REGRESSION ANALYSIS 2
Regression Analysis
Regression is a statistic concept for data analysis that shows the relationship among a set
of variables. It finds its application in various institutions that deal with numbers like in business,
research centers, finance, and engineering among others. The primary use of regression
technique is for prediction and establishing an inference from a given sample. Therefore, it is
vital to understand the interpretation of regression analysis results and the usefulness of
statistical model and coefficient estimates, which forms the focus of this essay.
Hand calculation accomplishes the regression analysis but is also done using statistical
software such as Excel, SPSS, and Stata, etc. Regression analysis utilizes equations that indicate
the relationship between dependent and independent variables (Ray, 2015). Expression of the
equation depends on the model in play. Some of the models include linear regression for one
single independent variable, multiple regression for more than one independent variable, and
logistic regression. The general regression equation is of the form Y = C
1
X
1
+ C
2
X
2
+ …. + A,
where Y stand for the dependent variable whose value is calculated from the equation. X
i
is the
individual independent variable, C the coefficients and A is a constant, i.e., the value of y when
all the independent variable are equated to zero. As an illustration, consider the SPSS model
results for estimating the outcome of job performance, the equation formulated by taking into
account the b-coefficient (column 1) establishes the relationship of the variables and hence
allowing estimation of the job performance of any worker.
Job performance = 18.13 + 0.265 x IQ-test + 0.308 x Motivation-test + 0.164 x social-support-
test …. (1)
