# Regression Analysis

An essay on regression analysis helps one understand and find the relationship between an independent variable and a dependent variable. In addition to that, an essay on regression analysis is mainly used in predicting and forecasting so as to identify which one of the independent variables is linked to the dependent variable. Like any statistics essay, however, it is not that easy to compose since computation of data is highly involved and mathematical formulae employed. In such a case, the data collected must be variables.
In most cases a graphing calculator or software package will be needed to perform the computations. Models are then used to analyze the data along with the computations. Regression models are normally useful even if the assumptions are breached to some extent. While writing this type of statistics essay, it is important to describe the hypothesis especially when it involves interactions. This will go a long way in making the interpretation easier. The analyses should be in descending order of importance. Caution should be taken to avoid missing data because this can be time consuming.
The essay on regression analysis has been increasingly used in the recent past because of the flexibility and power of regression as a technique of analysis. Essays on regression analysis have not only been used in the field of survey, but in other fields such as business, to analyze the businesses’ activities to increase organizational performance and profitability as well as reduce unneeded expenditure. Such essays have also been widely used in medicine, demography, et al. Caution should be taken when writing an essay on regression analysis to avoid unintended misrepresentation of the analysis and possible undesired consequences especially when dealing with assumptions.

## Regression Analysis essay example

Regression analysis is the most important tool of statistical analysis in use today. It can be found in almost all academic thesis, government planning documents and privates sector researches. Its level of usage is unprecedented and a common requirement for anyone seeking a post graduate degree.
The level of importance of this statistical tool can also be shown by the time in which it has developed into a ubiquitous tool of analysis. It was developed in 1805 and in its earliest form was only composed of the ordinary least squares method of analysis. Its development is credited to Legendre and Gauss. 1805 may seem a long time ago but for mathematical inventions and discoveries that came into being even before the birth of Christ this is recent. Its spread has been rapid. It took hundreds of years for the number zero to be used globally but regression did it in less than two centuries.
Other than time as an indicative factor of the importance and wide spread use of regression is the dependence which most companies and government have placed upon it for projection and prediction capabilities (Friedman 2001). Most governments use it to project valuable economic indicators such as population growth rates, economic growth rates, poverty rates and unemployment levels amongst other indicators. Governments then allocate resources, develop institutions and develop policy frameworks according to these predictions and forecasts.
The private sector makes projections as to their future revenues, profits and sales as to remove uncertainty and insert more predictability in their future cash flows. Consequently the private sector will alter their production levels, investment schedules and sales strategy according to the prediction generated by regression analysis.
This sheer dependence on a single mathematical and statistical tool vis-à-vis the level and amount of resources allocated or re-allocated as a result of it is a true testament to the importance of regression analysis in today’s society (Fisher, 1855).