Is it a good idea to make inference using regression in this situation? Chose the answer that is most appropriate: There is a linear trend so using regression is appropriate. Linearity assumption is satisfied. There is a linear trend but regression is not appropriate. Linearity assumption fails. The relationship seems to be curvilinear. Regression is still appropriate because the line seems reasonable. Linearity assumption is satisfied. The relationship seems to be more curvilinear rather than linear. Regression is not appropriate in this situation because the linearity condition fails and the independent random errors condition also fails.
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.
Is it a good idea to make inference using regression in this situation? Chose the answer that is most appropriate:
There is a linear trend so using regression is appropriate. Linearity assumption is satisfied.
There is a linear trend but regression is not appropriate. Linearity assumption fails.
The relationship seems to be curvilinear. Regression is still appropriate because the line seems reasonable. Linearity assumption is satisfied.
The relationship seems to be more curvilinear rather than linear.
Regression is not appropriate in this situation because the linearity condition fails and the independent random errors condition also fails.
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