Energy consumption: The following table presents the average annual energy expenditures (in dollars) for housing units of various sizes (in square feet). Size Energy Expenditure 250 531 350 745 450 948 550 1102 650 1301 750 1477 850 1678 950 1868 (a) Compute the least-squares regression line for predicting energy consumption from house size. Round your answers to four decimal places. Regression line equation: y =
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.
Energy consumption: The following table presents the average annual energy expenditures (in dollars) for housing units of various sizes (in square feet).
| Size | Energy Expenditure |
|
250
|
531
|
|
350
|
745
|
|
450
|
948
|
|
550
|
1102
|
|
650
|
1301
|
|
750
|
1477
|
|
850
|
1678
|
|
950
|
1868
|
(a) Compute the least-squares regression line for predicting energy consumption from house size. Round your answers to four decimal places.
|
Regression line equation:
y
=
|
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