AN 300 FINAL EXAM QUESTIONS WITH CORRECT ANSWERS LATEST UPDATE 2026

AN 300 FINAL EXAM QUESTIONS WITH CORRECT ANSWERS LATEST UPDATE 2026 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. - Answers -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 - Answers -Trend -Seasonality A recurring pattern that occurs at set periods within a larger time frame. - Answers Seasonality A gradual increase in values over time. - Answers Upward Trend A gradual decrease in values over time. - Answers Downward Trend A constant average value over time. - Answers 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. - Answers -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. - Answers Naive It adapts readily to sudden shifts in data pattern. - Answers Naive It requires the data points in the time series as well as the number of periods used in forecasting. - Answers Simple moving average It is a weighted average of all prior historical actual vales. - Answers Exponential smoothing average It uses the "best fit" linear trend line to make predictions. - Answers Linear Regression It uses indicator variables to capture variations. - Answers Linear regression for seasonality Which of the following statements about time-series forecasting methods is TRUE? Check all that apply. -Linear Regression is the method of choice for data with a trend pattern. -You choose a large value for alpha when using the Exponential Smoothing method to give less weight to the most recent data. -Linear Regression for Seasonality without Trend method is appropriate for data with a seasonal pattern only. -There is no time-series forecasting method for data with both seasonal and trend patterns. -You choose a small value for "k" when using the Simple Moving Average method of order "k" to track movement in the most recent data. -Linear Regression uses the time period as the dependent variable. -The Simple Average method is also known as the Historical Moving Average method. - Answers -Linear Regression is the method of choice for data with a trend pattern. -Linear Regression for Seasonality without Trend method is appropriate for data with a seasonal pattern only. -You choose a small value for "k" when using the Simple Moving Average method of order "k" to track movement in the most recent data. -The Simple Average method is also known as the Historical Moving Average method. Given the following sales data (in $000) for C&A's product: January -15 February -18 March -14 April -16 May -13 June -16 1. What is the naive forecast for June? 2. What is the historical moving average forecast for July? (rounded to 2 decimal places) 3. What is the simple moving average forecast of order 3 for June? (rounded to 2 decimal places) 4. What is the simple moving average forecast of order 1 for June? 5. What is the exponential smoothing forecast for July if alpha = 0.2 and the forecast for February is 15? (rounded to 2 decimal places) 6. The simple moving average method of order 1 is the same as the Naive method. (T/F) - Answers 1. 13 (Apply May to June) 2. 15.33 (All months sales/number of months) 3. 14.33 ((14+16+13)/3) 4. 13 (may one back) 5. 15.15 (Forecast for March = .2*Actual Feb + (1-.2)*Forecast Feb ... this continues through the months until reaching July) Forecast April (.2*14 + .8*march forecast)... 6. T Given the following actual and forecast data: January -Actual 1250 , Forecast 1050 February -Actual 1200 , Forecast 1133.33 March -Actual 1280 , Forecast 1183.33 April -Actual 1300 , Forecast 1243.33 May -Actual 1350 , Forecast 1260 June -Actual 1300 , Forecast 1310 What is the forecast error for June? :-90 :-10 :10 :90 - Answers : -10 Given the following actual and forecast data: January -Actual 1250 , Forecast 1050 February -Actual 1200 , Forecast 1133.33 March -Actual 1280 , Forecast 1183.33 April -Actual 1300 , Forecast 1243.33 May -Actual 1350 , Forecast 1260 June -Actual 1300 , Forecast 1310 What is the absolute error for June? : 90 : 10 : -10 : -90 - Answers : 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 - Answers -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. - Answers Cumulative Forecast error It averages all the errors available. - Answers Mean Absolute error It sums up all the squared error available. - Answers 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. - Answers Absolute percentage error It takes the average of the absolute percentage error values over a range of time periods. - Answers Mean absolute percentage error Which of the following statements about CFE and MFE is FALSE? Check all that apply. -MFE stands for Mean Forecast Error. -The biases are aggregated. -CFE stands for Cumulative Forecast Error. -They are unitless. -Positive errors may be offset by the -negative ones. -CFE is the scaled down version of MFE. -A positive CFE means the forecasting method is biased towards overestimating. -The least biased CFE/MFE is preferred. -They cannot be negative. - Answers -They are unitless. -CFE is the scaled down version of MFE. -A positive CFE means the forecasting method is biased towards overestimating. -They cannot be negative. Which of the following statements about MAE and SSE is FALSE? Check all that apply. -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. -Lower values are preferred. -Errors do not cancel out each other. -None can be negative. -MAE stands for Mean Absolute Error. -A positive value means the forecasting method is biased. - Answers -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. - Answers -It is unitless.