Final answer:
The question pertains to the use of exponential smoothing forecasts, a statistical modeling technique for predicting outcomes in time series data, such as stock market indices. The application of this technique in economics and other fields employs a recursive filtering approach, which applies exponentially decreasing weights to past observations. Option C is the correct answer.
Step-by-step explanation:
The question is about using exponential smoothing forecasts in time series analysis, which is a statistical modeling technique used for making predictions. Exponential smoothing is a rule of thumb technique for smoothing time series data, especially for recursively applying as many as three low-pass filters with exponential window functions. Such techniques apply weights that decrease exponentially over time and are used in various fields, including economics, for predicting trends like stock market movements.
In scenario 7, an economist is essentially using such predictive techniques - presumably exponential smoothing - to forecast the stock market outcomes. The ultimate goal for the economist is to refine the model to best fit the actual data.
For question 12.4 about predicting sales growth, the given regression model (îy = 101.32 + 2.48x) is used for making predictions. On day 60, the sales prediction would be îy = 101.32 + (2.48 * 60) = 250.12 thousand dollars, and for day 90, it would be îy = 101.32 + (2.48 * 90) = 324.12 thousand dollars (rounded to two decimals).
The term that describes the use of mathematical equations in the modeling of linear aspects of ecosystems, as mentioned in question 8, is a. analytical modeling.
To address the entire question, the correct option for describing the calculation of the exponential smoothing forecast is C) Statistical modeling technique.