Data was gathered from several homes for sale in Cincinnati Ohio, in order to examine the relationship between the size of the house (measured in square feet) and the price of the house (measured in dollars). Suppose you learn the relationship between these variables is linear and positive. When a regression equation is constructed in order to predict the price of a house based on its size, the equation is found to explain 54.7% of the variability in price. This means that within this data set, the correlation, or r, between the house size and house price must be equal to approximately 55. 74. 30.
Data was gathered from several homes for sale in Cincinnati Ohio, in order to examine the relationship between the size of the house (measured in square feet) and the price of the house (measured in dollars). Suppose you learn the relationship between these variables is linear and positive. When a regression equation is constructed in order to predict the price of a house based on its size, the equation is found to explain 54.7% of the variability in price. This means that within this data set, the correlation, or r, between the house size and house price must be equal to approximately 55. 74. 30.
Algebra and Trigonometry (MindTap Course List)
4th Edition
ISBN:9781305071742
Author:James Stewart, Lothar Redlin, Saleem Watson
Publisher:James Stewart, Lothar Redlin, Saleem Watson
Chapter1: Equations And Graphs
Section1.FOM: Focus On Modeling: Fitting Lines To Data
Problem 12P: Demand for Candy Bars In this problem you will determine a linear demand equation that describes the...
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- Data was gathered from several homes for sale in Cincinnati Ohio, in order to examine the relationship between the size of the house (measured in square feet) and the price of the house (measured in dollars). Suppose you learn the relationship between these variables is linear and positive. When a regression equation is constructed in order to predict the price of a house based on its size, the equation is found to explain 54.7% of the variability in price. This means that within this data set, the
correlation , or r, between the house size and house price must be equal to approximately
- 55.
- 74.
- 30.
- 27.
- 81.
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