We believe that utilizing Regression Analysis will provide us with enough data to make an accurate prediction. Regression Analysis is simply put using the value of one dependent variable Y based on the data of other independent variable(s) X. To conduct a Regression Analysis, we need to input data from two variables and produce a Regression equation that describes the relationship between the dependent variable and the independent variable. A dependent variable is the variable to be forecast, i. e. what we want to predict.
There's a specialist from your university waiting to help you with that essay.
Tell us what you need to have done now!
order now
An independent variable is what we believe the dependent variable is based on. In this case, we are trying to determine the best regression model to predict a student’s University GPA (Y, the dependent variable) based on the grades they received in high school (X, the independent variable). There are currently two options for the Y, independent variable, and this result is made possible by two methods (Xs) to predict a student’s University GPA (Y): 1. Method 1 (X1): utilizes the student’s average from the BEST 6 courses taken in High School. .
Method 2 (X2): utilizes the student’s average of English and Calculus, plus the Best 4 courses taken in High School. The goal is to change University admission standards in accordance with the best Regression model utilizing either Method 1 or Method 2. To find the most efficient model, which predicts a student’s University GPA best, we must analyze two regression models based on each of the methods. When comparing the values we can conclude the BEST 4+EC model is a more effective model to predict University GPA.
The B4+EC model is 35% accurate. The B6 model is only 24% accurate. The R2 value (coefficient of determination) shows us the strength of the relationship between x and y. This relationship was stronger in the B4+EC model by . 113 GPA units. This translates to approx. 11% more accuracy when compared to the B6 Model. In addition the standard error of the Best 4+EC model is also lower than the standard error of the B6 Model. This tells us the model will be superior because the error is lower.
By interpreting the Regression Equation of the B4+EC model, we canunderstand how the model will help us predict University GPA by means of the scores of a student’s Best 4 subjects plus English and Calculus. The R2 value, told us the model is only 35% effective. By interpreting the regression equation : y= 0. 137x -3. 32, ß1 (slope) tells us that as the scores of a student’s B4+EC Courses increase by 1 point, the predicted University GPA (y) of that student will increase by . 137 point.
Although the Best 4+EC model is valid and the best model to be used out of our options, it should be noted that it is only 35% efficient at best. This is not an ideal model; there are other factors that are contributing to the predicted GPA that were not accounted for in this model. These factors account for 65%. The Best 4 model should be used currently until another more efficient method for predicting university GPA is found. Though the model is only 35% effective, it is the best solution at the moment.
