You estimated a regression with the following output. Source | SS df MS Number of obs = 115 -------------+---------------------------------- F(1, 113) = 5454.39 Model | 186947380 1 186947380 Prob > F = 0.0000 Residual | 3873036.62 113 34274.6603 R-squared = 0.9797 -------------+---------------------------------- Adj R-squared = 0.9795 Total | 190820417 114 1673863.3 Root MSE = 185.13 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 28.58986 .3871141 73.85 0.000 27.82292 29.3568 _cons | 10.54686 26.92706 0.39 0.696 -42.80051 63.89423
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 = 115
-------------+---------------------------------- F(1, 113) = 5454.39
Model | 186947380 1 186947380 Prob > F = 0.0000
Residual | 3873036.62 113 34274.6603 R-squared = 0.9797
-------------+---------------------------------- Adj R-squared = 0.9795
Total | 190820417 114 1673863.3 Root MSE = 185.13
------------------------------------------------------------------------------
Y | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
X | 28.58986 .3871141 73.85 0.000 27.82292 29.3568
_cons | 10.54686 26.92706 0.39 0.696 -42.80051 63.89423
------------------------------------------------------------------------------
Which of the following is the estimated regression line?
a Y = 10.55 + 28.59*X
b Y = 26.93 + 0.39*X
c Y = 28.59 + 10.55*X
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