You estimated the following regression. What value would you predict for Y, if X = 89? (Round your final answer to zero decimal places.) Source | SS df MS Number of obs = 215 -------------+---------------------------------- F(1, 213) = 82741.83 Model | 921013654 1 921013654 Prob > F = 0.0000 Residual | 2370939.82 213 11131.1728 R-squared = 0.9974 -------------+---------------------------------- Adj R-squared = 0.9974 Total | 923384594 214 4314881.28 Root MSE = 105.5
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 = 89? (Round your final answer to zero decimal places.)
Source | SS df MS Number of obs = 215
-------------+---------------------------------- F(1, 213) = 82741.83
Model | 921013654 1 921013654 Prob > F = 0.0000
Residual | 2370939.82 213 11131.1728 R-squared = 0.9974
-------------+---------------------------------- Adj R-squared = 0.9974
Total | 923384594 214 4314881.28 Root MSE = 105.5
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Y | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
X | 33.52472 .1165474 287.65 0.000 33.29498 33.75445
_cons | 36.39003 13.54692 2.69 0.008 9.686834 63.09323
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