Multi line regression Statistics Project Example | Topics and Well Written Essays - 1250 words

Multi line regression - Statistics Project Exampleatly depends on statistics in order to steady down how well a country is doing in terms of trading with the relevant partners and in enhancing economic growth. In this task, the chosen myrmecophilous variable was the United States of America exports, the dependent variables selected are oil prices, USA car prices and technological product prices. It is assumed that the selected independent variables have a direct relationship with the dependent variable. For instance, car prices get out determine if the exports of the same will be higher, especially when compared to prices of cars from other countries such as Japan. In order to understand how the independent variables impact on the dependent variable, aggregate regression analysis is usually utilized. Regression is a statistical analysis that is used to evaluate the association or relationship between continuous dependent and continuous independent variable (Chatterjee & Simonof f, 2013). Usually the regression analysis helps establish a number of issues such as if a relationship exists between variables, the posture of the association, the structure or form of the relationship, as well as help in predicting the values of the dependent variable and controlling for other dependent variables. This makes regression superior to correlation analysis.Ideally, regression coefficients depict the mean, variance or change variables under investigation variable for one unit of change in the prognosticator variable while holding other predictor variables constant in the same model. With regards to coefficients above, it is evident that when oil prices and technical product prices are held constant, the amount of exports will increase by 6.94, when car prices, and technical product prices are held constant, then exports will increase by 4.27 and lastly, when car prices and oil prices are held constant, exports will edit by -0.52.R-square value stood at 0.9472. Coeffi cient of determination adjusted for the degree of freedom denoted as