Instructions: Use the data given to create a scatter plot, calculate the line of best fit and interpret the slope and y-intercept in context. A random sample of ten professional athletes produced the following data where x is the number of endorsements the player has and y is the amount of money made (in millions of dollars). x y 0 2 3 8 2 7 1 3 5 13 5 12 4 9 3 9 0 3 4 10 The dependent variable is the number of endorsements + made. The independent variable is the number of millions + made. The regression equation is y = x+ (Round to hundredths.) The y-intercept tells us that if an athlete had endorsements, they would expect to make about million dollars (round to hundredths).
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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