600 75 Genre 400 Action 50 Adventure Comedy Drama 200 25 Horror 0 80 -20 0 20 60 100 2.5 5.0 7.5 10.0 Residuals Fitted values 75 60 50 40 20 25 0 -- 0 200 400 1000 600 800 Action Adventure Comedy Genre Drama Horror Order of collection 80 60 40 20 0 2010 2014 2016 2012 2018 Release year Residuals Residuals 40 Residuals Residuals 80
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.
A FiveThirtyEight.com article reports that “Horror movies get nowhere near as much draw at the box office as the big-time summer blockbusters or action/adventure movies ... but there’s a huge incentive for studios to continue pushing them out. The return-on-investment potential for horror movies is absurd.” To investigate how the return-on-investment compares between genres and how this relationship has changed over time, an introductory statistics student fit a model predicting the ratio of gross revenue of movies from genre and release year for 1,070 movies released between 2000 and 2018. Using
the plots given below, determine if this regression model is appropriate for these data
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