Regression Analysis

Name of the Student
Professor’s Name
Statistics
19th December 2015
Regression Analysis
Introduction
Linear Regression is a statistical inference, from where the cause and effect relationship between two variables are assessed. It helps to predict the magnitude of one variable from the magnitude of another variable. The former variable is called criterion and the later variable is called criterion. In this article the price of real estate was estimated from the wise year prices.
Methodology
Sales data was considered from the hyperlink: https://netfiles.umn.edu/users/nacht001/www/nachtsheim/Kutner/Appendix%20C%20Data%20Sets/APPENC11.txt
Data was randomly selected and aligned to R software package. The dependent variable (criterion) was real estate price (price) while the independent variable was assessment year (year).
Hypothesis Testing
Null hypothesis contends that there is no significant relation between the two variables and one variable cannot be extrapolated to find out the value of other variable (p>0.05). On the other hand, the alternate hypothesis contends that there is significant relation between the two variables and one variable can be extrapolated to find out the value of other variable (p<0.05). Further, the relationship between the two variables may be positive or negative. This means the value of one variable may increase the value of another variable (positive correlation) or increasing the value of one variable may decrease the value of…