HCM 3001 Fall 2023 Class Exercises

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School

Humber College *

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Course

3001

Subject

Statistics

Date

Feb 20, 2024

Type

xlsx

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Uploaded by SargentBook16495

SUMMARY OUTPUT Regression Statistics Multiple R 0.60449274 R Square 0.36541148 Adjusted R S 0.290754 Standard Erro15.0194881 Observations 20 ANOVA df SS MS F Significance F Regression 2 2208.25463 1104.12732 4.89450633 0.02094999 Residual 17 3834.94537 225.585022 Total 19 6043.2 CoefficientsStandard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Intercept -30.5545893 17.9916466 -1.69826531 0.10768462 -68.5136456 7.40446695 -68.5136456 0.24659898 0.24583397 1.00311188 0.3298723 -0.27206537 0.76526333 -0.27206537 36.8256268 12.724272 2.89412446 0.01008738 9.97975957 63.671494 9.97975957 Medication Errors Case-Mix Index

# of Falls Intercepts + (Medication Error *20) + (CMI*1.5) y=a+ (b1*x1) + (b2*x2) 29.6158304 Upper 95.0% 7.40446695 0.76526333 63.671494

Example 5: Multiple Linear Regression Using linear regression, what is the prediction for Falls at a hospital with 20 medication errors and a CMI of 1.5 Hospital 1 26 6 1.392 2 43 5 1.392 3 8 26 0.889 4 16 17 0.889 5 18 12 1.38 6 11 9 1.38 7 24 21 1.628 8 28 7 1.628 9 8 1 1.08 10 20 26 1.08 11 22 44 1.72 12 86 39 1.72 13 18 3 1.22 14 14 6 1.334 15 17 4 1.689 16 46 4 1.689 17 8 24 1.12 18 16 36 1.12 19 29 40 1.505 20 26 25 1.505 Patient Falls Medication Errors Case-Mix Index

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Example 4: Linear Prediction of ED Visits Using linear regression, what is the prediction for total ED visits in Year 6? Year ED Visits 1 16,067 2 15,194 3 13,844 4 12,779 5 10,813 6 ?

SUMMARY OUTPUT Regression Statistics Multiple R 0.91081211 R Square 0.82957871 Adjusted R S 0.81253658 Standard Erro0.04141092 Observations 12 ANOVA df SS MS F Significance F Regression 1 0.08347636 0.08347636 48.6781134 3.82037E-05 Residual 10 0.01714864 0.00171486 Total 11 0.100625 CoefficientsStandard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Intercept 0.06040655 0.0274101 2.20380646 0.05210426 -0.00066695 0.12148005 -0.00066695 0.01557642 0.00223255 6.97697021 3.82037E-05 0.01060199 0.02055084 0.01060199 Revenue in $M (x)

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b a profit revenue intercept y=a+bx y 10 0.06040655 0.21617072 Upper 95.0% 0.12148005 0.02055084

Example 3: Linear Regression Using linear regression, what is the predicted profit for a clinic with $10M in revenues? Private Cosmetic Surgery Clinic Revenues and Profits Clinic x*y 1 $7.10 $0.16 1.14 50.41 2 $2.02 $0.11 0.22 4.08 3 $6.06 $0.13 0.79 36.72 4 $4.01 $0.16 0.64 16.08 5 $14.09 $0.26 3.66 198.53 6 $15.09 $0.28 4.23 227.71 7 $16.02 $0.24 3.84 256.64 8 $12.03 $0.21 2.53 144.72 9 $14.00 $0.27 3.78 196.00 10 $20.01 $0.44 8.80 400.40 11 $15.09 $0.35 5.28 227.71 12 $7.06 $0.18 1.27 49.84 Total $133 $2.79 36.18 1,808.84 Revenue in $M (x) Profit in $M (y) x 2 $0.00 $0.00 $0.05 $0.10 $0.15 $0.20 $0.25 $0.30 $0.35 $0.40 $0.45 $0.50 1 $0.00 $5.00 $10.00 $15.00 $20.00 $25.00

$5.00 $10.00 $15.00 $20.00 $25.00 Profit in $M (y) 1 2 3 4 5 6 7 8 9 10 11 12 Chart Title Revenue in $M (x) Profit in $M (y)

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Example 2: Weighted Moving Average Using a 3-period weighted moving average, what is the prediction for total ED visits in Year 6? Period Visits Weights 1 16,067 0.0 0 2 15,194 0.0 0 3 13,844 0.10 1,384 4 12,779 0.30 3,834 5 10,813 0.60 6,488 6 12,478 1.00 11,706 3-period Moving Average Prediction 3-period Weighted Moving Average Prediction

Example 1: Moving Average Using a 3-period moving average, what is the prediction for total ED visits in Year 6? Formula 1 Formula 2 Year ED Visits 1 16,067 2 15,194 3 13,844 4 12,779 5 10,813 6 ? 12,478 12,478 3-period Moving Average Prediction 3-period Moving Average Prediction