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 Name Surname Financial Metrics for Decision Making Summer 2020 Assignment Part 1 – Hypothesis Testing [10 marks] 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 Salary ($) 77,600 76,000 90,700 97,200 90,700 101,800 78,700 81,300 84,200 97,600 77,500 75,700 89,400 84,300 78,700 84,600 87,700 103,400 83,800 101,300 94,700 69,200 95,400 61,500 68,800 Null hypothesis Alternative hypothesis Sample Mean Sample Standard Deviation Sample Size Hypothesised Value of Mean Test Statistic p-value Part 2 – Modelling [40 marks] Part 3 – Simple Linear Regression [20 marks] Price Kms 1 69,700 13,400 2 66,900 21,100 3 61,900 15,900 4 61,000 62,000 5 59,000 43,000 6 49,000 80,000 7 54,800 41,200 8 42,800 152,000 9 46,900 130,000 10 45,000 120,000 11 43,000 94,000 12 45,900 111,900 13 35,500 135,000 14 34,600 167,000 15 34,900 142,300 16 31,900 220,000 17 36,500 122,000 18 28,000 206,500 19 27,000 226,000 20 19,000 275,000 Part 4 – Multiple Linear Regression [30 marks] 3.0 Years of PostHigh School Education 3.0 Hours Per Week Watching Television 34.0 22.6 3.0 3.0 59.0 33 3 38.7 3.0 0.0 11.0 47 4 53.0 4.0 3.0 2.0 44 5 93.2 5.0 4.0 8.0 52 6 87.2 5.0 2.0 2.0 27 7 115.8 2.0 2.0 9.0 57 8 46.1 6.0 2.0 28.0 36 9 104.0 7.0 4.0 32.0 62 10 84.7 2.0 5.0 40.0 67 11 15.5 4.0 4.0 48.0 37 12 119.4 5.0 5.0 28.0 51 13 118.3 6.0 4.0 15.0 60 14 47.4 2.0 2.0 47.0 53 15 32.4 3.0 1.0 34.0 35 16 86.5 2.0 1.0 57.0 33 17 86.2 2.0 4.0 30.0 49 18 50.3 4.0 3.0 10.0 57 19 33.0 8.0 4.0 26.0 22 20 41.6 5.0 0.0 28.0 46 21 40.2 2.0 1.0 38.0 41 22 100.4 6.0 5.0 34.0 47 23 98.2 1.0 4.0 49.0 35 24 109.0 6.0 2.0 59.0 51 25 10.4 6.0 1.0 2.0 47 26 62.5 3.0 1.0 17.0 65 27 29.6 1.0 1.0 10.0 61 28 103.2 3.0 1.0 58.0 46 29 103.6 6.0 1.0 51.0 46 30 70.4 5.0 4.0 46.0 42 31 61.6 5.0 1.0 45.0 40 32 33.0 6.0 3.0 26.0 59 33 110.5 5.0 5.0 1.0 40 34 83.7 6.0 3.0 46.0 56 35 43.5 4.0 1.0 15.0 58 36 42.0 7.0 4.0 9.0 33 37 118.4 3.0 1.0 18.0 33 Annual Income ($1000) Household Size 1 43.1 2 Age 34 38 36.4 7.0 2.0 35.0 55 39 87.6 2.0 1.0 20.0 45 40 63.3 7.0 2.0 45.0 37 41 122.3 2.0 4.0 52.0 58 42 48.1 6.0 1.0 15.0 56 43 61.3 7.0 4.0 20.0 44 44 68.3 6.0 1.0 54.0 38 45 