Finance Question

Economics

Melbourne institute of technology

Question Description

Follow the requirements to write 5pages report and an excel spreadsheet

The course is FINANCIAL METRICS FOR DECISION MAKING

The written report must be self-contained and formatted as a PDF file

And you will need to make a quantitative analysis of the given data on an excel

I have attached a sample which was done by another student please have a look make sure you are on the track

All the work must be original

Unformatted Attachment Preview

Finance Discipline Group UTS Business School FINANCIAL METRICS FOR DECISION MAKING – SUMMER 2020 ASSIGNMENT General Instructions and Information § § § § § § § § This assignment accounts for 40% of students’ final grade for 25624 Financial Metrics for Decision Making. The assignment is to be undertaken individually. The assignment is due on Friday the 5th of February 2021 (Week 11) by 5pm. The assignment must be submitted via UTSOnline. You’ll need to provide a written report and an Excel spreadsheet: • The written report must be self-contained and formatted as a PDF file. • Excel files will also be examined and will constitute 20% of the value of the assignment. The Excel file should include all calculations. The scope of this assignment is limited to [5] pages not including appendices and cover sheet. Use standard fonts (think Calibri, Times New Roman, Arial) and standard font sizes. There is no specific word count. You are encouraged to use figures and tables when reporting your results. The file names, for both the report and the Excel spreadsheet, will take the form: “Name – Student number”. For example, if your name is Jane Doe and your student number is 12345, then your file name will be “Jane Doe - 12345”. Please don’t write the words “name”, “student number” or anything else in the file name. All assignment-related questions should be posted to the Discussion Board on UTSOnline. Marking § § § This assessment will be graded on the quality of both, the written report and the quantitative analysis in Excel. Marks will be awarded 70% for content and analysis, and 30% for effectiveness of communication and presentation. Late submissions will be allocated a mark of zero with no exceptions unless via special consideration filing. Files In the Assignment folder on UTSOnline, you’ll find the following files: § Cover Sheet: is the cover sheet you’ll need to fill in, sign, and submit along with your written report. § Data: this Excel spreadsheet contains the following worksheets: • • Cover: you’ll need provide your student details here. Part 1 to Part 4: these worksheets contain the data (when applicable) for each part and should be used to perform all relevant data analysis required. Instructions Part 1 – Hypothesis Testing [10 marks] The national average annual salary for a campus manager is $89,000 a year. A state official took a sample of 25 campus managers in the state of New South Wales (NSW) to learn about salaries in the state and see if they differed from the national average. The data for this question is provided in the worksheet named ‘Part 1’. a. [5 marks] Formulate the null and alternative hypotheses that can be used to determine whether the annual salary mean of a campus manager in NSW differs from the national mean of $89,000. b. [5 marks] What is the p-value for your hypothesis test in part (a)? At a 5% significance level, can your null hypothesis be rejected? What is your conclusion? Part 2 – Modelling [40 marks] Background Information Your boss, a real estate business manager, has approached you for financial advice. She is interested in either purchasing or leasing a new car for her personal use. Aware of your financial expertise, she has asked you to develop a Spreadsheet Model that allows her to decide whether to buy or lease the vehicle. The retail price of the car she is interested in is $50,000. Buy Scenario In the Buy Scenario, your boss would like to purchase the car by making an initial down payment of $15,000 dollars and finance the difference with a conventional car loan to be repaid monthly for 3-years at a 5% interest rate. The following table summarises the relevant information for the Buy Scenario. Buy Scenario Car Price $ 50,000.00 Down Payment $ 15,000.00 Interest Rate 5% Term 3 years Lease Scenario In the Lease Scenario, there is no initial down payment. Instead, your boss would like to use a Finance Lease to rent the car for 3 years. At the end of this 3-year period, she plans to purchase the car from the lease financier (lessor) by paying a residual value of $25,000. In this scenario, to rent the car, your boss would have to pay a monthly rent of $850 for 3 years. The following table summarises the relevant information for the Lease Scenario. Lease Scenario Car Price $ 50,000.00 Residual Value $ 25,000.00 Monthly Rent $850 Term 3 years Note: A Finance Lease is a common way