Hospital Staffing. Where we considered the relationship between monthly man-hours needed to maintain an anesthesiology service and the predictor variables number of surgical cases, eligible population per 1000, and number of operating rooms. Use the technology of your choice to obtain output. a. Obtain a scatterplot matrix for the data, a three-dimensional scatterplot of the three predictor variables, and a regression analysis of man-hours on the three predictor variables (include the VIF s). Use these plots and output to assess the severity of multicollinearity in this regression analysis. b. What remedies would you suggest, if any, to lessen the effect of multicollinearity in this regression analysis?
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.
Hospital Staffing. Where we considered the relationship between monthly man-hours needed to maintain an anesthesiology service and the predictor variables number of surgical cases, eligible population per 1000, and number of operating rooms. Use the technology of your choice to obtain output.
a. Obtain a
b. What remedies would you suggest, if any, to lessen the effect of multicollinearity in this regression analysis?
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