SOLUTION: Melbourne Institute of Technology Financial Metrics for Decision Making Discussion

Completed the assignment. Name:Institution:Outline for Financial Metrics for Decision Making1. 1st and 2nd Paragraph•Part 1 – Hypothesis Testing2. 3rd Paragraph•Part 2 – Modelling3. 4th to 9th Paragraph•Part 3 – Simple Linear Regression4. 10th to 16th Paragraph•Part 4 – Multiple Linear Regression➢ Regression Model EquationRunning head: FINANCIAL METRICS1Financial Metrics for Decision MakingStudents NameInstitution NameFINANCIAL METRICS2Table of ContentsPart 1 – Hypothesis Testing ………………………………………………………………………………………………………….. 3Part 2 – Modelling ………………………………………………………………………………………………………………………. 3Part 3 – Simple Linear Regression…………………………………………………………………………………………………. 4Part 4 – Multiple Linear Regression ………………………………………………………………………………………………. 6Regression Model Equation ……………………………………………………………………………………………………. 6Reference …………………………………………………………………………………………………………………………………… 9FINANCIAL METRICS3Part 1 – Hypothesis TestingA null hypothesis is a hypothesis used to show no statistical difference between two values,while the alternative hypothesis is everything else. In this case, the null hypothesis is H0: P ≤89,000, which shows the salary mean in NSW differs from the national mean of $89,000. Thealternative hypothesis is everything else hence any value apart from $89,000; thus H1: P ≠89,000.Null hypothesisH0: P ≤ 89,000Alternative hypothesisH1: P ≠ 89,000Sample Mean85,272Sample Standard Deviation11,039Sample Size25Hypothesized Value of Mean-2Test Statistic-0.34p-value0.000The P-value of my hypothesis test is 0.000. since the p-value is less than 0.05, the nullhypothesis isn’t true and has to be rejected. This shows that the alternative hypothesis is true,thus H1: P ≠ 89,000. The annual mean salary of a campus manager in NSW is $85,272, whichisn’t equal to $89,000.Part 2 – ModellingBased on the calculations, the decision is made based on the interest rate’s effect on cashoutflows’ present value. Based on the results, the what-if analysis will be performed using aninterest rate of 1% because interest rates can influence the overall decision. Using an interest rateFINANCIAL METRICS4of 5% leasing is the best option because it has a lower present value of cash outflows. But if theinterest rate is changed to 1%, buying becomes the best option. One aspect to be improved is thedown payment, which doesn’t impact the change in decision. A significant down payment shouldhave the ability to change the decision whether to buy or lease.Part 3 – Simple Linear RegressionBased on the scatter plot, there is a negative relationship between Kms and price. Thisprovides an inversely proportional relationship. The higher the number in Kms, the lower thecar’s price but, the lower the number in the distance, the higher the price of the car.PriceScatter Plot80,00070,00060,00050,00040,00030,00020,00010,0000y = -0.1792x + 65973R² = 0.9047050,000100,000150,000200,000250,000300,000KmsBased on the scatter plot, the price is on the y axis while the Kms is on the x-axis, showingdependence and independence, respectively. The regression model equation is y = -0.1792x +65973 meaning the x value influences the y value. Therefore, buyers will use the equation todetermine the car’s price based on the distance traveled in Kms (Altman & Krzywinski, 2015).H0 : p = 0H1 : p ≠ 0FINANCIAL METRICS5Since x is the independent variable at a value of zero, the y-axis will read 65973. But if they-axis reads zero, the x-axis will read 368,152 km. This implies that if the vehicle has notcovered any distance, the price is fixed at $65,973. But if the distance covered is 368,152 km,then the car’s value will be lower and almost $0. Therefore, we reject the null hypothesis sincethe regression parameters are not equal to zero.KmPricePredicted Price113,40069,70063571.726,128221,10066,90062191.884,708315,90061,90063123.72-1,224462,00061,00054862.66,137543,00059,00058267.4733680,00049,00051637-2,637741,20054,80058589.96-3,7908152,00042,80038734.64,0659130,00046,900426774,22310120,00045,000444695311194,00043,00049128.2-6,12812111,90045,90045920.52-2113135,00035,50041781-6,28114167,00034,60036046.6-1,44715142,30034,90040472.84-5,57316220,00031,900265495,35117122,00036,50044110.6-7,61118206,50028,00028968.2-96819226,00027,00025473.81,52620275,00019,000166932,307DifferenceUsing