people can use a car without actually buying it. Under a Finance Lease, the car belongs to the financier (lessor) who rents it out to the borrower (lessee) in exchange for monthly instalments. At the end of the lease term, the lessee has the option to claim ownership of the car by paying a residual value. a. [5 marks] Lay out the decision-making problem, the alternatives, and the overall criteria you would use to evaluate the different alternatives. b. [5 marks] Carefully establish all the inputs and assumptions you would include in the Spreadsheet Model for each scenario. If you include inputs/variables other than the ones provided (e.g. interest rate on savings), justify your choices based on data from the Australian market. c. [10 marks] Based on your answers to a) and b), build a Spreadsheet Model which helps your boss decide whether to buy or lease the vehicle. Make your spreadsheet selfexplanatory. d. [5 marks] Perform What-If analysis for at least one of your inputs (e.g. down payment). That is, show what would happen to your model’s output at, at least, three different values of the chosen input. In your spreadsheet, highlight the section you would present to your boss to help her with her decision-making problem. e. [5 marks] Of all the inputs included in your model, which one do you think is the most important in determining whether buying or leasing is the best option for your boss? Provide an explanation. f. [5 marks] Describe the model’s limitations and/or aspects that could be improved. What other factors haven’t been considered? g. [5 marks] Are there any cognitive biases you would suggest your boss to be aware of when finally making her decision? Part 3 – Simple Linear Regression [20 marks] The Toyota Hilux is the top selling car in Australia. The price of a previously owned Hilux depends on many factors, including the number kilometres (kms) travelled. To investigate the relationship between a car’s kms and its sales price, data was collected on a sample of 20 used Hilux in Sydney. The data for this question is provided in the worksheet named ‘Part 3’. a. [2 marks] Create a scatter plot for this data with kms as the independent variable. What does the scatter plot indicate about the relationship between price and kms? b. [5 marks] Estimate a simple linear regression model with price as the dependent variable and kms as the independent variable. What is the estimated regression model (equation)? c. [5 marks] Test whether each of the regression parameters (intercept and coefficient) is equal to zero at a 5% significance level. Interpret the coefficients of the estimated regression parameters and discuss whether these interpretations are reasonable. d. [4 marks] Using the model estimated in part (b), calculate the predicted price for each of the cars in the sample. Based on the difference between the true and predicted prices, identify the two cars that were the biggest bargains. e. [4 marks] Suppose that you are considering purchasing a previously owned Hilux that has been driven 100,000 kms. Use the model estimated in part (b) to predict the price for this car. Is this the price you would offer the seller? Part 4 – Multiple Linear Regression [30 marks] A financial institution has a large dataset of information provided by its customers when they apply for a credit card. This customer information includes the following variables: • Annual household income (in thousands of dollars) • Household size (number of people) • Number of years of post-high school education • Number of hours per week watching television • Age • Gender In addition, the financial institution has records of the credit card charges accrued by each customer over the past year. The data for this question is provided in the worksheet named ‘Part 4’. a. [5 marks] Plot histograms to contrast the distribution of annual credit card charges for 1) People with zero years of post-high school education vs. People with at least 1 year of post-high school education, and 2) Female vs. Male. Describe the overall shape of each histogram and comment on any observable differences. b. [10 marks] Estimate a multiple linear regression model in which the dependent variable is the credit card charges accrued by each customer in the data over the past year, and the independent variables are all the variables the financial institution collected when the customer first applied for a credit card (e.g. annual household income). What is the estimated regression model (equation)? a. Hint: For Gender, create a dummy variable that takes 1 if the customer is female and 0 if male. c. [15 marks] Interpret each of the regression coefficients and comment on both their economic and statistical significance. For each significant regressor (at a 5% significance level) provide a potential explanation for its statistical relationship with the dependent variable. Student ID 13,338,900 Name WEI Surname