You estimated a regression with the following output. Source | SS df MS Number of obs = 494 -------------+---------------------------------- F(1, 492) = 38566.69 Model | 803403712 1 803403712 Prob > F = 0.0000 Residual | 10249120.6 492 20831.546 R-squared = 0.9874 -------------+---------------------------------- Adj R-squared = 0.9874 Total | 813652832 493 1650411.42 Root MSE = 144.33 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+----------------------------
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 = 494
-------------+---------------------------------- F(1, 492) = 38566.69
Model | 803403712 1 803403712 Prob > F = 0.0000
Residual | 10249120.6 492 20831.546 R-squared = 0.9874
-------------+---------------------------------- Adj R-squared = 0.9874
Total | 813652832 493 1650411.42 Root MSE = 144.33
------------------------------------------------------------------------------
Y | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
X | 23.00296 .1171325 196.38 0.000 22.77281 23.2331
_cons | 34.71944 13.12788 2.64 0.008 8.925808 60.51307
------------------------------------------------------------------------------
Which of the following is the estimated regression line?
a Y = 34.72 + 23.00*X
b Y = 13.13 + 0.12*X
c Y = 0.12 + 13.13*X
d Y = 23.00 + 34.72*X
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