the estimated regression model equation, the predicted price is the car’s price based ontheir distance covered in Kms. The true price is the actual price of the cars as indicated in theprice category. The difference is the amount in cash of the predicted price that deviates from theactual price. A bargain, in this case, occurs when the sale is much cheaper than expected.Therefore, when the actual price is lower than the expected price, that’s a bargain. Car number 17has the biggest bargain since the true price is $36,500 while the predicted value using theFINANCIAL METRICS6equation is $44,110.6 making a difference of $7,611. The second biggest bargain is car number13, where the true price is $35,500, yet the predicted price is $41,781 making a difference of$6,281 (Altman & Krzywinski, 2015).y = -0.1792(100,000) + 65973 = 48,053Based on the equation, the price is $48,053. However, based on the regression analysis, theactual equation of the table is given by y = -0.17202x + 64758.02.CoefficientsIntercept13400StandardErrort StatP-valueLower 95%Upper 95%Lower95.0%Upper95.0%64758.022004.232.311151.06E-1660529.5368986.5260529.5368986.52-0.172020.014003-12.28467.02E-10-0.20156-0.14247-0.20156-0.14247y = -0.17202(100,000) + 64758.02. = $47,556.02. I would not pay the predicted value of$48,053 for the car to the seller since the actual price would be $47,556.02.Part 4 – Multiple Linear RegressionThe first histogram’s overall shape explains that individuals with 0 years of post-high schoolhave a collective sum of $4,334,951.91 and individuals with at least one year or more have acollective sum of annual charges at $35,200,267.83. The second histogram shows that the malegender has a higher sum of annual charges than the female gender.Regression Model EquationAnnual Household Income & Annual Credit card Charges = y = 119.7616x + 3205.877. theequation shows a positive relation between Annual Household Income & Annual Credit cardCharges. Since the regression analysis shows R2 = 0.303982, this is a weak relationship betweenthe two variables. This implies that the annual household income doesn’t in any way impact theannual credit card charges.CoefficientsStandardErrort StatP-valueLower 95%Upper 95%Lower95.0%Upper95.0%FINANCIAL METRICSIntercept43.173205.877222.1400514.4317856.54E-462770.3413641.4142770.3413641.414119.76163.056616939.18108676.2E-279113.7687125.7545113.7687125.7545Household Size & Annual Credit card Charges = y = 604.2661x + 8483.116 and R2 =0.042122. this is a week relationship between the two variables hence household size doesn’timpact the annual credit card charges.CoefficientsIntercept3StandardErrort StatP-valueLower 95%Upper 95%Lower95.0%Upper95.0%8483.116243.3189934.86417435.5E-2298006.0558960.1768006.0558960.176604.266148.60330712.43261229.26E-35508.9725699.5596508.9725699.5596Years of Post-High School Education & Annual Credit card Charges = y = -514.8643 +1253.38 and R2 = 0.016144. this is also a week negative relationship between the two variableshence years of post-high school education doesn’t impact the dependent variable (Aiken et al.2012).CoefficientsIntercept3StandardErrort StatP-valueLower 95%Upper 95%Lower95.0%Upper95.0%12536.38198.7397463.0793883012146.7212926.0412146.7212926.04-514.86467.794573-7.59447733.94E-14-647.785-381.944-647.785-381.944Hours Per Week Watching Television & Annual Credit card Charges = y = 22.25113x +10569.98 and the R2 = 0.004062, thus showing a week relationship between the two variables.CoefficientsIntercept34StandardErrort StatP-valueLower 95%Upper 95%Lower95.0%Upper95.0%10569.98204.2281451.755731010169.5610970.3910169.5610970.3922.251135.87676173.786290760.00015510.7289233.7733410.7289233.77334Age & Annual Credit card Charges = y = -4.210753x + 11425.73 and R2 = 0.0000560. Thevalue is almost zero showing no relationship.CoefficientsIntercept34StandardErrort StatP-valueLower 95%Upper 95%Lower95.0%Upper95.0%11425.73432.3070526.42967531.5E-14010578.1412273.3310578.1412273.33-4.210759.4877124-0.44381120.657206-22.812714.39123-22.812714.39123Gender & Annual Credit card Charges = y = -138.3694x + 11302.37 and R2 = 0.00012868.The value is almost zero showing no relationship between the two variables.FINANCIAL