This is Multiple Linear Regression and Analyses also based on Econometric model and variables. It further explains state the economic and regression model.
Multiple Linear Regression and Analyses- Econometric model and variables
** Word File for Report**
** Excel file for the Multiple Linear Regression**
-For Excel File
Firstly select the data/issue with at least three variables/factors, including one as the Dependent Variable;
Secondly collect actual data and calculate summary of the data, including the mean(average) and standard deviations and correlations;
Thirdly state the economic and regression model;
Fourthly run the multiple linear regression and show the outcomes from that;
Lastly explain the whole model and each coefficient’s significance and why?
-For Word File
Multiple Linear Regression and Analyses- Econometric model and variables
(1) Introduction/background of the issue/topic;
(2) Econometric model and variables;
(3) Data-explanations, summaries/descriptive statistics and correlations among any two variables;
(4) Regression results and explanations-whether and why the whole model is significant and whether and why each coefficient is significant;
(5) Applications of the outcomes such as the meanings of the slope(s).
(6) summary
Multiple linear regression (MLR), also known simply as multiple regression, is a statistical technique that uses several explanatory variables to predict the outcome of a response variable. The goal of multiple linear regression (MLR) is to model the linear relationship between the explanatory (independent) variables and response (dependent) variable.
In essence, multiple regression is the extension of ordinary least-squares (OLS) regression that involves more than one explanatory variable.
Formula and Calcualtion of Multiple Linear Regression
yi=β0+β1xi1+β2xi2+…+βpxip+ϵwhere, for i=n observations:yi=dependent variablexi=expanatory variablesβ0=y-intercept (constant term)βp=slope coefficients for each explanatory variableϵ=the model’s error term (also known as the residuals)begin{aligned} &y_i = beta_0 + beta _1 x_{i1} + beta _2 x_{i2} + … + beta _p x_{ip} + epsilon\ &textbf{where, for } i = n textbf{ observations:}\ &y_i=text{dependent variable}\ &x_i=text{expanatory variables}\ &beta_0=text{y-intercept (constant term)}\ &beta_p=text{slope coefficients for each explanatory variable}\ &epsilon=text{the model’s error term (also known as the residuals)}\ end{aligned}
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