00:00 / 00:00
Introduction to biostatistics
Types of data
Fisher's exact test
Kaplan-Meier survival analysis
Mann-Whitney U test
Spearman's rank correlation coefficient
Type I and type II errors
Hypothesis testing: One-tailed and two-tailed tests
Methods of regression analysis
Repeated measures ANOVA
Mean, median, and mode
Normal distribution and z-scores
Range, variance, and standard deviation
Standard error of the mean (Central limit theorem)
0 / 10 complete
0 / 2 complete
Correlation is a statistical technique that shows whether two quantitative variables are related, and also how strongly they’re related.
For example, let’s say you want to figure out if drinking more soda is correlated with having a higher body mass index or BMI.
So, you ask 100 people how many sugary beverages they drink in a week and then check each person’s height and weight to calculate their BMI.
You could plot these measurements or data points on a scatterplot, with the number of beverages on the x-axis and BMI on the y-axis, and where each data point represents one individual.
Typically, a trendline is drawn to best represent the pattern of data points on the plot, with roughly half the points above the line and half the points below the line.
Now, a positive correlation means that BMI increases as the number of beverages increases, and, if the two variables have a perfect positive correlation, then the trendline will pass through every single data point.
Now imagine that there’s a negative correlation. That means that the BMI decreases as the number of beverages increases, and, with a perfect negative correlation, the trendline also passes through every data point.
Finally, if there’s no correlation, then the data points will be randomly spread out all over the scatterplot, and the trendline will be flat with no positive or negative direction.
To figure out how strongly two variables are correlated, we can use the Pearson’s correlation test, which is a parametric test that measures how close or spread out the data points are from the trendline.
The Pearson’s correlation test calculates a correlation coefficient, which is a number that represents how well the two variables are correlated, and usually it’s written with a lowercase r.
The correlation coefficient can range from negative 1 - which represents perfect negative correlation - to positive 1 - which represents perfect positive correlation.
A correlation coefficient of 0 means that there’s no correlation between the two variables.
Now, there are four key assumptions that are used in a Pearson’s correlation test, and an acronym for these assumptions is L-I-N-E, or LINE.
A correlation is a statistical measure that quantifies the degree of relationship between two variables. In other words, it shows how closely two things are related. A correlation coefficient can range from -1 to 1, with a value of 1 indicating a perfect positive correlation and a value of -1 indicating a perfect negative correlation.
Latest on COVID-19
Nurse Practitioner (NP)
Physician Assistant (PA)
Create custom content
Raise the Line Podcast
Copyright © 2024 Elsevier, its licensors, and contributors. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
Cookies are used by this site.
Terms and Conditions
USMLE® is a joint program of the Federation of State Medical Boards (FSMB) and the National Board of Medical Examiners (NBME). COMLEX-USA® is a registered trademark of The National Board of Osteopathic Medical Examiners, Inc. NCLEX-RN® is a registered trademark of the National Council of State Boards of Nursing, Inc. Test names and other trademarks are the property of the respective trademark holders. None of the trademark holders are endorsed by nor affiliated with Osmosis or this website.