Skewness is a measure used in statistics that helps reveal the asymmetry of a probability distribution.It can either be positive or negative, irrespective of signs.To calculate the skewness, we have to first find the mean and variance of the given data.
This can be done using the Shapiro-Wilk test for normality, which you can carry out using Minitab. 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. A value of 0 (zero) indicates that there is no relationship between the two variables. If there was a strong, positive association, we could say that more time spent revising was associated with higher test performance. Alternately, you could use a Pearsons 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 mmHg, and time spent exercising, measured in hours per week). If there was a moderate, negative association, we could say that exercising more per week is associated with lower blood pressure. ![]() You cannot test the first of these assumptions with Minitab because it relates to your study design and choice of variables. However, you should check whether your study meets this assumption before moving on. If this assumption is not met, there is likely to be a different statistical test that you can use instead. 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 ordinal variables include Likert scales (e.g., a 7-point scale from strongly agree through to strongly disagree), amongst other ways of ranking categories (e.g., a 5-point scale for measuring job satisfaction, ranging from most satisfied to least satisfied; or a 3-point scale explaining how much a customer liked a product, ranging from Not very much to Yes, a lot). You have to check that your data meets these assumptions because if it does not, the results you get when running a Pearsons correlation might not be valid. In fact, do not be surprised if your data violates one or more of these assumptions. However, there are possible solutions to correct such violations (e.g., transforming your data) such that you can still use a Pearsons correlation. Whilst there are a number of ways to check whether a linear relationship exists, we suggest creating a scatterplot using Minitab, where you can plot your two variables against each other. You can then visually inspect the scatterplot to check for linearity. If the relationship displayed in your scatterplot is not linear, you will have to either transform your data or run a Spearmans correlation instead, which you can do using Minitab. An outlier is simply a case within your data set that does not follow the usual pattern. For example, consider a study examining the relationship between test anxiety of 500 students (where anxiety was measured on a scale of 0-100, with 0 no anxiety and 100 maximum anxiety) and exam performance (on a scale from 0 to 100, with 100 the top score). If most participants that had an anxiety score of around 70 had an exam score of around 45, a participant with an anxiety score of 70 who scored 90 in the exam (i.e., an unusually high score) might be an outlier. Pearsons r is sensitive to outliers, which can have a very large effect on the line of best fit and the Pearson correlation coefficient, leading to very difficult conclusions regarding your data. Therefore, it is best if there are no outliers or they are kept to a minimum. Fortunately, you can create scatterplots in Minitab to detect possible outliers. In order to assess the statistical significance of the Pearson correlation, you need to have bivariate normality, but this assumption is difficult to assess, so the simpler method of assessing the normality of each variable separately is more commonly used.
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