Estimating multiple regression model; GCC countries Statistics Project

Estimating multiple regression model; GCC countries - Statistics Project Example The assignment will conduct a multiple regression analysis using the data from observations for a period from 1999 – 2012. The multiple regression analysis is a technique of statistics that is used to develop a linear model for predicting the unknown value of a variable from known or assumed values of independent variables. In this case, the model Y = ∠« (Xa, Xb); where Y = GDP per capita, Xa = FDI, and Xb = Export is represented by the equation Y = b0 + b1Xa + b2Xb. The task of the multiple regression analysis is to determine the values of parameters a, b1, and b2. In order to it, the assignment collected data for Xa, Xb, and Y for years from 1999 – 2002. Table 1 presents data set; values will be used for regression analysis. The sample data of table 1 is collected from the annual reports published by the Qatar Central Bank (â€Å"Annual Reports†, n.d.) and Index Mundi (â€Å"GDP†, n.d,; â€Å"Exports†, n.d)). The set represent values of three variables collected from 14 observations. These values will be used to find a regression line that best fits the data illustrated in Table 1. The regression coefficients of variables as well the intercept of the regression equation are calculated using Excel functions. The parameter calculation method does not consider the noise in regression analysis. This study does not conduct sample data distribution phenomenon; however, it does conduct a study of Goodness of fit of the model. The given data are used to conduct descriptive and inferential analyses. Descriptive coefficients characterizes the data set; the concept represents the central tendency and dispersion of data taken from observations. Inferential analysis, in this case, describes the behavior and authentication of the model through hypothesis testing of regression parameters. Descriptive statistics of all three variable are evaluated using Excel built in function; they are presented in Table 2.