Sales of jelly (X) and sales of peanut butter (Y) at a local store are recorded weekly. The correlation between X and Y is 0.9. Which is the best explanation of what this correlation means? (a) 90% of customers either buy peanut butter or jelly. (b) 90% of the variability in peanut butter sales can be explained by variability in jelly sales. (c) 81% of the variability in peanut butter sales can be explained by variability in jelly sales. (d) 90% of the time when the price of peanut butter goes up, so does the price of jelly.
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.
Sales of jelly (X) and sales of peanut butter (Y) at a local store are recorded weekly. The
(a) 90% of customers either buy peanut butter or jelly.
(b) 90% of the variability in peanut butter sales can be explained by variability in jelly sales.
(c) 81% of the variability in peanut butter sales can be explained by variability in jelly sales.
(d) 90% of the time when the price of peanut butter goes up, so does the price of jelly.
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