a. Ignore for now the months since the last maintenance service (¤1 ) and the repairperson who performed the ser estimated simple linear regression equation to predict the repair time (y) given the type of repair (2 ). Recall that 22 = 0 if the type of repair is mechanical and 1 if the type of repair is electrical (to 2 decimals). Time = Туре b. Does the equation that you developed in part (a) provide a good fit for the observed data? Explain. (to 4 decimals) No because the p-value of shows that the relationship is not significant v for any reasonable value of a. c. Ignore for now the months since the last maintenance service and the type of repair associated with the machine. Develop the estimated simple linear regression equation to predict the repair time given the repairperson who performed the service. Let 23 =0 if Bob Jones performed the service and #3 = 1 if Dave Newton performed the service (to 2 decimals). Enter negative value as negative number. Time = Person d. Does the equation that you developed in part (c) provide a good fit for the observed data? Explain.
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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