Regression and correlation are used to predict current and future behaviors of variables. Use an example to discuss how regression and correlation can be used to predict relationships between variables.
What will be an ideal response?
Ans: Regression allows researchers to derive an imaginary line called the regression line or the best fitting line and represents the closest possible linear relationship between two variables. Conceptually, regression shows one line that could have best explained the relationship between variables if this were a linear relationship. The best fitting line of the relationship between variables also tells us the direction of a relationship. Pearson correlation coefficient r can range in value from -1 to 1, indicating the level of linearity between two variables. The closer this coefficient, r, is to -1 or 1, the more likely it is that the relationship under investigation resembles a linear relationship. The closer this coefficient is to 0, the less likely it is that these two variables are correlated to each other. A correlation of 1 and ?1 are perfect positive and negative relationships, respectively, while a correlation coefficient of 0 indicates no relationship between variables (examples used will vary).
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