#Multiple regression in minitab express how to#

If you are unsure whether your dependent variable is continuous (i.e., measured at the interval or ratio level), see our Types of Variable guide. Examples of such continuous variables include height (measured in feet and inches), temperature (measured in вЊ), salary (measured in US dollars), revision time (measured in hours), intelligence (measured using IQ score), firm size (measured in terms of the number of employees), age (measured in years), reaction time (measured in milliseconds), grip strength (measured in kg), power output (measured in watts), test performance (measured from 0 to 100), sales (measured in number of transactions per month), academic achievement (measured in terms of GMAT score), and so forth.

Assumption #1: Your two variables should be measured at the continuous level (i.e., they are interval or ratio variables).

If this assumption is not met, there is likely to be a different statistical test that you can use instead. However, you should check whether your study meets this assumption before moving on.

You cannot test the first of these assumptions with Minitab because it relates to your study design and choice of variables. Minitab AssumptionsДЄ Pearson's correlation has four assumptions. However, before we introduce you to this procedure, you need to understand the different assumptions that your data must meet in order for a Pearson's correlation to give you a valid result.

#Multiple regression in minitab express how to#

In this guide, we show you how to carry out a Pearson's correlation using Minitab, as well as interpret and report the results from this test. If there was a moderate, negative association, we could say that exercising more per week is associated with lower blood pressure. Alternately, you could use a Pearson's correlation to understand whether there is an association between blood pressure and time spent exercising (i.e., your two variables would be 'blood pressure', measured in mm/Hg, and 'time spent exercising', measured in hours per week). If there was a strong, positive association, we could say that more time spent revising was associated with higher test performance. A value of 0 (zero) indicates that there is no relationship between the two variables.ДЇor example, you could use a Pearson's correlation to understand whether there is an association between test performance and revision time (i.e., your two variables would be 'test performance', measured as the exam mark achieved, and 'revision time', measured in hours per week). Its coefficient, r, indicates the strength and direction of this relationship and can range from -1 for a perfect negative linear relationship to +1 for a perfect positive linear relationship. The Pearson product-moment correlation, often shortened to Pearson correlation or Pearson's correlation, is used to assess the strength and direction of association between two continuous variables that are linearly related. Pearson's correlation using Minitab Introduction