You estimated the following regression. What value would you predict for Y, if X = 55? (Round your final answer to zero decimal places.) Source | SS df MS Number of obs = 330 -------------+---------------------------------- F(1, 328) = 8077.71 Model | 274424755 1 274424755 Prob > F = 0.0000 Residual | 11143169.1 328 33973.0765 R-squared = 0.9610 -------------+---------------------------------- Adj R-squared = 0.9609 Total | 285567924 329 867987.612 Root MSE = 184.32 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 38.54589 .4288781 89.88 0.000 37.70219 39.38959 _cons | 22.14854 31.07752 0.71 0.477 -38.98786 83.28494
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 the following regression. What value would you predict for Y, if X = 55? (Round your final answer to zero decimal places.)
Source | SS df MS Number of obs = 330
-------------+---------------------------------- F(1, 328) = 8077.71
Model | 274424755 1 274424755 Prob > F = 0.0000
Residual | 11143169.1 328 33973.0765 R-squared = 0.9610
-------------+---------------------------------- Adj R-squared = 0.9609
Total | 285567924 329 867987.612 Root MSE = 184.32
------------------------------------------------------------------------------
Y | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
X | 38.54589 .4288781 89.88 0.000 37.70219 39.38959
_cons | 22.14854 31.07752 0.71 0.477 -38.98786 83.28494
------------------------------------------------------------------------------
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