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- 1. Part I: (Short Answer) 1. In Java, what are the three different ways you can implement an interface? 2. Discuss examples of “is-a” and “has-a” relationships and possible Java implementations. 3. When is it appropriate to use the various techniques for handling exceptions? 4. When is it appropriate to use an applet? 5. Discuss how to convert a GUI application into an applet. First International Resource Management, Inc. (aka. FIRM) asks you to develop programs to solve the following problem. FIRM pays its employees on a weekly basis. FIRM has four types of employees: salaried employees, who are paid a fixed weekly salary regardless of the number of hours worked; hourly employees, who are paid by the hour and receive overtime pay; commission employees, who are paid a percentage of their sales; and salaried-commission employees, who receive a base salary plus a percentage of their sales. FIRM wants to implement a java application that performs its payroll calculations polymorphically. Of course each employ belongs to a department. Based on the description of the problem and several discussions with the client, a class diagram is agreed upon and the class hierarchy is shown in the diagram. At first, the management wants to see a GUI application about three employee types: HourlyEmployee, SalariedEmployee, and CommissionEmployee.
- 2. Development of Classes and an Interface Employee: abstract super class Attributes: · Employee name: String Methods: · Constructor: with one parameter of employee name · Get and set methods for the attribute · earnings(): Abstract method that will return a double value SalariedEmployee: subclass of Employee Additional Attribute: · weeklySalary: double Methods: · Constructor: with two parameters of employee name and weekly salary · Constructor: with one parameter of employee name, and default salary is $800. · Get and set methods for weeklySalary · earnings(): return weekly salary HourlyEmployee: subclass of Employee Addition Attributes: · wage: double · hours: double Methods: · Constructor: with three parameters of employee name, wage, and hours · Constructor: with two parameters of employee name and hours, default wage is $8. · Get and set methods for wage and hours · earnings(): if the employee worked overtime (hours>40), the
- 3. overtime portion is paid half timemore than regular wage. CommissionEmployee: subclass of Employee Additional Attribute: · grossSales: double · commissionRate: double Methods: · Constructor: with three parameters of employee name, gross sales, and commission rate. · Constructor: with two parameters of employee name and gross sales, and default rate is 0.05. · Get and set for grossSales and commissionRate · earnings(): commission is calculated as gross sales times commission rate. BasePlusCommissionEmployee: subclass of CommissionEmployee, do not worry about it for this home work. All the classes also need to have a method to print out pay check, which will show company name, basic information of the employee, and earnings of the current week. An interface class Company is used for the purpose and all the employee types will implement this interface. Company: interface Attribute: · Company name: First International Resource Management, Inc. Methods: · tellAboutSelf() Graphic User Interface
- 4. To make things easier at the beginning, only take into account of SalariedEmployee, CommissionEmployee, and HourlyEmplyee. You can have your own design of the window interface. Basically, the interface will allow user to inputbasic information of an employee, calculate earnings, and show a paycheck. · The interface checks the valid input, e.g., name should not be empty, and salary should benumeric, and inputs should be in reasonable range (such as hours is great than 0 and not greatthan 168) so on. For numeric input and valid range, please use Exception handling. You may need to create your exceptions. · The interface allows user to use default values. If a field is left blank, the program shouldshow appropriate default value. · Clicking Add will create an employee object and show a successful message. · After adding an employee, clicking Earn button will show the earning of the employee in amessage. · Clicking Print will show a message box that contains paycheck information. · Clicking Clear will reset text fields to be blank. · Clicking Close will shutdown the program. Interface for Salary Employee: After inputting data and clicking Add. After clicking Earn (please only click Earn after a succesful Add.) After clicking Print
- 5. Interface for Commision Employee: Interface for Hourly Employee: When Hours is not valid, a message is shown. Following information can be used as testing data. When testing
- 6. your program, try all kinds of things to break it. Then fix it. Employee Type Avery Tolar Salary Salary is 1000 Tammy Hemphill Commission Employee Gross sales = 14500, default rate Wayne Tarrance Hourly employee Worked 55 hours last week, default wage Problem 20Ending Inventory BalanceMonthly SalesThese are the two variables that you need to answer problem 20.15445053Copy and paste them into Minitab.1913505220287507117828871554388019104454120838 55246788242021015716 Problem 19ResidualsThese are the residuals referred to in problem 19.-24You may copy and paste them into Minitab if you wish.-348-892-62-378-489-3423449023578198 Problems 21 through 30SalesThis is monthly data to be used to answer questions 21 through 30.6028Load it into Minitab before you take the exam.592710515322765192031294235733646518959139181798 71529416850127532690161494147862579905131853599230384 13961933022707153953082625589103184197608686003990991 36858781596793344353719277733665351157217509206229110 08110289312885710477611103663701826573141648341856512 42673289554164373160608176096142363114907113552127042 51604803662089382638302522162195661490822138881789471 33650116946164154588438238622480335430132826331364721 45613371921834821446181397501845467104315293025055940
