You estimated the following regression. What value would you predict for Y, if X = 57? (Round your final answer to zero decimal places.) Source | SS df MS Number of obs = 447 -------------+---------------------------------- F(1, 445) > 99999.00 Model | 3.2330e+09 1 3.2330e+09 Prob > F = 0.0000 Residual | 12914153.8 445 29020.5704 R-squared = 0.9960 -------------+---------------------------------- Adj R-squared = 0.9960 Total | 3.2459e+09 446 7277811.63 Root MSE = 170.35 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 42.66842 .1278372 333.77 0.000 42.41718 42.91966 _cons | 99.79518 13.01235 7.67 0.000 74.2219 125.3685
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 = 57? (Round your final answer to zero decimal places.)
Source | SS df MS Number of obs = 447
-------------+---------------------------------- F(1, 445) > 99999.00
Model | 3.2330e+09 1 3.2330e+09 Prob > F = 0.0000
Residual | 12914153.8 445 29020.5704 R-squared = 0.9960
-------------+---------------------------------- Adj R-squared = 0.9960
Total | 3.2459e+09 446 7277811.63 Root MSE = 170.35
------------------------------------------------------------------------------
Y | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
X | 42.66842 .1278372 333.77 0.000 42.41718 42.91966
_cons | 99.79518 13.01235 7.67 0.000 74.2219 125.3685
------------------------------------------------------------------------------
Step by step
Solved in 2 steps with 1 images
