1.2 In-Class Activity

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Directions: LAB1a calculate the NPV, IRR, Payback period, Discounted pa Years Undiscounted Cash Inflows Intere Year 0 ($ Outlay) ($1,000,000) Year 1: $250,000 (1+.03)^1 Year 2: $250,000 (1+.03)^2 Year 3: $375,000 (1+.03)^3 Year 4: $375,000 (1+.03)^4 Year 5: $250,000 (1+.03)^5 A. NPV: $370,380.39 IS NPV GO B. IRR: 15% IS IRR GOO C. Undiscounted Payback Period: 3.7 IS SHORTER OR L D. Discounted Payback Period 3.5 IS SHORTER OR L E. Profitability Index 0.63 IS THIS A Project A Data Investment Required: ($1,000,000) Discount Rate: 3% Cash Inflows over 5 years: Year 1 $250,000 Year 2 $250,000 Year 3 $375,000 Year 4 $375,000 Year 5 $250,000 $1,500,000 Formula for Pro investment + NP Investment Formula for ex positive year + Profitability Ind
ayback period and profitability Index for this project per the data given below. Answer the qu est Factor Discounted Cash Inflows Undiscounted Payback Period ($1,000,000) 1.03 $242,718.45 ($750,000) 1.06 $235,648.98 ($500,000) 1.09 $343,178.12 ($125,000) 1.13 $333,182.64 $250,000 1.16 $215,652.20 OOD OR BAD? GOOD Good NPV = positive NPV Value OD OR BAD? GOOD Good IRR = IRR > Discount Rate LONGER PB BETTER? SHORTER LONGER PB BETTER? SHORTER A GOOD PI? BAD If PI < 1.00 = BAD ofitability Index: PV xact discount period: prior year negative/positive year positive dex Formula: -outlay + NPV -outlay
uestions. Complete everything in yellow. Discounted Payback Period ($757,281.55) ($521,632.58) ($178,454.45) $154,728.19
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Directions: LAB1a calculate the NPV, IRR, Payback period, Discounted pa Years Undiscounted Cash Inflows Intere Year 0 ($ Outlay) ($1,000,000) Year 1: $250,000 (1+.03)^1 Year 2: $250,000 (1+.03)^2 Year 3: $375,000 (1+.03)^3 Year 4: $375,000 (1+.03)^4 Year 5: $250,000 (1+.03)^5 A. NPV: $370,380.39 IS NPV GO B. IRR: 15% IS IRR GOO C. Undiscounted Payback Period: 3.7 IS SHORTER OR L D. Discounted Payback Period 3.5 IS SHORTER OR L E. Profitability Index 0.63 IS THIS A Project A Data Investment Required: ($1,000,000) Discount Rate: 3% Cash Inflows over 5 years: Year 1 $250,000 Year 2 $250,000 Year 3 $375,000 Year 4 $375,000 Year 5 $250,000 $1,500,000 Formula for Pro investment + NP Investment Formula for ex positive year + Profitability Ind
ayback period and profitability Index for this project per the data given below. Answer the qu est Factor Discounted Cash Inflows Undiscounted Payback Period ($1,000,000) 1.03 $242,718.45 ($750,000) 1.06 $235,648.98 ($500,000) 1.09 $343,178.12 ($125,000) 1.13 $333,182.64 $250,000 1.16 $215,652.20 OOD OR BAD? GOOD Good NPV = positive NPV Value OD OR BAD? GOOD Good IRR = IRR > Discount Rate LONGER PB BETTER? SHORTER LONGER PB BETTER? SHORTER A GOOD PI? BAD If PI < 1.00 = BAD ofitability Index: PV xact discount period: + prior year negative/positive year positive dex Formula: ( -outlay + NPV) -outlay
uestions. Complete everything in yellow. Discounted Payback Period ($757,281.55) ($521,632.58) ($178,454.45) $154,728.19
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Lab#1b Descriptive Statistics Directions: Data Set #1 Data Set#2 1. For each Data Set, run 6657536 8500000 2. Use the data analysis 5263222 7500000 3. Place the 2 sets of resu 3899355 5000000 4. Calculate the differenc 3831276 4980000 2863562 3500000 Statistics 2420101 3200000 Mean 1520398 2800000 Standard Error 1208031 2300000 Median 982625 1800000 Mode 867270 1500000 Standard Deviation 755674 1200000 Sample Variance 368113 1000000 Kurtosis Skewness Range Minimum Maximum Sum Count
n descriptive statistics toolkit ults in field below. ces. Data Set #1 Data Set#2 Differences 2,553,097 3,606,666.67 1,053,570 576,959 705,787 128,828 1,970,249 3,000,000 1,029,751 #N/A #N/A #N/A 1,998,644 2,444,918 446,274 3,994,579,727,777 5,977,624,242,424 1,983,044,514,647 (0) 0 0 1 1 0 6,289,423 7,500,000 1,210,577 368,113 1,000,000 631,888 6,657,536 8,500,000 1,842,464 30,637,161 43,280,000 12,642,839 12 12 -
Lab#1b Descriptive Statistics DATA SETS Data Set #1 Data Set#2 6657536 8500000 5263222 7500000 3899355 5000000 3831276 4980000 2863562 3500000 2420101 3200000 1520398 2800000 1208031 2300000 982625 1800000 867270 1500000 755674 1200000 368113 1000000
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Directions: 1. For each Data Set, run descriptive statistics 2. Use the data analysis toolkit 3. Place the 2 sets of results in field below. 4. Calculate the differences. Statistics Data Set #1 Data Set#2 Differences Mean 2,553,097 3,606,667 1,053,570 Standard Error 576,959 705,787 128,828 Median 1,970,249 3,000,000 1,029,751 Mode #N/A #N/A #N/A Standard Deviation 1,998,644 2,444,918 446,274 Sample Variance 3,994,579,727,777 5,977,624,242,424 1,983,044,514,647 Kurtosis (0) 0 0 Skewness 1 1 0 Range 6,289,423 7,500,000 1,210,577 Minimum 368,113 1,000,000 631,888 Maximum 6,657,536 8,500,000 1,842,464 Sum 30,637,161 43,280,000 12,642,839 Count 12 12 -
