following statement about the relationship between the variables: There is a strong relationship. There is a weak relationship. 3. As one increases, the other increases.
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.
Two variables are studied and the
- There is a strong relationship.
- There is a weak relationship.
3. As one increases, the other increases.
4. As one increases, the other decreases.
5. A causal connection between variables has been proven.
Which of the following are true?
Correlation coefficient indicates how strong a relationship is between the data. It lies between 0 and 1. 1 implies a strong positive relationship. -1 implies a strong negative relationship. 0 implies there exist no relationship between the values.
Step by step
Solved in 2 steps
