Section A: Forecasting Techniques

What is an irregular pattern?

An irregular pattern is a fluctuation caused by short-term, nonrecurring factors.

What are the three ways to use smoothing in calculating a forecasted amount?

Moving average Weighted moving average Exponential smoothing

What are moving average, weighted moving average, and exponential smoothing as used in forecasting?

Moving average uses the average of the most recent data in the time series. A weighted moving average also uses the most recent data in time series, but uses different weights for each data value. Exponential smoothing forecasts a value for the next period by calculating a weighted average of 1) the most recent period's actual value, and 2) the most recent period's forecasted value, as calculated using exponential smoothing.

What are the benefits of regression analysis?

Regression analysis is a quantitative method and as such it is objective. A given data set generates specific results. The results can be used to draw conclusions and make forecasts. Regression analysis is an important tool for budgeting and cost accounting. In budgeting, it is virtually the only way to compute fixed and variable portions of costs that contain both fixed and variable components (mixed costs).

What is multiple regression analysis?

Regression analysis using multiple independent variables to forecast a dependent variable is called multiple regression analysis. Multiple regression analysis is another type of causal forecasting.

What is the coefficient of correlation (r)?

The coefficient of correlation (r) is a numerical measure that expresses both the direction (positive or negative) and the strength of the linear association between two variables. This amount of correlation is expressed as a number between −1 and +1.

What is the coefficient of determination (r2)?

The coefficient of determination (r2) is the percentage of the total amount of change in the dependent variable (y) that can be explained by changes in the independent variable (x). r2 is the square of the coefficient of correlation and is expressed as a number between 0 and 1.

What is the equation of a linear regression line?

The equation of a linear regression line is: ŷ = ax + b Where: ŷ = the predicted value of y on the regression line corresponding to each value of x a = the slope of the line b = the y-intercept, or the value of y when x is zero (0) x = the value of x on the x-axis that corresponds to the value of y on the regression line

What are the disadvantages of exponential smoothing?

* Its forecast will lag behind as the trend increases or decreases over time. * It does not account for dynamic changes that occur in actual practice. Its forecasts will require constant updating in order to respond to new information. * Each month's forecast can be determined only after the month has begun because it is only then that actual data from the previous month will become available.

What is a cyclical pattern?

A cyclical pattern is a recurring fluctuation that persists over more than one year. A cyclical pattern is usually due to the cyclical nature of the economy.

What is a seasonal pattern?

A seasonal pattern is a fluctuation due to seasonality in a business.

What is a trend pattern?

A trend pattern is a gradual shifting to a higher or lower level over a long period of time.

What is a trend projection and when should a trend projection be used?

A trend projection is done with simple regression analysis, which forecasts values using historical information from all available past observations of the value. Trend projection should be used when a time series has a consistent long-term upward or downward trend.

What are the limitations of regression analysis?

* To use regression analysis, historical data is required for the variable that is being forecast or for the variables that are causal to this variable. If historical data is not available, regression analysis cannot be used. * Even when historical data is available, its use is questionable for predicting the future if a significant change has taken place in the conditions surrounding that data. * In causal forecasting, the usefulness of the data generated by regression analysis depends upon the choice of independent variable(s). If the choice of independent variable(s) is inappropriate, the results can be misleading. * The statistical relationships that can be developed using regression analysis are valid only for the range of data in the sample.

What are the two basic assumptions of simple regression analysis?

1 Changes in the value of the dependent variable can be explained by changes in the level of the independent variable. 2 The relationship between the dependent variable and the independent variable is linear.

What are the two ways that time series methods are used in forecasting?

1 Smoothing (moving averages, weighted moving averages, and exponential smoothing) 2 Trend projection (including trends adjusted for seasonal influence)

What are the two basic forecasting methods?

1 Time series methods, which look only at the historical pattern of one variable and generate a forecast by extrapolating the pattern using one or more of the components (or patterns) of the time series, and 2 Causal forecasting methods, which look for a cause-and-effect relationship between the variable we are trying to forecast (the dependent variable) and one or more other variables (the independent variables).

Simple linear regression analysis relies on what two assumptions?

1 Variations in the dependent variable (i.e., what we are forecasting) are explained by variations in one single independent variable (i.e., the passage of time, if a time series is what we are forecasting). 2 The relationship between the independent variable (time or a specific value) and the dependent variable (sales or whatever we are forecasting based on the value of the independent variable) is linear.

When are causal forecasting methods used?

Causal forecasting methods are used when we can determine that the value we are forecasting is affected by some other value. If we can identify a cause-and-effect relationship between the other value and the value we are forecasting, and if that relationship is a linear one, we can use a projection of the other value (as the independent variable) to forecast the value we are interested in (the dependent variable).

What is the main advantage of exponential smoothing?

The main advantage of exponential smoothing is that it does not require a great deal of historical data. Therefore, it is an inexpensive method to use when multiple forecasts need to be made every period.

What is the t-statistic (t-value)?

The t-statistic, or t-value, measures the degree to which the independent variable has a valid, long-term relationship with the dependent variable.

The t-value for the independent variable used in simple regression should generally be greater than __.

The t-value for the independent variable used in a simple regression should generally be greater than 2. A value below 2 indicates little or no relationship between the independent variable and the dependent variable, and thus the forecast resulting from the regression analysis should not be used.

What is time series analysis?

Time series analysis looks at patterns of the desired variable over time. These patterns from the past are then used to forecast a future result.

What are the four patterns that a time series can have that influence its behavior?

Trend Cyclical Seasonal Irregular