Suppose that we are working with some doctors on heart attack patients. The dependent variable is whether the patient has had a second heart attack within 1 year (yes=1). We have two independent variables, one is age of the patient and the other is a score on anxiety scale (a higher score means more anxious). After applying logistic regression model, we have the following output: Deviance Residuals: 10 Median Min 30 Маx |-1.064 0.000 0.000 0.000 1.446 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -471.441 223186.509 -0.002 0.998 Age 6.394 3057.349 0.002 0.998 Anxiety 1.347 611.470 0.002 0.998 | (Dispersion parameter for binomial family taken to be 1) Null deviance: 27.7259 on 19 degrees of freedom Residual deviance: 3.7087 on 17 degrees of freedom AIC: 9.7087 Number of Fisher Scoring iterations: 23 a. Determine the estimated logistic regression equation. b. Calculate the odds ratio and interpret.
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.
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