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AN 300 Final Exam
Which of the following statements about time-series forecasting is TRUE? Check all that apply. -It only works with trend data patterns. -It is a predictive analytics method. -It is a predictive analytics technique. -It applies to strategic planning by predicting market growth rate. -It needs data on observations of an item of interest over time. -It answers what should happen questions. -It predicts the future outcome of the item of interest. - -It is a predictive analytics technique. -It applies to strategic planning by predicting market growth rate. -It needs data on observations of an item of interest over time. -It predicts the future outcome of the item of interest. What kind of data pattern is depicted in the following line graph? Check all that apply. Its a graph with a straight red line angled positively. There is a Blue line that begins below the red line and then had alternating periods above and below the line. -Trend -Random -Level -Seasonality -Cycle - -Trend -Seasonality A recurring pattern that occurs at set periods within a larger time frame. - Seasonality A gradual increase in values over time. - Upward Trend
A gradual decrease in values over time. - Downward Trend A constant average value over time. - Level Which of the following is a business application of time series forecasting? -Sales forecast of swimsuits from advertising expenditure. -Sales forecast of swimsuits from a survey of vacationers. -Sales forecasts of swimsuits from past records. -Sales forecast of swimsuits from demand of sunglasses. - -Sales forecasts of swimsuits from past records. Match the characteristics on the left with the most appropriate forecasting methods to which it applies on the right. It requires only one historical data value. - Naive It adapts readily to sudden shifts in data pattern. - Naive It requires the data points in the time series as well as the number of periods used in forecasting. - Simple moving average It is a weighted average of all prior historical actual vales. - Exponential smoothing average It uses the "best fit" linear trend line to make predictions. - Linear Regression It uses indicator variables to capture variations. - Linear regression for seasonality Which of the following statements about time-series forecasting methods is TRUE? Check all that apply.
- What is the exponential smoothing forecast for July if alpha = 0.2 and the forecast for February is 15? (rounded to 2 decimal places)
- The simple moving average method of order 1 is the same as the Naive method. (T/F) - 1. 13 (Apply May to June)
- 15.33 (All months sales/number of months)
- 14.33 ((14+16+13)/3)
- 13 (may one back)
- 15.15 (Forecast for March = .2Actual Feb + (1-.2)Forecast Feb ... this continues through the months until reaching July) Forecast April (.214 + .8march forecast)...
- T Given the following actual and forecast data: January -Actual 1250 , Forecast 1050 February -Actual 1200 , Forecast 1133. March -Actual 1280 , Forecast 1183. April -Actual 1300 , Forecast 1243. May -Actual 1350 , Forecast 1260 June -Actual 1300 , Forecast 1310 What is the forecast error for June? :- :- : :90 - : - Given the following actual and forecast data: January -Actual 1250 , Forecast 1050 February -Actual 1200 , Forecast 1133.
March -Actual 1280 , Forecast 1183. April -Actual 1300 , Forecast 1243. May -Actual 1350 , Forecast 1260 June -Actual 1300 , Forecast 1310 What is the absolute error for June? : 90 : 10 : - : -90 - : 10 Which of the following is NOT one of the forecast error metrics that you learned in this class? -Sum of Squared Errors -Mean Absolute Percentage Error -Mean Forecast Error -Tracking signal -Cumulative Forecast Error - -Tracking signal Match the definition on the left with the forecasting error metric to which it applies on the right. It sums up all the errors available. - Cumulative Forecast error It averages all the errors available. - Mean Absolute error It sums up all the squared error available. - Sum of squared error It divides the absolute error of a period by the actual value of that period. The resulting quotient is then multiplied by 100. - Absolute percentage error
-MAE stands for Mean Absolute Error. -A positive value means the forecasting method is biased. - -SSE is the scaled down version of MAE.
