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Regression Analysis In Java

 
 
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Job Summary:
 
Job Type: Project
Budget: $ 20-100
Required Skills: Assembly / Machine language, Java, Programming
Attached Files: pr.docx
 
Status: Closed for bidding
 
Buyer Summary:
 
Username:
gem999  
Feedback Score: Rating: (No Feedback Yet)
Award Reliability: 2 posted 0 paid
 
Location: London, United Kingdom
Member Since: November 5, 2011
 
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Description
Regression analysis is an important computation technique used in Statistics. Regression analysis helps in understanding how the typical value of the dependent variable changes when any one of the independent variable is varied, while the other independent variables are held fixed. It is also used to forecast the dependent variable in the future based on past values of the dependent and independent variables. For example, we might want to predict the level of sales in the future for a product by studying the relationship between the sales (dependent variable) and the level of marketing (independent variable).


You have been asked by the Statistics Group to provide them with an application that performs linear regression analysis on a set of data for selling prices of houses. This dataset consists of the selling price which here is the dependent variable, Y and a set of seven independent variables (X1, X2,…,X7). The aim of the application is to help a user to understand the relationship between the selling price of a house and parameters such as no. of rooms, age of the house, area of the site, etc. Additionally, the user should be able to use this application to forecast the selling price of a house given a certain no. of rooms, the age of a house and so on. Regression analysis will be performed using the dependent variable, Y and one independent variable Xi at a time.


The application will provide the following functionalities:
• Selection of independent variable, Xi to be used for regression analysis.
• Plotting the scatter diagram of the (Xi,Y) value pairs (e.g. figure A).
• Calculation of the regression coefficients and display the regression equation.
• Calculation of the correlation coefficient, R2 and the standard error.
• Graphical representation of the least squares regression line of y on x (e.g. figure B)
• Determine in ascending order which independent variable, Xi provides the highest correlation with the dependent variable, Y.
• Forecasting of the dependent variable in the future (i.e predict y given x)
• Graphical representation of the forecasted values (e.g. in figure C)




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