Interperting Regression Results (Continuation of Exercise 8-39) The following table showsadditional regression results presented by the researchers in the study described in Exercise 8-39.There are two regressions. The right-hand column shows the results for all patients, including thosetreated with laparoscopic surgery. The left-hand column shows the results for the sample of patientswho were treated without the laparoscopic surgery.Nonlaparoscopic Patients Only All PatientsCoefficients for Independent VariablesRegression intercept $8,043 $3,719 Length of stay Coefficient* Not significant 861 Standard error for the coefficient Not applicable 80 Number of complications Coefficient 3,393 1,986 Standard error for the coefficient 1,239 406 Laparascopic Coefficient Not applicable 908 Standard error for the coefficient Not applicable 358 R-squared 0.11 0.53 *All independent variables are significant at the level of p = .05 (and t-value >2) except for the length of stay variable in the nonlaparoscopic condition. Also, the t-value for each independent variable can be calculated by dividing the coefficient of the variable by the standard error for the coefficient of that variable.Required1. Which of the two regressions has the better reliability in estimating costs? Why?2. Calculate the t-value for each of the independent variables, and interpret the values of each coefficientand the t-values for each independent variable.
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.
Interperting Regression Results (Continuation of Exercise 8-39) The following table shows
additional regression results presented by the researchers in the study described in Exercise 8-39.
There are two regressions. The right-hand column shows the results for all patients, including those
treated with laparoscopic surgery. The left-hand column shows the results for the sample of patients
who were treated without the laparoscopic surgery.
Nonlaparoscopic Patients Only All Patients
Coefficients for Independent Variables
Regression intercept $8,043 $3,719
Length of stay
Coefficient* Not significant 861
Standard error for the coefficient Not applicable 80
Number of complications
Coefficient 3,393 1,986
Standard error for the coefficient 1,239 406
Laparascopic
Coefficient Not applicable 908
Standard error for the coefficient Not applicable 358
R-squared 0.11 0.53
*All independent variables are significant at the level of p = .05 (and t-value >2) except for the length of stay variable in the nonlaparoscopic condition. Also, the t-value for each independent variable can be calculated by dividing the coefficient of the variable by the standard error for the coefficient of that variable.
Required
1. Which of the two regressions has the better reliability in estimating costs? Why?
2. Calculate the t-value for each of the independent variables, and interpret the values of each coefficient
and the t-values for each independent variable.
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