XIANG Stock 1 CE Stock 2 NOW Stock 3 CTAS Stock 4 SJM Stock 5 SNA Financial Metrics for Decision Making Spring 2020 Assignment Stock 6 NDAQ Stock 7 LLY Stock 8 HLT Stock 9 CNC Stock 10 AVGO n 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 Symbol MMM ABT ABBV ABMD ACN ATVI ADBE AMD AAP AES AFL A APD AKAM ALK ALB ARE ALXN ALGN ALLE LNT ALL GOOGL GOOG MO AMZN AMCR AEE AAL AEP AXP AIG AMT AWK AMP ABC AME AMGN APH ADI ANSS ANTM AON AOS APA AIV AAPL AMAT APTV ADM Name 3M Company Abbott Laboratories AbbVie Inc. ABIOMED Inc Accenture plc Activision Blizzard Adobe Inc. Advanced Micro Devices Inc Advance Auto Parts AES Corp AFLAC Inc Agilent Technologies Inc Air Products & Chemicals Inc Akamai Technologies Inc Alaska Air Group Inc Albemarle Corp Alexandria Real Estate Equities Alexion Pharmaceuticals Align Technology Allegion Alliant Energy Corp Allstate Corp Alphabet Inc.(Class A) Alphabet Inc.(Class C) Altria Group Inc Amazon.com Inc. Amcor plc Ameren Corp American Airlines Group American Electric Power American Express Co American International Group American Tower Corp. American Water Works Company Inc Ameriprise Financial AmerisourceBergen Corp AMETEK Inc. Amgen Inc. Amphenol Corp Analog Devices, Inc. ANSYS Anthem Aon plc A.O. Smith Corp Apache Corporation Apartment Investment & Management Apple Inc. Applied Materials Inc. Aptiv PLC Archer-Daniels-Midland Co GICS Sector Industrials Health Care Health Care Health Care Communication Services Information Technology Information Technology Consumer Discretionary Utilities Financials Health Care Materials Information Technology Industrials Materials Real Estate Health Care Health Care Industrials Utilities Financials Communication Services Communication Services Consumer Staples Consumer Discretionary Materials Utilities Industrials Utilities Financials Financials Real Estate Utilities Financials Health Care Industrials Health Care Information Technology Information Technology Information Technology Health Care Financials Industrials Energy Real Estate Information Technology Information Technology Consumer Discretionary Consumer Staples 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 ANET AJG AIZ T ATO ADSK ADP AZO AVB AVY BKR BLL BAC BK BAX BDX BRK-B BBY BIO BIIB BLK BA BKNG BWA BXP BSX BMY AVGO BR BF-B CHRW COG CDNS CPB COF CAH KMX CCL CAT CBOE CBRE CDW CE CNC CNP CTL CERN CF SCHW CHTR CVX Arista Networks Arthur J. Gallagher & Co. Assurant AT&T Inc. Atmos Energy Autodesk Inc. Automatic Data Processing AutoZone Inc AvalonBay Communities Avery Dennison Corp Baker Hughes Co Ball Corp Bank of America Corp The Bank of New York Mellon Baxter International Inc. Becton Dickinson Berkshire Hathaway Best Buy Co. Inc. Bio-Rad Laboratories Biogen Inc. BlackRock Boeing Company Booking Holdings Inc BorgWarner Boston Properties Boston Scientific Bristol-Myers Squibb Broadcom Inc. Broadridge Financial Solutions Brown-Forman Corp. C. H. Robinson Worldwide Cabot Oil & Gas Cadence Design Systems Campbell Soup Capital One Financial Cardinal Health Inc. Carmax Inc Carnival Corp. Caterpillar Inc. Cboe Global Markets CBRE Group CDW Celanese Centene Corporation CenterPoint Energy CenturyLink Inc Cerner CF Industries Holdings Inc Charles Schwab Corporation Charter Communications Chevron Corp. Information Technology Financials Financials Communication Services Utilities Information Technology Information Technology Consumer Discretionary Real Estate Materials Energy Materials Financials Financials Health Care Health Care Financials Consumer Discretionary Health Care Health Care Financials Industrials Consumer Discretionary Consumer Discretionary Real Estate Health Care Health Care Information Technology Information Technology Consumer Staples Industrials Energy Information Technology Consumer Staples Financials Health Care Consumer Discretionary Consumer Discretionary Industrials Financials Real Estate Information Technology Materials Health Care Utilities Communication Services Health Care Materials Financials Communication Services Energy 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 CMG CB CHD CI CINF CTAS CSCO C CFG CTXS CLX CME CMS KO CTSH CL CMCSA CMA CAG CXO COP ED STZ COO CPRT GLW CTVA COST COTY CCI CSX CMI CVS DHI DHR DRI DVA DE DAL XRAY DVN DXCM FANG DLR DFS DISCA DISCK DISH DG DLTR D Chipotle Mexican Grill Chubb Limited Church & Dwight CIGNA Corp. Cincinnati Financial Cintas Corporation Cisco Systems Citigroup Inc. Citizens Financial Group Citrix Systems The Clorox Company CME Group Inc. CMS Energy Coca-Cola Company Cognizant Technology Solutions Colgate-Palmolive Comcast Corp. Comerica Inc. Conagra Brands Concho Resources ConocoPhillips Consolidated Edison Constellation Brands The Cooper Companies Copart Inc Corning Inc. Corteva Costco Wholesale Corp. Coty, Inc Crown Castle International Corp. CSX Corp. Cummins Inc. CVS Health D. R. Horton Danaher Corp. Darden Restaurants DaVita Inc. Deere & Co. Delta Air Lines Inc. Dentsply Sirona Devon Energy DexCom Diamondback Energy Digital Realty Trust Inc Discover Financial Services Discovery, Inc.