You estimated a regression with the following output. Source | SS df MS Number of obs = 475 -------------+---------------------------------- F(1, 473) = 13535.81 Model | 62600571.3 1 62600571.3 Prob > F = 0.0000 Residual | 2187536.65 473 4624.81322 R-squared = 0.9662 -------------+---------------------------------- Adj R-squared = 0.9662 Total | 64788108 474 136683.772 Root MSE = 68.006 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 12.09408 .1039515 116.34 0.000 11.88982 12.29834 _cons | 41.99978 7.034782 5.97 0.000 28.17648 55.82307 ------------------------------------------------------------------------------ Which of the following is the estimated regression line? Group of answer choices a Y = 12.09 + 42.00*X b Y = 42.00 + 12.09*X c Y = 7.03 + 0.10*X d Y = 0.10 + 7.03*X
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.
You estimated a regression with the following output.
Source | SS df MS Number of obs = 475
-------------+---------------------------------- F(1, 473) = 13535.81
Model | 62600571.3 1 62600571.3 Prob > F = 0.0000
Residual | 2187536.65 473 4624.81322 R-squared = 0.9662
-------------+---------------------------------- Adj R-squared = 0.9662
Total | 64788108 474 136683.772 Root MSE = 68.006
------------------------------------------------------------------------------
Y | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
X | 12.09408 .1039515 116.34 0.000 11.88982 12.29834
_cons | 41.99978 7.034782 5.97 0.000 28.17648 55.82307
------------------------------------------------------------------------------
Which of the following is the estimated regression line?
a Y = 12.09 + 42.00*X
b Y = 42.00 + 12.09*X
c Y = 7.03 + 0.10*X
d Y = 0.10 + 7.03*X
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