- They can be used to compare two applications of the same forecasting method. -SSE stands for Sum of Standard Errors. -MAE is always greater than SSE. -They are unitless. -A positive value means the forecasting method is biased. Which of the following statements about MAPE is TRUE? Check all that apply. -It is unitless. -It can be negative. -It can be used to compare two applications of the same forecasting method. -It can tell whether or not the method is biased. -It stands for Mean Average Percentage Error. -It has no error cancellation problem. - -It is unitless. -It can be used to compare two applications of the same forecasting method. -It has no error cancellation problem. Which of the following is performed to modify the data from the Data Visualization Application for time series forecasting in this application? Check all that apply. -Change the data type of the Month column from numeric to categorical. -Remove columns that are irrelevant. -Add indicator columns for seasonality. -Add a column to aggregate the number of COVID deaths by month. -Add a column to account for the time period in the series. - -Remove columns that are irrelevant. -Add indicator columns for seasonality.
Which of the following time-series forecasting smoothing methods is selected as least biased in this application to track the recent movement of data to forecast COVID mortality? -Simple average -Exponential smoothing with alpha = 0. -Naive -Linear regression -Moving average with k=3 - -Naive Which of the following time-series forecasting smoothing methods is selected as most accurate in this application to track the recent movement of data to forecast COVID mortality? -Naive -Linear regression -Simple average -Moving average with k= -Exponential smoothing with alpha = 0.9 - -Simple average Match the kind of time series forecasts to be generated on the left with the most appropriate linear regression model on the right. Time series forecasts for seasonality. - A multiple regression model with indicator variables as the independent variables Time series forecasts for trend. - A simple regression model with time period as the independent variable. Time series forecasts for seasonality with trend. - A multiple regression with indicator variables and time period as the independent variables.
You want to capture the seasonal variations of spring, summer, fall, and winter on sales of swimsuits using a regression model. Which of the following statements is TRUE? Check all that apply. -Three dummy variables should be used in the model. -A simple regression model should be used. -Four dummy variables should be used in the model. -A multiple regression model should be used. - -Three dummy variables should be used in the model. -A multiple regression model should be used. Which of the following is true about the estimating regression process? Check all that apply. -It estimates the intercept and slope of the regression equation. -It uses partial least square method. -It minimizes the sum of the squared differences between the observed and predicted values of the dependent variable. -There is a random error term in the estimated equation. -It estimates only the slope of the regression equation. - -It estimates the intercept and slope of the regression equation. -It minimizes the sum of the squared differences between the observed and predicted values of the dependent variable. Which of the following is not a goodness of fit measure for linear regression? -Standard error of the estimate -Coefficient of determination -Adjusted coefficient of determination -Standard coefficient - -Standard coefficient Match the description on the left with the appropriate goodness of fit measure on the right.
It is the standard deviation of the residue. - Standard error of the estimate The closer its value to zero, the better the model fit. - Standard error of the estimate The proportion of the sample variation in the dependent variable that is explained by the estimated regression equation. - Coefficient of determination The closer its value to one, the better the model fit. - Coefficient of determination Its value can be negative. - Adjusted coefficient of determination It should be used to compare models with different numbers of independent variables. - Adjusted coefficient of determination Which of the following is a data requirement for linear regression. Check all that apply. -The dependent variable must be categorical in value. -There are multiple columns of dependent variables. -The dependent variable must be numerical in value. -The variables must be organized in a table with each variable as a row. -The independent variables must be numerical in value. - -The dependent variable must be numerical in value. -The independent variables must be numerical in value. The relevant information from the regression report includes _________. Check all that apply. -The predicted value. -The regression equation. -The goodness-of-fit measures. -The slope coefficients for the independent variables.
