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Correlation And Regression Analysis

Describe the similarities and differences between correlation and regression.
Correlation is a way of measuring the extent to which two variables are related. Correlation lets us know if there is a relationship or no relationship present between the two different variables. Regression on the other hand is a way of predicting the value of one variable from another. Regression is a hypothetical model of the relationship between the two variables. The similarities between them are, they both are statistical analyses used to identify the relationship between the predictor and the outcome variable.
What is a line of best fit, what does it tell us, and how is it developed? The line of best fit is a linear line that minimizes error within a data set. It helps to
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Please identify (1) what they are, (2) where to find them on SPSS, and (3) how you know if you have met each of the assumptions.
Correlation: Linearity- Assumption that there is a linear relationship between you predictor variable and the outcome variable that you are testing. You are able to check for this in SPSS by generating scatterplots and inserting the line of best fit. If you have a consistent alignment of points in a linear fashion going along with the line of best fit you meet the assumption. If your points are all over and not in a linear fashion, linearity is not met.

Normality-This is the assumption that all variables are in normal distribution. We can find this assumption on SPSS by creating a histogram with your data. If you have a bell shaped, symmetric, and asymptotic shaped histogram then you can assume that normality is met. You can also look at the skewness and kurtosis through calculating you descriptive in SPSS.

Regression: Interval/Ratio levels of measurement for all variables-Variables must be interval or ratio. If you have continuous and data with a true zero you have met the basic
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