Data were collected from a random sample of 200 home sales from a community in 2013. Let Price denote the selling price (in $1000), BDR denotethe number of bedrooms, Bath denote the number of bathrooms, Hsizedenote the size of the house (in square feet), Lsize denote the lot size (insquare feet), Age denote the age of the house (in years), and Poor denotea binary variable that is equal to 1 if the condition of the house is reportedas “poor.” An estimated regression yields "Price "= 109.7 + 0.567BDR + 26.9Bath + 0.239Hsize + 0.005Lsize + 0.1Age - 56.9Poor, R2 = 0.85, SER = 45.8.a. Suppose a homeowner converts part of an existing family room intheir house into a new bathroom. What is the expected increase in thevalue of the house?b. Suppose that a homeowner adds a new bathroom to their house,which increases the size of the house by 80 square feet. What is theexpected increase in the value of the house?c. What is the loss in value if a homeowner lets their house run down,such that its condition becomes “poor?”d. Compute the R2 for the regression.
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.
Data were collected from a random sample of 200 home sales from a community in 2013. Let Price denote the selling price (in $1000), BDR denotethe number of bedrooms, Bath denote the number of bathrooms, Hsizedenote the size of the house (in square feet), Lsize denote the lot size (insquare feet), Age denote the age of the house (in years), and Poor denotea binary variable that is equal to 1 if the condition of the house is reportedas “poor.” An estimated regression yields "Price "= 109.7 + 0.567BDR + 26.9Bath + 0.239Hsize + 0.005Lsize + 0.1Age - 56.9Poor, R2 = 0.85, SER = 45.8.a. Suppose a homeowner converts part of an existing family room intheir house into a new bathroom. What is the expected increase in thevalue of the house?b. Suppose that a homeowner adds a new bathroom to their house,which increases the size of the house by 80 square feet. What is theexpected increase in the value of the house?c. What is the loss in value if a homeowner lets their house run down,such that its condition becomes “poor?”d. Compute the R2 for the regression.
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