What are the types of these correlations? Look at the picture and explain results In all six panels. Think of examples from life that can give such correlation coefficients. NOTE: we are talking about Pearson correlation here. r= 0.7 r= 0.3 r=0 r=-0.7 r=-0.3. r= 0
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.
Correlation is used to find the relationship between two quantitative variables.
The value of correlation lies between -1 to 1.
A correlation is said to be strong positive if it is 0.7 or greater and a correlation is said to be strong negative if it is -0.7 or smaller.
If the correlation coefficient is 0, then there is no correlation between variables.
In the first picture the correlation coefficient (r) is 0.7, therefore, there is strong positive correlation between the variables.
In the second picture the correlation coefficient (r) is 0.3, therefore, there is weak positive correlation between the variables.
In the third picture the correlation coefficient (r) is 0, therefore, there is no correlation between the variables.
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