- 7. 9567394747272874230303375402195409173518181702258713 Chapter 5 : Chapter 5 - Exam 1 Top of Form YOU NEED TO HAVE MINITAB TO COMPLETE THIS ASSIGNMENT Eco 309 Exam 1 (Chapter 1 through 5) You will have 2 and 1/2 hours to complete the 30 multiple choice questions. This exam must be completed. I suggest that you complete the exam within one session to prevent the loss of your answers. You must take this exam since there will be no make-up tests. The excel data for this test may be downloaded from Doc Sharing under Exam 1 Data and can be copied and pasted directly into Minitab. I suggest that you download the data before you begin the exam. Be sure to select the best answer for each question and do not leave any questions unanswered. 1. You are given only three quarterly seasonal indices and quarterly seasonally adjusted data for the entire year. What is the raw data value for Q4? Raw data is not adjusted for seasonality. Quarter Seasonal Index Seasonally Adjusted Data Q1 .80 295 Q2 .85 299 Q3 1.15 270 Q4 --- 271 (Points : 3) 325 225
- 8. 252 271 2. One model of exponential smoothing will provide almost the same forecast as a liner trend method. What are linear trend intercept and slope counterparts for exponential smoothing? (Points : 3) Alpha and Delta Delta and Gamma Alpha and Gamma Std Dev and Mean 3. Why is the residual mean value important to a forecaster? (Points : 3) Large mean values indicate nonautoregressiveness. Small mean values indicate the total amount of error is small. Large absolute mean values indicate estimate bias. Large mean values indicate the standard error of the model is small. 4. When performing correlation analysis what is the null hypothesis? What measure in Minitab is used to test it and to be 95% confident in the significance of correlation coefficient. (Points : 3) Ho: r = .05 p < .5 Ho: r = 1 p =.05
- 9. Ho: r ≠ 0 p≤.05 Ho: r = 0 p≤.05 5. In decomposition what does the cycle factor (CF) of .80 represent for a monthly forecast estimate of a Y variable? (Points : 3) The estimated value is 80% of the average monthly seasonal estimate. The estimate is .80 of the forecasted Y trend value. The estimated value is .80 of the historical average CMA values. The estimated value has 20% more variation than the average historical Y data values. 6. A Burger King franchise owner notes that the sales per store has fallen below the stated national Burger King outlet average of $1,258,000. He asserts a change has occurred that reduced the fast food eating habits of Americans. What is his hypothesis (H1) and what type of test for significance must be applied? (Points : 3) H1: u ≥ $1.258,000 A one-tailed t-test to the left. H1: u = $1.258,000 A two-tailed t-test. H1: u < $1.258,000 A one-tailed t-test to the left. H1: p < $1.258,000 A one-tailed test to the right. 7. The CEO of Home Depot wants to see if city size has any relationship to the current profit margins of the company stores. What data type will he likely use to determine this? (Points : 3) Time series data of profits by store.
- 10. Recent 10 year sample of profits by stores. Recent cross section of store profits by city. Trend of a random sample of store profits over time. 8. Sometimes forecasters get lazy or forgetful and do not check the significance of XY data correlations and use the X variable to forecast Y. What is the result of this? (Points : 3) Type 2 error Autocorrelation error Type 3 error Type 1 error 9. In exponential smoothing what is the weight of the alpha coefficient for a time series data observation from the 3rd previous period if the original alpha value is set at .3? (Points : 3) The weight cannot be calculated since the data observation is not given. The weight is zero since the alpha value is set relatively high. .125 .103 .084 10. What is not a characteristic of a random data series? (Points : 3) Zero mean with an normal distribution. ACF LBQ values less than .05. Non autoregressive observations Central tendency 11. What is the major cause of non randomness
- 11. (autoregressiveness) in business data? (Points : 3) Randomness only occurs for short time periods. Random events such as storms or technologies offset over the long run. Measurements naturally increase or decrease over time. Business participant’s decisions and work. 12. Which form of exponential smoothing can result in a naïve forecast? (Points : 3) Winters with a very low seasonal coefficient. Simple with a very low trend coefficient. Simple with a very high alpha value. Double with a very low alpha value. 13. What statistical characteristic enables forecasters to move from uncertainty to quantifiable low risk in the business forecasting process? (Points : 3) Large amounts of available business data naturally create statistical accuracy. Although business data are not normally distributed the statistics from the data are normally distributed. Statistical forecasting technology has improved the accuracy of models. Statistical t and p-values always reflect the data population. 14. What is used to determine the forecast model confidence level for Exponential Smoothing and Decomposition models? (Points : 3) The significance level of the smoothing constants The error measures
- 12. The residual LBQ Chi-Square values The mean of the residuals 15. You are responsible for forecasting your company’s revenues for the next 24 months. You have three years of historical monthly data and previous forecasts that indicate that the company revenues with no obvious seasonality have grown significantly over that time. Which forecast method would you apply to the problem?(Points : 3) 3 period moving average 12 period moving average Simple exponential smoothing Double exponential smoothing 16. You obtained a correlation coefficient from two data series that indicates a p-value of .97. Can you be 95% confident that the correlation is significantly different from zero? (Points : 3) Yes, since the p value is above the confidence level. Yes, since the p value is above 1 minus the confidence level. No, since the p-value is above the 1 minus the confidence level. No, since the data is not provided to determine true confidence. 17. In decomposition the seasonal indices are the period relationships between what two data series? (Points : 3) Seasonal moving averages and the trend data series. Smoothed data from centered moving averaging and the original data series. Trend data and the cycle factors. Trend data and the original data series.
