Understanding the Basis of Regression Analysis
When two or more variables are considers as methodically connected relationship, regression analysis is used. In a standard regression, you can see two variables and one is dependant of another. Now this particular analysis is actually done to foretell the dependency of the variable in connection with other independent variables. Although the dependent variable should continuous, the independent variable can be either continuous or dichotomous. The dichotomous state means that the variable is divided into two significant part or classification. Although, sometime there are dichotomous variable that is more than two parts.
Simply put, you can use an analysis of road accidents, which is the dependent variable (Y) in relations with the age of the drivers, which is the independent variable (X). When doing this analysis, by the terminology you can say the age of the driver can predict the occurrence of road accidents. However, you cannot insist that the age of the driver are the causes of road accidents.
There are two type of regression analysis. The first one would be a simple linear regression that uses one predictor. This regression model clarifies the relationship of the variable using a straight-line method or simple regression. In the case of more than one predictor, multiple regressions are used.
As in any scientific study, in regression analysis, you will need to determine the accuracy of the data. In the case of road accidents and the driver’s age, where do you think you can get the most accurate data set. You must make sure that you did not miss any significant data as well. This is important in multiple regressions analysis. You might analyse the drivers age but do you think that you should add the fact that the driver are male or female, when they had their driver license or whether they are foreigners who are not familiar with the roads. Then you need to decide whether to use those variables or not. However, some variable are so distinct that they becomes outliers. You need to determine whether these cases should be considers as part of your regression analysis. Furthermore, there should a sense or normality in your analysis. Apart from all these, there are many other aspects of a good analysis such linearity, homoscedasticity, transformations and many more.
For methods, there are also many of them. In fact, the study of regression itself is still active. In recent decades, new and better method had been develops. These consist on the study of the variables and predictors involve. Nonetheless, one cannot ignore the fact that regression analysis plays a big part in creating a reliable and good statistical data. These data in turns help many individuals or organization in making important decisions.
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