METRICSCoefficientsIntercept18StandardErrort StatP-valueLower 95%Upper 95%Lower95.0%Upper95.0%11302.37138.8477981.4011256011030.1411574.611030.1411574.6-138.369205.72823-0.67258350.501257-541.728264.9894-541.728264.9894FINANCIAL METRICSReferenceAiken, L. S., West, S. G., Pitts, S. C., Baraldi, A. N., & Wurpts, I. C. (2012). Multiple linearregression. Handbook of Psychology, Second Edition, 2.Altman, N., & Krzywinski, M. (2015). Simple linear regression.9Student IDNameSurnameFinancial Metrics for Decision MakingSummer 2020AssignmentPart 1 – Hypothesis Testing [10 marks]12345678910111213141516171819202122232425Salary ($) Expected77,6008527276,0008527290,7008527297,2008527290,70085272101,8008527278,7008527281,3008527284,2008527297,6008527277,5008527275,7008527289,4008527284,3008527278,7008527284,6008527287,70085272103,4008527283,80085272101,3008527294,7008527269,2008527295,4008527261,5008527268,80085272Null hypothesisAlternative hypothesisSample MeanSample Standard DeviationSample SizeHypothesised Value of MeanTest Statisticp-valueH0: P ≤ 89,000H1: P ≠ 89,00085,27211,03925-2-0.340.000H0: P ≤ 89,000H1: P ≠ 89,000Part 2 – Modelling [40 marks]Present Cash flow to be determinedMonthly Discount rate 0.417%Monthly Interest = (5/12)% = 0.4167%Buy ScenarioPresent Value of Cash out FlowsLeasing ScenarioDiscount RateNumber of MonthsMonthly rentResidual ValuePresent Value of Cash out Flows$50,0000.42%36$850$25,000$49,885.25Advantage LeasingDecision: To LeasePresent Value is LowerThe change in down payment doesent change the decisionThe change in interest rate will influence the decisionIf the interest rate was 1%Lease ScenarioDiscount Rate = (1/12)%0.08%Number of months36Monthly rent$850Residual Value$25,000Present Value of Cash out Flows$54,394.63Buy option has a lower present valuePart 3 – Simple Linear Regression [20 marks]KmsPrice113,40069,70063571.726,128221,10066,90062191.884,708315,90061,90063123.72-1,224462,00061,00054862.66,137543,00059,00058267.4733680,00049,00051637-2,637741,20054,80058589.96-3,7908152,00042,80038734.64,0659130,00046,900426774,22310120,00045,000444695311194,00043,00049128.2-6,12812111,90045,90045920.52-2113135,00035,50041781-6,28114167,00034,60036046.6-1,44715142,30034,90040472.84-5,57316220,00031,900265495,35117122,00036,50044110.6-7,61118206,50028,00028968.2-96819226,00027,00025473.81,52620275,00019,000166932,307Predicted PriceDifferenceRegression StatisticsScatter Plot80,00070,000y = -0.1792x + 65973R² = 0.904760,000Price50,000SUMMARY OUTPUT40,00030,000Regression StatisticsMultiple R0.948028R Square0.898756Adjusted R Square 0.892801Standard Error4313.286Observations1920,00010,0000050,000100,000150,000200,000250,000KmsANOVAdfRegressionResidualTotalInterceptSSMS1 2.81E+09 2.81E+0917 3.16E+08 1860443418 3.12E+09FSignificance F150.912 7.02E-10CoefficientsStandard Error t StatP-value Lower 95%Upper 95%Lower 95.0%64758.022004.2 32.31115 1.06E-16 60529.53 68986.52 60529.5313400 -0.17202 0.014003 -12.2846 7.02E-10 -0.20156 -0.14247 -0.20156250,000300,000Upper 95.0%68986.52-0.14247Part 4 – Multiple Linear Regression [30 marks]3.0Years of PostHigh SchoolEducation3.0Hours Per WeekWatchingTelevision34.022.63.03.0338.73.0453.0593.26Annual Income($1000)Household Size143.12AgeGender34Female59.033Male0.011.047Male4.03.02.044Female5.04.08.052Female87.25.02.02.027Female7115.82.02.09.057Male846.16.02.028.036Female9104.07.04.032.062Male1084.72.05.040.067Male1115.54.04.048.037Female12119.45.05.028.051Female13118.36.04.015.060Female1447.42.02.047.053Female1532.43.01.034.035Female1686.52.01.057.033Female1786.22.04.030.049Female1850.34.03.010.057Female1933.08.04.026.022Female2041.65.00.028.046Female2140.22.01.038.041Male22100.46.05.034.047Male2398.21.04.049.035Female24109.06.02.059.051Female2510.46.01.02.047Female2662.53.01.017.065Male2729.61.01.010.061Female28103.23.01.058.046Female29103.66.01.051.046Female3070.45.04.046.042Female3161.65.01.045.040Female3233.06.03.026.059Male33110.55.05.01.040Male3483.76.03.046.056Male3543.54.01.015.058Male3642.07.04.09.033Male37118.43.01.018.033Female3836.47.02.035.055Male3987.62.01.020.045Male4063.37.02.045.037Male41122.32.04.052.058Male4248.16.01.015.056Male4361.37.04.020.044Male4468.36.01.054.038Male4580.82.03.09.038Male4653.95.02.052.054Female4781.53.03.023.041Female48124.13.05.022.050Male4986.