ou estimated the following regression. What value would you predict for Y, if X = 88? (Round your final answer to zero decimal places.) Source | SS df MS Number of obs = 435 -------------+---------------------------------- F(1, 433) = 34496.64 Model | 1.0313e+09 1 1.0313e+09 Prob > F = 0.0000 Residual | 12944297 433 29894.4504 R-squared = 0.9876 -------------+---------------------------------- Adj R-squared = 0.9876 Total | 1.0442e+09 434 2405996.6 Root MSE = 172.9 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 25.96473 .1397962 185.73 0.000 25.68997 26.2395 _cons | 25.21453 12.4239 2.03 0.043 .7958721 49.63318 ------------------------------------------------------------------------------
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 = 88? (Round your final answer to zero decimal places.)
Source | SS df MS Number of obs = 435
-------------+---------------------------------- F(1, 433) = 34496.64
Model | 1.0313e+09 1 1.0313e+09 Prob > F = 0.0000
Residual | 12944297 433 29894.4504 R-squared = 0.9876
-------------+---------------------------------- Adj R-squared = 0.9876
Total | 1.0442e+09 434 2405996.6 Root MSE = 172.9
------------------------------------------------------------------------------
Y | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
X | 25.96473 .1397962 185.73 0.000 25.68997 26.2395
_cons | 25.21453 12.4239 2.03 0.043 .7958721 49.63318
------------------------------------------------------------------------------
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