80.8 2.0 3.0 9.0 38 46 53.9 5.0 2.0 52.0 54 47 81.5 3.0 3.0 23.0 41 48 124.1 3.0 5.0 22.0 50 49 86.4 2.0 3.0 57.0 58 50 67.9 3.0 0.0 33.0 40 51 59.6 5.0 2.0 51.0 56 52 57.6 4.0 2.0 16.0 45 53 45.5 7.0 3.0 8.0 48 54 43.2 5.0 1.0 14.0 40 55 96.6 4.0 2.0 7.0 25 56 44.0 6.0 2.0 28.0 44 57 99.1 6.0 2.0 2.0 50 58 78.0 8.0 4.0 1.0 38 59 48.1 2.0 5.0 11.0 57 60 75.4 2.0 4.0 23.0 49 61 42.0 3.0 4.0 49.0 50 62 118.1 6.0 1.0 58.0 62 63 75.0 5.0 0.0 38.0 34 64 25.0 7.0 3.0 34.0 56 65 70.6 3.0 0.0 47.0 47 66 82.6 2.0 3.0 6.0 48 67 103.3 2.0 3.0 37.0 30 68 87.7 1.0 5.0 35.0 58 69 79.7 4.0 2.0 15.0 54 70 44.1 3.0 4.0 26.0 20 71 88.2 4.0 2.0 12.0 49 72 77.6 1.0 0.0 55.0 67 73 43.1 4.0 2.0 50.0 31 74 58.5 1.0 0.0 35.0 32 75 49.3 2.0 1.0 39.0 46 76 40.2 2.0 1.0 48.0 58 77 31.5 4.0 1.0 9.0 31 78 53.7 3.0 3.0 46.0 47 79 77.2 6.0 3.0 54.0 58 80 53.6 2.0 3.0 27.0 51 81 70.3 4.0 3.0 0.0 34 82 41.9 3.0 3.0 8.0 33 83 78.0 4.0 3.0 30.0 45 84 75.6 8.0 5.0 1.0 40 85 118.3 6.0 0.0 49.0 46 86 103.4 3.0 3.0 20.0 51 87 17.5 4.0 4.0 33.0 51 88 27.9 1.0 3.0 13.0 37 89 18.3 7.0 2.0 9.0 45 90 85.5 7.0 4.0 4.0 52 91 54.2 3.0 2.0 18.0 47 92 125.5 1.0 0.0 21.0 47 93 115.4 6.0 3.0 16.0 28 94 49.3 5.0 2.0 15.0 44 95 15.9 1.0 4.0 9.0 38 96 90.3 5.0 4.0 23.0 43 97 80.8 8.0 4.0 34.0 43 98 41.1 6.0 1.0 6.0 55 99 51.8 4.0 3.0 25.0 39 100 106.3 6.0 4.0 37.0 32 101 52.1 6.0 0.0 21.0 58 102 124.2 6.0 2.0 2.0 57 103 73.8 5.0 0.0 56.0 55 104 108.8 7.0 4.0 20.0 59 105 72.6 3.0 4.0 8.0 47 106 32.0 2.0 1.0 32.0 47 107 40.5 6.0 3.0 20.0 40 108 28.0 7.0 2.0 14.0 23 109 33.7 8.0 3.0 23.0 38 110 86.2 8.0 3.0 22.0 48 111 94.2 5.0 5.0 44.0 24 112 92.2 5.0 3.0 52.0 36 113 93.4 2.0 1.0 38.0 37 114 74.8 7.0 1.0 37.0 46 115 40.9 1.0 5.0 16.0 34 116 49.2 7.0 0.0 37.0 50 117 82.7 7.0 3.0 50.0 54 118 89.8 2.0 3.0 31.0 56 119 20.4 7.0 3.0 14.0 66 120 93.6 3.0 0.0 7.0 31 121 72.3 2.0 1.0 8.0 43 122 100.2 8.0 5.0 34.0 29 123 29.5 2.0 3.0 31.0 65 124 52.3 7.0 2.0 27.0 45 125 53.0 8.0 2.0 33.0 66 126 77.0 4.0 2.0 17.0 46 127 58.2 7.0 5.0 41.0 36 128 74.4 8.0 2.0 24.0 62 129 56.3 4.0 2.0 50.0 38 130 73.5 3.0 2.0 5.0 45 131 50.1 3.0 3.0 49.0 27 132 81.8 6.0 4.0 35.0 44 133 116.1 4.0 5.0 7.0 47 134 28.2 8.0 1.0 44.0 40 135 