42.03.057.058Female5067.93.00.033.040Female5159.65.02.051.056Male5257.64.02.016.045Male5345.57.03.08.048Female5443.25.01.014.040Male5596.64.02.07.025Female5644.06.02.028.044Male5799.16.02.02.050Female5878.08.04.01.038Female5948.12.05.011.057Female6075.42.04.023.049Male6142.03.04.049.050Female62118.16.01.058.062Female6375.05.00.038.034Female6425.07.03.034.056Female6570.63.00.047.047Male6682.62.03.06.048Male67103.32.03.037.030Male6887.71.05.035.058Female6979.74.02.015.054Male7044.13.04.026.020Female7188.24.02.012.049Male7277.61.00.055.067Female7343.14.02.050.031Male7458.51.00.035.032Male7549.32.01.039.046Male7640.22.01.048.058Male7731.54.01.09.031Male7853.73.03.046.047Female7977.26.03.054.058Male8053.62.03.027.051Female8170.34.03.00.034Female8241.93.03.08.033Female8378.04.03.030.045Female8475.68.05.01.040Female85118.36.00.049.046Male86103.43.03.020.051Male8717.54.04.033.051Female8827.91.03.013.037Female8918.37.02.09.045Female9085.57.04.04.052Female9154.23.02.018.047Male92125.51.00.021.047Male93115.46.03.016.028Female9449.35.02.015.044Female9515.91.04.09.038Female9690.35.04.023.043Female9780.88.04.034.043Male9841.16.01.06.055Male9951.84.03.025.039Male100106.36.04.037.032Male10152.16.00.021.058Male102124.26.02.02.057Female10373.85.00.056.055Male104108.87.04.020.059Female10572.63.04.08.047Female10632.02.01.032.047Male10740.56.03.020.040Female10828.07.02.014.023Male10933.78.03.023.038Male11086.28.03.022.048Male11194.25.05.044.024Male11292.25.03.052.036Female11393.42.01.038.037Female11474.87.01.037.046Female11540.91.05.016.034Male11649.27.00.037.050Male11782.77.03.050.054Female11889.82.03.031.056Male11920.47.03.014.066Male12093.63.00.07.031Male12172.32.01.08.043Female122100.28.05.034.029Female12329.52.03.031.065Female12452.37.02.027.045Male12553.08.02.033.066Male12677.04.02.017.046Male12758.27.05.041.036Female12874.48.02.024.062Male12956.34.02.050.038Male13073.53.02.05.045Male13150.13.03.049.027Male13281.86.04.035.044Male133116.14.05.07.047Male13428.28.01.044.040Male13556.52.00.03.042Female13688.77.04.029.067Female13748.27.01.01.040Female13828.47.00.036.025Female13989.43.01.031.041Female140104.87.04.07.049Female14138.61.04.014.052Male14279.33.01.040.022Male14342.34.03.010.041Female14443.61.04.055.047Male14541.63.04.040.041Male14672.42.00.029.046Male14725.07.02.049.034Female148130.47.00.029.032Female14974.06.04.024.023Male15057.23.02.045.039Male15127.62.00.029.039Female152109.06.04.037.044Female153101.82.03.048.047Female154101.23.01.013.043Male15573.78.01.03.042Male15686.03.05.056.034Female157106.87.02.053.038Female15897.28.00.024.066Female15924.97.00.046.029Female16055.78.03.033.040Male16153.78.01.029.033Male162118.31.01.019.035Female16323.31.02.01.054Female16449.46.00.041.026Female16579.72.01.043.041Male16617.67.03.056.024Male167107.82.05.046.033Male16847.38.01.010.049Male16955.84.03.045.035Male17049.55.03.024.053Female17145.83.00.048.062Male17290.73.02.026.044Female17392.51.04.047.069Male17429.64.04.016.044Male17553.25.04.047.042Male17646.16.00.040.052Male177112.86.05.052.033Male178100.26.02.010.045Female17986.01.02.051.023Female18053.56.04.030.024Male18174.75.02.037.038Female18289.25.02.041.047Male18367.34.05.037.059Male18452.87.00.034.052Female185123.65.03.03.022Male18617.84.00.023.040Male18775.17.05.053.046Male18849.17.01.032.050Female18972.65.03.01.043Male19091.14.00.02.050Female19138.81.04.01.038Female19278.14.01.051.059Female19325.93.02.059.055Male19430.38.04.06.043Female19554.58.02.060.042Female196102.57.02.017.040Female19783.84.03.02.037Female19854.06.02.050.044Male19963.25.01.028.032Male200119.17.04.025.032Male20128.55.05.059.038Male20232.55.04.040.031Male20342.07.04.011.026Female20482.47.04.010.040Male20542.32.04.054.053Female20640.34.02.035.053Male20742.07.01.05.059Male20858.36.05.019.054Female20950.72.02.012.066Male21046.67.03.056.064Male211121.51.02.025.050Male21238.27.04.03.039Female21346.04.04.025.042Female21473.76.00.044.041Male21592.6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  • Money-back guarantee
  • 24/7 support
Our Options
  • Writer’s samples
  • Expert Proofreading
  • Overnight delivery
  • Part-by-part delivery
  • Copies of used sources
Paper format
  • 275 words per page
  • 12 pt Arial/Times New Roman
  • Double line spacing
  • Any citation style (APA, MLA, Chicago/Turabian, Harvard)

