The tables show the amount of revenue (y) in billions for different companies years after 2005. Match the table with the correct description of its regression model and correlation. Negative Linear Regression 1. æ 0 1 2 3 4 5 6 7 8 9 10 Strong Negative Correlation y 12 12 13 14 15 16 18 19 21 22 25 Positive Linear Regression * 0 12 3 4 5 6 7 8 9 10 2. Strong Positive Correlation y 20 18 17 15 13 14 16 18 21 25 28 Quadratic Regression 3. æ 0 1234 5 6 7 8 9 10 Strong Positive Correlation y 5679 13 15 18 24 25 28 30 Increasing Exponential Regression 4. æ 0 1 2 3 4 5 6 789 10 Strong Positive Correlation y 20 20 19 18 16 15 12 8 765 Decreasing Exponential Regression 5. 0 1 2 3 4 5 6 7 8 9 10 Strong Negative Correlation y 20 19 20 18 16 15 13 11 10 11 10
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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