56.5 2.0 0.0 3.0 42 136 88.7 7.0 4.0 29.0 67 137 48.2 7.0 1.0 1.0 40 138 28.4 7.0 0.0 36.0 25 139 89.4 3.0 1.0 31.0 41 140 104.8 7.0 4.0 7.0 49 141 38.6 1.0 4.0 14.0 52 142 79.3 3.0 1.0 40.0 22 143 42.3 4.0 3.0 10.0 41 144 43.6 1.0 4.0 55.0 47 145 41.6 3.0 4.0 40.0 41 146 72.4 2.0 0.0 29.0 46 147 25.0 7.0 2.0 49.0 34 148 130.4 7.0 0.0 29.0 32 149 74.0 6.0 4.0 24.0 23 150 57.2 3.0 2.0 45.0 39 151 27.6 2.0 0.0 29.0 39 152 109.0 6.0 4.0 37.0 44 153 101.8 2.0 3.0 48.0 47 154 101.2 3.0 1.0 13.0 43 155 73.7 8.0 1.0 3.0 42 156 86.0 3.0 5.0 56.0 34 157 106.8 7.0 2.0 53.0 38 158 97.2 8.0 0.0 24.0 66 159 24.9 7.0 0.0 46.0 29 160 55.7 8.0 3.0 33.0 40 161 53.7 8.0 1.0 29.0 33 162 118.3 1.0 1.0 19.0 35 163 23.3 1.0 2.0 1.0 54 164 49.4 6.0 0.0 41.0 26 165 79.7 2.0 1.0 43.0 41 166 17.6 7.0 3.0 56.0 24 167 107.8 2.0 5.0 46.0 33 168 47.3 8.0 1.0 10.0 49 169 55.8 4.0 3.0 45.0 35 170 49.5 5.0 3.0 24.0 53 171 45.8 3.0 0.0 48.0 62 172 90.7 3.0 2.0 26.0 44 173 92.5 1.0 4.0 47.0 69 174 29.6 4.0 4.0 16.0 44 175 53.2 5.0 4.0 47.0 42 176 46.1 6.0 0.0 40.0 52 177 112.8 6.0 5.0 52.0 33 178 100.2 6.0 2.0 10.0 45 179 86.0 1.0 2.0 51.0 23 180 53.5 6.0 4.0 30.0 24 181 74.7 5.0 2.0 37.0 38 182 89.2 5.0 2.0 41.0 47 183 67.3 4.0 5.0 37.0 59 184 52.8 7.0 0.0 34.0 52 185 123.6 5.0 3.0 3.0 22 186 17.8 4.0 0.0 23.0 40 187 75.1 7.0 5.0 53.0 46 188 49.1 7.0 1.0 32.0 50 189 72.6 5.0 3.0 1.0 43 190 91.1 4.0 0.0 2.0 50 191 38.8 1.0 4.0 1.0 38 192 78.1 4.0 1.0 51.0 59 193 25.9 3.0 2.0 59.0 55 194 30.3 8.0 4.0 6.0 43 195 54.5 8.0 2.0 60.0 42 196 102.5 7.0 2.0 17.0 40 197 83.8 4.0 3.0 2.0 37 198 54.0 6.0 2.0 50.0 44 199 63.2 5.0 1.0 28.0 32 200 119.1 7.0 4.0 25.0 32 201 28.5 5.0 5.0 59.0 38 202 32.5 5.0 4.0 40.0 31 203 42.0 7.0 4.0 11.0 26 204 82.4 7.0 4.0 10.0 40 205 42.3 2.0 4.0 54.0 53 206 40.3 4.0 2.0 35.0 53 207 42.0 7.0 1.0 5.0 59 208 58.3 6.0 5.0 19.0 54 209 50.7 2.0 2.0 12.0 66 210 46.6 7.0 3.0 56.0 64 211 121.5 1.0 2.0 25.0 50 212 38.2 7.0 4.0 3.0 39 213 46.0 4.0 4.0 25.0 42 214 73.7 6.0 0.0 44.0 41 215 92.6 3.0 2.0 4.0 47 216 113.8 5.0 3.0 58.0 32 217 34.5 2.0 3.0 28.0 53 218 54.7 4.0 0.0 34.0 46 219 52.5 6.0 1.0 24.0 52 220 23.0 3.0 2.0 34.0 62 221 122.3 6.0 4.0 8.0 55 222 69.1 7.0 