AcademicWritingCompany guarantees

Our customer is the center of what we do and thus we offer 100% original essays..
By ordering our essays, you are guaranteed the best quality through our qualified experts.All your information and everything that you do on our website is kept completely confidential.

Money-back guarantee

Academicwritingcompany.com always strives to give you the best of its services. As a custom essay writing service, we are 100% sure of our services. That is why we ensure that our guarantee of money-back stands, always

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Zero-plagiarism tolerance guarantee

The paper that you order at academicwritingcompany.com is 100% original. We ensure that regardless of the position you are, be it with urgent deadlines or hard essays, we give you a paper that is free of plagiarism. We even check our orders with the most advanced anti-plagiarism software in the industry.

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Free-revision guarantee

The Academicwritingcompany.com thrives on excellence and thus we help ensure the Customer’s total satisfaction with the completed Order.To do so, we provide a Free Revision policy as a courtesy service. To receive free revision the Academic writing Company requires that the you provide the request within Fifteen (14) days since the completion date and within a period of thirty (30) days for dissertations and research papers.

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Privacy and Security policy

With Academicwritingcompan.com, your privacy is the most important aspect. First, the academic writing company will never resell your personal information, which include credit cards, to any third party. Not even your lecturer on institution will know that you bought an essay from our academic writing company.

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Adherence to requirements guarantee

The academic writing company writers know that following essay instructions is the most important part of academic writing. The expert writers will, therefore, work extra hard to ensure that they cooperate with all the requirements without fail. We also count on you to help us provide a better academic paper.

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Calculate the price of your order

550 words
We'll send you the first draft for approval by September 11, 2020 at 10:52 AM
Total price:
$26
The price is based on these factors:
Customer Academic level
Number of pages required
Urgency of paper