Suppose that a simple linear regression model is appropriate for describing the relationship between y = house price and x = house size (sq ft) for houses in a large city. The true regression line is y = 23,000 + 48x and ? = 4000. (a) What is the average change in price associated with one extra sq ft of space? $ With an additional 100 sq ft of space? $ (b) What proportion of 1800 sq ft homes would be priced over $110,000? (Round your answer to four decimal places.) Under $100,000? (Round your answer to four 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.
Suppose that a simple linear regression model is appropriate for describing the relationship between y = house price and x = house size (sq ft) for houses in a large city. The true regression line is y = 23,000 + 48x and ? = 4000.
$
With an additional 100 sq ft of space?
$
(b) What proportion of 1800 sq ft homes would be priced over $110,000? (Round your answer to four decimal places.)
Under $100,000? (Round your answer to four decimal places.)
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