Selling Price of single-family homes in Seattle are based on land values and structural improvements. You collected data on 28 recent single-family home sales in Seattle. You recorded selling price (in $000) and size (in square ft) of each home. You then completed a simple regression analysis to explain variation in selling price as a function of home size. (Attached picture) a) Write the estimated regression equation. b) What percent of the total variation in selling price does the regression equation, or size, explain or determine? c) Interpret the value for standard error under Regression Statistics in this context (be specific). d) Interpret the value for the estimated slope coefficient for Size in this situation or context. e) Interpret the value for the estimated intercept coefficient in this situation. Should we give any importance to the intercept interpretation?
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.
Selling Price of single-family homes in Seattle are based on land values and structural improvements. You collected data on 28 recent single-family home sales in Seattle. You recorded selling price (in $000) and size (in square ft) of each home. You then completed a simple
a) Write the estimated regression equation.
b) What percent of the total variation in selling price does the regression equation, or size, explain or determine?
c) Interpret the value for standard error under Regression Statistics in this context (be specific).
d) Interpret the value for the estimated slope coefficient for Size in this situation or context.
e) Interpret the value for the estimated intercept coefficient in this situation. Should we give any importance to the intercept interpretation?
f) State the hyptheses associated with testing the statistical significance of the relationship between Size and Selling price (null and alternative).
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