The data below are the average one-way commute times (in minutes) for selected students and the number of absences for those students during the term. Find the equation of the regression line for the given data. What would be the predicted number of absences if the commute time was 95 minutes? Is this a reasonable question? Round the predicted number of absences to the nearest whole number. Round the regression line values to the nearest hundredth. Commute time (min), x 72 85 91 90 8S 98 75 100 so Number of absences, y 7 10 10 15 15 O y = 0.45x - 30.27; 12 absences; No, it is not reasonable. 95 minutes is well outside the scope of the model. Oy = 0.45x + 30.27; 73 absences; No, it is not reasonable. 95 minutes is well outside the scope of the model. O y = 0.45x - 30.27; 12 absences; Yes, it is reasonable. = 0.45x + 30.27; 73 absences; Yes, it is reasonable.
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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