2.0 31.0 27 223 93.4 4.0 2.0 5.0 55 224 80.3 4.0 4.0 6.0 37 225 24.3 5.0 3.0 46.0 61 226 112.7 7.0 5.0 43.0 61 227 20.5 5.0 4.0 22.0 46 228 71.2 8.0 0.0 34.0 33 229 55.1 6.0 4.0 11.0 46 230 47.4 7.0 1.0 1.0 55 231 65.7 7.0 3.0 39.0 50 232 53.3 6.0 2.0 28.0 33 233 81.5 8.0 3.0 33.0 50 234 37.3 5.0 2.0 31.0 47 235 36.0 8.0 0.0 10.0 60 236 20.5 4.0 1.0 10.0 55 237 49.4 5.0 4.0 44.0 40 238 60.8 2.0 5.0 4.0 41 239 100.3 2.0 2.0 49.0 55 240 33.6 2.0 1.0 0.0 48 241 102.8 2.0 4.0 25.0 23 242 71.9 8.0 2.0 4.0 31 243 41.9 7.0 1.0 28.0 42 244 111.8 2.0 3.0 57.0 45 245 41.7 2.0 3.0 19.0 42 246 48.4 4.0 4.0 33.0 60 247 44.3 6.0 1.0 16.0 43 248 49.7 2.0 2.0 18.0 52 249 45.5 3.0 4.0 49.0 36 250 69.9 8.0 2.0 28.0 53 251 73.7 2.0 2.0 9.0 44 252 28.6 5.0 3.0 17.0 55 253 38.6 4.0 2.0 51.0 45 254 69.9 5.0 4.0 35.0 22 255 39.0 7.0 2.0 55.0 35 256 109.9 7.0 2.0 37.0 56 257 41.4 7.0 3.0 59.0 67 258 87.3 6.0 4.0 3.0 40 259 107.8 4.0 4.0 8.0 59 260 28.8 8.0 4.0 29.0 31 261 120.7 6.0 1.0 14.0 47 262 113.0 2.0 0.0 54.0 51 263 120.1 3.0 4.0 7.0 53 264 20.3 3.0 1.0 29.0 45 265 61.6 8.0 4.0 18.0 34 266 115.9 4.0 4.0 7.0 50 267 28.8 7.0 4.0 57.0 53 268 44.2 7.0 2.0 48.0 40 269 51.9 3.0 3.0 51.0 33 270 33.7 6.0 2.0 15.0 47 271 61.6 2.0 4.0 17.0 56 272 105.0 5.0 2.0 4.0 57 273 53.6 6.0 1.0 16.0 44 274 42.3 5.0 2.0 48.0 27 275 106.4 7.0 2.0 41.0 36 276 37.5 3.0 5.0 55.0 37 277 34.2 4.0 3.0 44.0 48 278 46.1 6.0 2.0 42.0 53 279 129.5 4.0 4.0 29.0 27 280 80.6 5.0 0.0 57.0 66 281 54.8 3.0 1.0 25.0 54 282 121.7 8.0 3.0 45.0 37 283 48.8 6.0 5.0 36.0 30 284 107.1 5.0 2.0 20.0 54 285 101.5 2.0 2.0 55.0 36 286 34.9 4.0 3.0 54.0 38 287 97.4 1.0 1.0 54.0 48 288 71.8 4.0 4.0 12.0 46 289 29.5 3.0 4.0 35.0 20 290 57.5 6.0 5.0 4.0 51 291 39.1 3.0 4.0 34.0 31 292 70.9 8.0 4.0 13.0 36 293 26.8 5.0 2.0 46.0 62 294 101.4 5.0 4.0 18.0 46 295 76.0 8.0 2.0 10.0 26 296 85.7 1.0 5.0 13.0 51 297 93.8 5.0 3.0 2.0 32 298 46.1 3.0 2.0 19.0 40 299 38.0 3.0 3.0 20.0 49 300 54.7 2.0 3.0 42.0 55 301 31.5 4.0 5.0 37.0 59 302 96.0 7.0 2.0 23.0 29 303 77.7 4.0 5.0 11.0 35 304 124.0 7.0 0.0 21.0 30 305 98.1 2.0 1.0 34.0 45 306 95.3 1.0 5.0 18.0 48 307 95.8 3.0 4.0 ...
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