARY OUTPUT 27 Regression Statistics Multiple R 0.534479011 R Square 0.285667813 Adjusted R Square 0.17287852 Standard Error 8.051645605 Observations 23 ANOVA df SS MS Regression 3 492.5881883 164.1960628 Residual 19 1231.750942 64.82899695 Total 22 1724.33913 Coefficients Standard Error t Stat Intercept 44.85544855 7.50344509 5.977980515 1.97525E-05 5.24147E-05 0.376849685 Vehicle Age -1.68200663 0.939863244 -1.78962912 Mileage Mileage

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Purchased -7.6035564 5.865072973 -1.29641292 R-Square changed by 0.06 and the intercept increased by 8.68 a Only a 28.57% variation in malles/gallon explains the variation i ANOVA indicates that the slope and the intercept are both not C) SUMMARY OUTPUT Regression Statistics Multiple R 0.647220765 R Square 0.418894718 Adjusted R Square 0.289760211 Standard Error 7.461093815 Observations 23 ANOVA df SS MS Regression 4 722.3165539 180.5791385 Residual 18 1002.022577 55.66792092 Total 22 1724.33913 Coefficients Standard Error t Stat Intercept 63.07630153 11.3488299 5.557956379 -1.9459E-05 5.22651E-05 -0.37230483 Vehicle Age -2.75768669 1.019265757 -2.70556199 Purchased -29.2806215 11.97511525 -2.44512231 Interaction 2.423136293 1.192814835 2.031443793 ANOVA indicates that the slopes and the intercept are both sta Both Vehicle age and purchase have P-Value<0.05 indicating tha Mileage variables.

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F Significance F 2.8614055 0.080745009 P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% 1.43324E-09 28.97841305 43.38339626 28.97841305 43.38339626 0.87006743 -0.0001095 9.3385E-05 -0.0001095 9.3385E-05 0.238367549 -2.08177376 0.548961812 -2.08177376 0.548961812 F Significance F 2.53275649 0.087647802 P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% 9.40919E-06 29.15055748 60.56033961 29.15055748 60.56033961 0.710461177 -8.9953E-05 0.000129458 -8.9953E-05 0.000129458 0.089465958 -3.64916301 0.285149749 -3.64916301 0.285149749

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0.210358976 -19.8792952 4.672182413 -19.8792952 4.672182413 and the P-Values are still greather than 0.05 in millage and age of the vehichle statistically significant F Significance F 3.243863531 0.036042637 P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% 2.82137E-05 39.23329466 86.9193084 39.23329466 86.9193084 0.714013127 -0.00012926 9.03464E-05 -0.00012926 9.03464E-05 0.014479569 -4.89908458 -0.61628879 -4.89908458 -0.61628879 0.024993977 -54.4394051 -4.12183795 -54.4394051 -4.12183795 0.057242639 -0.08287468 4.929147269 -0.08287468 4.929147269 atistically significant as P-Values <0.05 at both slope and intercept are statistically significant

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