- 13. 18. If sales growth and market penetration for a new product are expected to occur rapidly due to low product price and “need to have” technology which forecast model would you apply? (Points : 3) Logistics S-curve Gompertz S-curve 3 period Moving Averages Double Exponential Smoothing 19. You have forecast the sales for your company for the last 12 months and the forecast residuals are shown below. Are these residuals to be considered random? (This data also appears in the Doc Sharing excel worksheet download for Exam 1 Data under the Problem 19 tab.) Residuals -24 -348 -892 -62 -378 -489 -342 34 490 23 578 198 (Points : 4) Yes, since the residuals randomly vary in magnitude. Yes since the residuals are positive and negative and vary in magnitude. No, since the residuals are stationary and vary in magnitude. No, since the residuals indicate positive slope.
- 14. 20. Given the data series below for variables Y (Monthly Inventory Balance) and X (Monthly Sales) are they significantly correlated at the 95% confidence level and how can you tell? (This data also appears in the docsharing download for Exam 1 Data excel worksheet under the Problem 20 tab.) Ending Inv. Bal. Y Monthly Sales X 1544 5053 1913 5052 2028 7507 1178 2887 1554 3880 1910 4454 1208 3855 2467 8824 2101
- 15. 5716 (Points : 4) Yes. The correlation coefficient is .873 that is greater than .05. Yes. The correlation p-value is .002 which is less than .05. No. The correlation coefficient is above the p-value. No. The correlation p-value is greater than the 95% confidence level. 21. From the monthly sales data series below which exponential smoothing model would you apply? (This data also appears in the docsharing excel worksheet download for Exam 1 under the problem 21 through 30 tab.) Sales 6028 5927 10515 32276 51920 31294 23573 36465 18959 13918 17987 15294 16850 12753 26901 61494 147862 57990 51318 53599
- 18. 195409 173518 181702 258713 (Points : 4) Simple Double Winters Moving Averages 22. Run the data with the exponential smoothing model that applies and obtain the best model by adjusting each of the coefficients. (Make sure that you only use one decimal place for each coefficient – e.g. .1, or .2, or .3 …. to .9.) What coefficient value for Alpha will result in the best exponential smoothing result in the model selected? (Points : 4) .1 .5 .9 .2 23. What is the RMSE for the Fit period for the best exponential smoothing model? (Points : 4) 22634 38693 12971 20 24. Use the best exponential smoothing model to generate a forecast for 12 months. What is the forecast value for the 12th month? (Points : 4) 280762
- 19. 85095 250981 46840 25. Are the residuals from the best exponential smoothing model random and how can you tell? (Points : 4) No, since they still have significant seasonality. No, since they still have significant trend. Yes, since they are normally distributed with a near zero mean. Yes, since none of the residuals is significantly autoregressive. 26. Use the same monthly sales data series and run a decomposition model and estimate 12 forecast periods. Which month has the greatest seasonal sales? (Points : 4) Month 1 Month 12 Month 4 Month 5 27. What is the MAPE for the decomposition model? (Points : 3) 24% 22% 29% 24341 28. What is the forecast value for the 12th period (last forecast month). Do not adjust if for cycle factors. (Points : 4) 87838 221239
- 20. 353622 181473 29. Are the decomposition residuals random? Why or why not? (Points : 4) No. They still have seasonality. No. They still have significant trend. Yes. They are normally distributed with a near zero mean. Yes. None of the residuals are significantly autoregressive. 30. What is the forecast value for the 12th period (last forecast month) adjusted for cycle? (Points : 3) 223746 353622 665423 282712 Bottom of Form