Econometria

KAUNAS UNIVERSITY OF TECHNOLOGY

Simple Linear Regression

ECONOMETRICS


MIGUEL ANGEL ABALOS MEDINA

02/12/2011






 
Contents
• Introduction 3
• General4
• The major techniques used in simple linear regression analysis 4
• Scatterplot and interpretation 4
• Estimate the regression line through 5
• Regression line by the method of least squares6
• Verification of the estimation equation 7
• Standard error of estimate 7
• Shortcut method for calculating the standard error of the estimate8
• Interpretation of the standard error of the estimate 8
• Confidence intervals using standard deviation 8
• Relationship between level of trust and confidence interval 9
• Approximate prediction intervals9
• Correlation Analysis 10
• Coefficient of determination 11
• Another interpretation of r212
• The correlation coefficient 12
• Conclusion 13
• Bibliography14



















Introduction
Econometrics has been defined as "the application of mathematics and statistical methods to economic data" and described as the branch of economics" that aims to give empirical content to economic relations." More precisely, it is "the quantitative analysis of actual economic phenomenabased on the concurrent development of theory and observation, related by appropriate methods of inference. The first known use of the term "econometrics" (in cognate form) was by Paweł Ciompa in 1910. Ragnar Frisch is credited with coining the term in the sense that it is used today.

In statistics, simple linear regression is the least squares estimator of a linear regression model with a singleexplanatory variable. In other words, simple linear regression fits a straight line through the set of n points in such a way that makes the sum of squared residuals of the model (that is, vertical distances between the points of the data set and the fitted line) as small as possible.
The adjective simple refers to the fact that this regression is one of the simplest in statistics. The fitted linehas the slope equal to the correlation between y and x corrected by the ratio of standard deviations of these variables. The intercept of the fitted line is such that it passes through the center of mass (x, y) of the data points.
Other regression methods besides the simple ordinary least squares (OLS) also exist (see linear regression model). In particular, when one wants to do regression by...