You estimated a regression with the following output. Source | SS df MS Number of obs = 327 -------------+---------------------------------- F(1, 325) = 88196.79 Model | 1.7062e+09 1 1.7062e+09 Prob > F = 0.0000 Residual | 6287233.75 325 19345.3346 R-squared = 0.9963 -------------+---------------------------------- Adj R-squared = 0.9963 Total | 1.7125e+09 326 5253017.44 Root MSE = 139.09 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 24.69304 .0831473 296.98 0.000 24.52947 24.85662 _cons | 79.95371 9.884466 8.09 0.000 60.5081 99.39933 ------------------------------------------------------------------------------ Which of the following is the estimated regression line? Group of answer choices a Y = 24.69 + 79.95*X b Y = 9.88 + 0.08*X c Y = 0.08 + 9.88*X d Y = 79.95 + 24.69*X
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 a regression with the following output.
Source | SS df MS Number of obs = 327
-------------+---------------------------------- F(1, 325) = 88196.79
Model | 1.7062e+09 1 1.7062e+09 Prob > F = 0.0000
Residual | 6287233.75 325 19345.3346 R-squared = 0.9963
-------------+---------------------------------- Adj R-squared = 0.9963
Total | 1.7125e+09 326 5253017.44 Root MSE = 139.09
------------------------------------------------------------------------------
Y | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
X | 24.69304 .0831473 296.98 0.000 24.52947 24.85662
_cons | 79.95371 9.884466 8.09 0.000 60.5081 99.39933
------------------------------------------------------------------------------
Which of the following is the estimated regression line?
a Y = 24.69 + 79.95*X
b Y = 9.88 + 0.08*X
c Y = 0.08 + 9.88*X
d Y = 79.95 + 24.69*X
Step by step
Solved in 2 steps with 1 images
