Below is a scatterplot showing preliminary exam and final exam scores for students in a secondary school along with the linear regression line: Prelim Exam Score vs. Final Exam Score 90 80 y= 0.98x + 1.36 70 50 40 30 40 45 50 55 60 65 70 75 80 Prelim Exam Score The average scores for the preliminary exam and final exam were both 60, with standard deviations of 5.1 and 6.6 respectively. The slope of 0.98 in the linear regression line predicts: None of the other options. The average final exam score of students who scored 0 on the preliminary exam. The increase in average final exam scores, corresponding to an increase of 1 mark in the preliminary examination. The correlation between the final and preliminary exam scores. Final Exam Score
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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