Strengths and Limitations of Regression Analysis Using Linear Programming
An individual will collect data parting the question at hand and thereafter employs regression to estimate the quantitative effect of a causal variable in comparison with the variable that they directly or indirectly influence their performance, (Wendorf, 2004). This method of data analysis has been in operation for an extended period especially in the economic statistics. Regression has also become popular among lawyers in the recent past, being used as evidence of liability under Title VII of the Civil Rights Act 1964. Regression analysis while handling a single explanatory variable is characterized as a simple regression. (Fox, 1997) Regression analysis will be used when it is determined that two or more variables have a connection in a linear relationship, Hinkle, (1996). When we focus on simple regression, it means that we have only two variables in question. The variables will be denoted by X and Y. and the relationship can be determined as. y = ß0 + ß1 x + έ, this is a linear equation because it will result in a straight line when presented in a graph, (Fox, 1997). Data analysis is an activity we cannot escape from in our day to day activities. Data may be realized from the business activities such as their mode of supply or transport. Businesses will, therefore, seek to obtain the most favorable model by analyzing the data. The kind of analysis is called The linear regression analysis, (Fox, 1997).The regression analysis has a number of strengths, first, according to Fox (1997), regression method of data analysis is beneficial in circumstances where forecasts are promising, such as the number of intended admissions in a college. This methodology assists administrators to predict and get ready for additional demands that are likely to arise in the future Regression analysis is also a quite cheaper method as data are easily collected and usually can be gathered from another earlier source. Furthermore, independent variable data are not expensive to update as compared to acuity-quality data, (Fox, 1997).