Janice Carilllo a gainesville florida real estate developer has devised a regression model to help determine residential housing prices in northeastern florida. The model was developed using recent sales in a particular neighborhood. The price (y) of the house is based on the size (square footage =X) of the house. The model is : Y=12,973+37.65X The coefficient of correlation for the model is 0.73 a) Using the above model the selling price of a house that is 1760 square feet= $79,237 b) a 1760 square foot house recently sold for $92,000 which is dfiferent than predicted value. This is possible as the forecast represents average value. c) To make this model more realistic additional quantitative variables that could be included in multiple regression model are =The age of the house the number of bedrooms and the size of the lot d) For the given model the value of the coefficient of determination= [__] (round your response to three decimal places)
Correlation
Correlation defines a relationship between two independent variables. It tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.
Linear Correlation
A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related.
Regression Analysis
Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.
Janice Carilllo a gainesville florida real estate developer has devised a regression model to help determine residential housing prices in northeastern florida. The model was developed using recent sales in a particular neighborhood. The price (y) of the house is based on the size (square footage =X) of the house. The model is :
Y=12,973+37.65X
The coefficient of
a) Using the above model the selling price of a house that is 1760 square feet= $79,237
b) a 1760 square foot house recently sold for $92,000 which is dfiferent than predicted value. This is possible as the forecast represents average value.
c) To make this model more realistic additional quantitative variables that could be included in multiple regression model are =The age of the house the number of bedrooms and the size of the lot
d) For the given model the value of the coefficient of determination= [__] (round your response to three decimal places)
Given:
Where represent Price of the house.
and represent the square footage of the house.
The selling Price of the house that is square feet is
A square feet house recently sold for which is different than predicted value. This is as the forecast represent value.
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