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Lab1c - Inferential Statistics Effect Cause Y X Dependent Variable Independent Variable Higher Project success Higher Project Budget $1,000,000 $300,000 $750,000 $200,000 $333,555 $125,000 Step 1 $1,500,000 $275,000 $2,000,000 $400,000 $890,000 $189,000 $780,000 $89,000 $2,500,000 $890,000 $1,000,000 $275,000 Step 2 $750,000 $200,000 $950,000 $333,000 Step 3 $1,500,000 $375,000 $1,250,000 $333,000 Step 4
Step 5 Step 6 Step 7
Directions: complete the fields flagged in yellow below: State Your Hypothesis (done): Spending more money on our project will result in State Your Testing Method: Simple Linear Regr State the Null Ho: Spending more $ DOES NOT result in State the Alternative Ha: Spending more $ will result in hig Gather Variable Data (done) Set threshold criteria in terms of percentage. (Done Below) Decision Rules: A. B. Run the testing using Simple Regression through Excel. Put Results within Bo NOTE: You will use the data analysis toolpak in Excel. Click "data" and you should see the analysis toolpak on the far right. If you don’t see it --> you must do an Add-In SUMMARY OUTPUT Regression Statistics Multiple R 0.883652225716616 R Square 78.08% Adjusted R Square 0.760917733833376 Standard Error 284800.613012225 if testing shows a correlation of 75% or more, we conclude that there is a st the alternative Hypothesis and REJECT the Null. If testing shows a correlation of <75%, we can conclude insufficient evidenc and ACCEPT the Null.
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Observations 13 ANOVA df SS Regression 1 3178911761898.78 Residual 11 892225280893.532 Total 12 4071137042792.31 Coefficients Standard Error Intercept 375924.319714866 149359.244585599 Higher Project Budget 2.5894926816533 0.413634134573787 Indicate the Rsquared 78.08% Accept Null? Reject Null? YES State Final Conclusion Below: . Per our simple linear regression testing model, we find that there is an Rsquared of 78.08%. Therefore, there i and accept the alternative which shows that there is a strong relationship between spending more money on a
higher project success. ression n higher project success gher project success. ox. trong correlation for ce for the alternative
MS F Significance F 3178911761898.78 39.191928462 6.17E-05 81111389172.1393 t Stat P-value Lower 95% Upper 95% er 95 Upper 95.0% 2.51691363837489 0.0286322641 47186.839 704661.8 ### 704661.80057 6.26034571426641 6.17017E-05 1.6790901 3.4998953 1.7 3.4998952736 is sufficient statistical evidence to reject the Null hypothesis project budget and project success.
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Lab1c - Inferential Statistics Effect Cause Y X Dependent Variable Independent Variable Higher Project success Higher Project Budget $1,000,000 $300,000 $750,000 $200,000 $333,555 $125,000 Step 1 $1,500,000 $275,000 $2,000,000 $400,000 $890,000 $189,000 $780,000 $89,000 $2,500,000 $890,000 $1,000,000 $275,000 Step 2 $750,000 $200,000 $950,000 $333,000 Step 3 $1,500,000 $375,000 $1,250,000 $333,000 Step 4
Step 5 Step 6 Step 7
Directions: complete the fields flagged in yellow below: State Your Hypothesis (done): Spending more $ will result in a higher probabilit State Your Testing Method: Simple Linear Regr State the Null Ho: Spending more $ DOES NOT result in State the Alternative Ha: Spending more $ will result in hig Gather Variable Data (done) Set threshold criteria in terms of percentage. (Done Below) Decision Rules: A. B. Run the testing using Simple Regression through Excel. Put Results within Bo NOTE: You will use the data analysis toolpak in Excel. Click "data" and you should see the analysis toolpak on the far right. If you don’t see it --> you must do an Add-In SUMMARY OUTPUT Regression Statistics Multiple R 0.883652225716616 R Square 78.08% Adjusted R Square 0.760917733833376 Standard Error 284800.613012225 Observations 13 if testing shows a correlation of 75% or more, we conclude that there is a s the alternative Hypothesis and REJECT the Null. If testing shows a correlation of <75%, we can c onclude insufficient eviden and ACCEPT the Null.
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ANOVA df SS Regression 1 3178911761898.78 Residual 11 892225280893.532 Total 12 4071137042792.31 Coefficients Standard Error Intercept 375924.319714866 149359.244585599 Higher Project Budget 2.5894926816533 0.413634134573787 Indicate the Rsquared 78.08% Accept Null? Reject Null? Yes State Final Conclusion Below: . Per our simple linear regression testing model, we find that there is an Rsquared of 78.08%. Therefore, there i and accept the alternative which shows that there is a strong relationship between spending more money on a
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ty of project success. ression n higher project success gher project success. ox. strong correlation for nce for the alternative
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MS F Significance F 3178911761898.78 39.191928462 6.17E-05 81111389172.1393 t Stat P-value Lower 95% Upper 95% er 95 Upper 95.0% 2.51691363837489 0.0286322641 47186.839 704661.8 ### 704661.80057 6.26034571426641 6.17017E-05 1.6790901 3.4998953 1.7 3.4998952736 is sufficient statistical evidence to reject the Null hypothesis project budget and project success.
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