Final answer:
If we want to determine the best technique for forecasting TFY demand, we need to compare the three methods mentioned: Regression Analysis, Residual Analysis, and Exponential Smoothing.
The answer is option ⇒1, 2 and 3
Step-by-step explanation:
1) Regression Analysis: This method involves fitting a regression model to the historical sales data and using it to predict future sales. The forecast is based on the relationship between the sales and other variables, such as time or seasonality. To evaluate the accuracy of the forecast, we can calculate measures of forecast error, such as Mean Absolute Error (MAE) or Root Mean Squared Error (RMSE). Lower values of these measures indicate better forecast accuracy.
2) Residual Analysis: This method involves analyzing the differences (residuals) between the actual sales and the forecasted values. By examining the patterns and distribution of the residuals, we can assess the accuracy and reliability of the forecast. Additionally, we can calculate measures of forecast error, such as MAE or RMSE, to quantify the accuracy of the forecast.
3) Exponential Smoothing: This method is a time series forecasting technique that assigns exponentially decreasing weights to past observations. It is particularly useful when there is a trend or seasonality in the data. To determine the best forecast, we can compare the forecasted values with the actual sales and calculate measures of forecast error, such as MAE or RMSE.
In summary, to determine the best forecasting technique for TFY demand, we need to compare the forecast errors obtained from Regression Analysis, Residual Analysis, and Exponential Smoothing. The method with the lowest forecast error would indicate the best forecast. Additionally, we can improve the forecast by incorporating additional variables and using more advanced techniques.
The answer is option ⇒1, 2 and 3
Your question is incomplete, but most probably the full question was:
Once you have calculated the forecasts based on the above data, determine the error terms by comparing them to the actual sales for 2020 given below:
Tires for You, Inc. Tires for You, Inc. (TFY), founded in 1987, is an automotive repair shop specializing in replacement tires. Located in Altoona, Pennsylvania, TFY has grown successfully over the past few years because of the addition of a new general manager, lan Overbaugh. Since tire replacement is a major portion of TFY's business (it also performs oil changes, small mechanical repairs, etc), lan was surprised at the lack of forecasts for tire consumption for the company. His senior mechanic, Skip Grenoble, told him that they usually stocked for this year what they sold last year. He readily admitted that several times throughout. the season stockouts occurred, and customers had to go elsewhere for tires.
Although many tire replacements were for defective or destroyed tires, most tires were installed on cars whose original tires had worn out. Most often, four tires were installed at the same time. lan was determined to get a better idea of how many tires to hold in stock during the various months of the year. Listed below is a summary of individual tire sales by month: Ian has hired you to determine the best technique for forecasting TFY demand based on the given data.
Based on the three methods used to calculate a forecast for TFY, which method produced the best forecast?
A) Regression Analysis
B) Residual Analysis
C) Exponential Smoothing
D) Forecasting Error