Which of the following is a data pattern in time-series data that can be uncovered by time-series forecasting? Check all that apply. -Trend -Level -Random -Horizontal -Cycle -Vertical - -Trend -Level -Horizontal -Cycle Indicate whether the following statements are T/F. A high value of alpha places more weight to recent data in generating the next period's exponential smoothing forecast. - T Linear regression adapts to sudden shifts to data patterns. - F Naive method needs only one historical data point to be given. - T Smoothing method is appropriate for data with a trend pattern. - F Historical moving average method is also known as simple average method. - T A large number of time periods should be used as k in simple moving average forecast of order k in order to track movement in the most recent data points. - F
A linear trend line with a positive slope indicates a gradual upward shift in data values. - T A linear trend line cannot have a zero intercept. - F Four dummy variables should be used in the linear trend line equation to represent the effect of each of the four seasons on the forecast. - F Time period is an independent variable in a linear regression with trend model. - T Match the situation on the left with the appropriate forecast error metrics on the right. You want to know if the forecast method is biased or not. - Mean forecast error You have a negative forecast error. - Mean forecast error You want to compare two different applications of the same forecasting method. - Mean absolute error percentage You want to emphasize large errors. - Sum of squared errors The actual demand and forecast demand for January is 3 and 3 respectively. What is its forecast error? - 0 The actual demand and forecast demand for January is 5 and 37 respectively. What is its forecast error?
It is used to predict the outcome of a variable of interest based on the value of one variable. - Simple Regression It is the variable of interest being predicted. - Dependent variable
A. What kind of data pattern is observed in the given sales data? B. What is the Simple Average forecast for May? C. What is the Exponential Smoothing forecast for April if α= 0.8 and the forecast for February is the actual of January, i.e., 42.74? D. Naïve method requires the least amount of historical data to perform forecasting (T/F). E. For Simple Moving Average method, an order (i.e., k) of four yields the same forecasted value for May as the Simple Average method (T/F). F. For Exponential Smoothing method, an αclose to zero should be used to give more weight to recent data (T/F). G. Linear Regression with trend adapts well to sudden shift in data pattern (T/F). - A. level B. 42.74+40.24+35.85+43.37/4 = 40. C. Forecast for March = (0.840.24+0.242.74 = 40.74); (Forecast for April = 0.835.85_0.240.74 =
D. T -only need one data point E. T - 40.54 is the may forecast F. F 0< a < 1, a value close to zero means less weight is given to the most recent data. G. F - it works well for trend data pattern Given the following actual and forecast sales data for C&A's chocolate: Month / Sales ($ Million) / Forecast Jan / 42.74 / - Feb / 40.24 / 42. Mar / 35.85 / 40. Apr / 43.37 / 36. May / 42.45 / 42. A. What is the Forecast Error for February, March and April? B. What is the Mean Forecast Error (MFE) for April? C. What is the Absolute Percentage Error (APE) for February and March?
D. What is the Mean Absolute Percentage Error (MAPE) for March? E. The forecast for March is negatively biased (T/F). F. The forecast over-estimates the actual in April (T/F). G. An APE of100 means that its forecast and actual values are the same (T/F). H. MAPE can be negative (T/F). - A. FE_Feb = 40.24 - 42.74 = 2.5; FE_Mar = 35.85 - 40.74 = -4.92; FE_April = 43.37 - 36.80 = 6. B. -2.5 - 4.92 + 6.57 / 3 = -0. C. APE_Feb = |40.24-42.74|/ 40.24 * 100 = 6.21; APE_Mar = |35.85-40.74|/35.85*100 = 13. D. 6.21+13.74/2 = 9. E. T - forecast error is negative which means it is negatively biased. F. F - forecast is less than actual which means it under-estimates the actual G. F - APE is 100 means the absolute error has the same value as the actual. H. F - absolute error cannot be negative which means APE/MAPE cannot be negative Which of the following statements concerning Linear Regression is TRUE? Check all that apply. A. Linear Regression can have more than one independent variable. B. Multiple Regression must have two or more dependent variables. C. The estimating process of linear regression is about describing the relationship between the outcome and input variables. D. The predicted value of the outcome can be equal to the intercept of a linear regression equation. E. We can use ordinary least squares to evaluate the goodness of fit of a linear regression model. F. The value of Coefficient of Determination can be 100. G. Adjusted Coefficient of Determination should be used to evaluate the goodness of fit of multiple regression models. H. The method that has the highest value of Root Mean Squared Error is the one with the best fit. - A. T - multiple regression has two or more independent variables, linear regression is one. B. F - linear regression has only one dependent variable. C. F - the estimating process is about determining the slope and intercept of the regression equation.
