To compute a Pearson correlation coefficient, data must be measured on an ordinal scale. True or False
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.
To compute a Pearson
True or False
Nominal: Nominal scales are used for labelling variables without any quantitative value.
Ex: blood group of a person (A, B, AB, etc)
Ordinal: The ordinal scale contains things that you can place in order.
Ex: Review of particular movie, obtained grades in particular class.
Interval: An interval scale has ordered numbers with meaningful divisions.
Ex: Temperature is on the interval scale.
Ratio: The ration scale is exactly the same as the interval scale with one major difference.
Ex: height(cm), weight(kg).
Correlation coefficient = The correlation coefficient can be calculated as,
Were,
σx is a standard deviation of x and,
σy is a standard deviation of y
The range of the correlation coefficient is -1 to 1 i.e. -1≤r≤1
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