Analysis Of Regression and Forecasting Models

Analysis Of Regression And Forecasting Models
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Illustrate the importance of using regression models. Discuss and differentiate among the different forecasting techniques and models. Explain cautions, mistakes, and pitfalls to watch out for, in the use of regression and forecasting models. Provide alternative methods to avoid making erroneous and costly decisions. Provide examples of real-world applications.
The association between the dependent variables and independent variable(s) is undertaken in regression analyses and helps to make predictions. Regression helps to establish whether there is a pattern or trend between variables, which is useful in forecasting and decision making. Since the models help to understand change, they are potentially beneficial to decision makers who are better placed to manage and control some variables.
The main quantitative causal forecasting techniques are: straight-line, moving average, simple linear regression, and multiple linear regressions. The straight-line approach assumes that there is a straight line that can be identified when historical figures are used to predict the future. In the moving average the arithmetic means of groups are calculated and there is elimination of fluctuations to get the smother trend line in the time series (Wilcox, 2009). Unlike the straight line approach, the moving average focuses on seasonal tr...