(Class A) Discovery, Inc.(Class C) Dish Network Dollar General Dollar Tree Dominion Energy Consumer Discretionary Financials Consumer Staples Health Care Financials Industrials Information Technology Financials Financials Information Technology Consumer Staples Financials Utilities Consumer Staples Information Technology Consumer Staples Communication Services Financials Consumer Staples Energy Energy Utilities Consumer Staples Health Care Industrials Information Technology Materials Consumer Staples Consumer Staples Real Estate Industrials Industrials Health Care Consumer Discretionary Health Care Consumer Discretionary Health Care Industrials Industrials Health Care Energy Health Care Energy Real Estate Financials Communication Services Communication Services Communication Services Consumer Discretionary Consumer Discretionary Utilities 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 DPZ DOV DOW DTE DUK DRE DD DXC ETFC EMN ETN EBAY ECL EIX EW EA EMR ETR EOG EFX EQIX EQR ESS EL EVRG ES RE EXC EXPE EXPD EXR XOM FFIV FB FAST FRT FDX FIS FITB FE FRC FISV FLT FLIR FLS FMC F FTNT FTV FBHS FOXA Domino's Pizza Dover Corporation Dow Inc. DTE Energy Co. Duke Energy Duke Realty Corp DuPont de Nemours Inc DXC Technology E*Trade Eastman Chemical Eaton Corporation eBay Inc. Ecolab Inc. Edison Int'l Edwards Lifesciences Electronic Arts Emerson Electric Company Entergy Corp. EOG Resources Equifax Inc. Equinix Equity Residential Essex Property Trust, Inc. Estée Lauder Companies Evergy Eversource Energy Everest Re Group Ltd. Exelon Corp. Expedia Group Expeditors Extra Space Storage Exxon Mobil Corp. F5 Networks Facebook, Inc. Fastenal Co Federal Realty Investment Trust FedEx Corporation Fidelity National Information Services Fifth Third Bancorp FirstEnergy Corp First Republic Bank Fiserv Inc FleetCor Technologies Inc FLIR Systems Flowserve Corporation FMC Corporation Ford Motor Company Fortinet Fortive Corp Fortune Brands Home & Security Fox Corporation(Class A) Consumer Discretionary Industrials Materials Utilities Utilities Real Estate Materials Information Technology Financials Materials Industrials Consumer Discretionary Materials Utilities Health Care Communication Services Industrials Utilities Energy Industrials Real Estate Real Estate Real Estate Consumer Staples Utilities Utilities Financials Utilities Consumer Discretionary Industrials Real Estate Energy Information Technology Communication Services Industrials Real Estate Industrials Information Technology Financials Utilities Financials Information Technology Information Technology Information Technology Industrials Materials Consumer Discretionary Information Technology Industrials Industrials Communication Services 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 FOX BEN FCX GPS GRMN IT GD GE GIS GM GPC GILD GL GPN GS GWW HRB HAL HBI HIG HAS HCA PEAK HSIC HSY HES HPE HLT HFC HOLX HD HON HRL HST HWM HPQ HUM HBAN HII IEX IDXX INFO ITW ILMN INCY IR INTC ICE IBM IP IPG Fox Corporation(Class B) Franklin Resources Freeport-McMoRan Inc. Gap Inc. Garmin Ltd. Gartner Inc General Dynamics General Electric General Mills General Motors Genuine Parts Gilead Sciences Globe Life Inc. Global Payments Inc. Goldman Sachs Group Grainger (W.W.) Inc. H&R Block Halliburton Co. Hanesbrands Inc Hartford Financial Svc.Gp. Hasbro Inc. HCA Healthcare Healthpeak Properties Henry Schein The Hershey Company Hess Corporation Hewlett Packard Enterprise Hilton Worldwide Holdings Inc HollyFrontier Corp Hologic Home Depot Honeywell Int'l Inc. Hormel Foods Corp. Host Hotels & Resorts Howmet Aerospace HP Inc. Humana Inc. Huntington Bancshares Huntington Ingalls Industries IDEX Corporation IDEXX Laboratories IHS Markit Ltd. Illinois Tool Works Illumina Inc Incyte Ingersoll Rand Intel Corp. Intercontinental Exchange International Business Machines International Paper Interpublic Group Communication Services Financials Materials Consumer Discretionary Consumer Discretionary Information Technology Industrials Industrials Consumer Staples Consumer Discretionary Consumer Discretionary Health Care Financials Information Technology Financials Industrials Consumer Discretionary Energy Consumer Discretionary Financials Consumer Discretionary Health Care Real Estate Health Care Consumer Staples Energy Information Technology Consumer Discretionary Energy Health Care Consumer Discretionary Industrials Consumer Staples Real Estate Industrials Information Technology Health Care Financials Industrials Industrials Health Care Industrials Industrials Health Care Health Care Industrials Information Technology Financials Information Technology Materials Communication Services 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 28 ...
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