You estimated the following regression. What value would you predict for Y, if X = 87? (Round your final answer to zero decimal places.) Source | SS df MS Number of obs = 233 -------------+---------------------------------- F(1, 231) = 3829.81 Model | 76032335.6 1 76032335.6 Prob > F = 0.0000 Residual | 4585993.64 231 19852.7863 R-squared = 0.9431 -------------+---------------------------------- Adj R-squared = 0.9429 Total | 80618329.2 232 347492.798 Root MSE = 140.9 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 11.39529 .1841353 61.89 0.000 11.0325 11.75809 _cons | 11.08928 16.23965 0.68 0.495 -20.90749 43.08605 ------------------------------------------------------------------------------
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 = 87? (Round your final answer to zero decimal places.)
Source | SS df MS Number of obs = 233
-------------+---------------------------------- F(1, 231) = 3829.81
Model | 76032335.6 1 76032335.6 Prob > F = 0.0000
Residual | 4585993.64 231 19852.7863 R-squared = 0.9431
-------------+---------------------------------- Adj R-squared = 0.9429
Total | 80618329.2 232 347492.798 Root MSE = 140.9
------------------------------------------------------------------------------
Y | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
X | 11.39529 .1841353 61.89 0.000 11.0325 11.75809
_cons | 11.08928 16.23965 0.68 0.495 -20.90749 43.08605
------------------------------------------------------------------------------
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