# Correlation

Related:  Correlation

Correlation When two sets of data are strongly linked together we say they have a High Correlation. The word Correlation is made of Co- (meaning "together"), and Relation Correlation is Positive when the values increase together, and Correlation is Negative when one value decreases as the other increases Like this: Correlation can have a value: 1 is a perfect positive correlation 0 is no correlation (the values don't seem linked at all) -1 is a perfect negative correlation The value shows how good the correlation is (not how steep the line is), and if it is positive or negative. Example: Ice Cream Sales The local ice cream shop keeps track of how much ice cream they sell versus the temperature on that day, here are their figures for the last 12 days: And here is the same data as a Scatter Plot: We can easily see that warmer weather leads to more sales, the relationship is good but not perfect. In fact the correlation is 0.9575 ... see at the end how I calculated it. Correlation Is Not Good at Curves Where:

The Hitchhiker's Guide to the Galaxy Good examples of: Correlation doesn't prove Causation Quote: Even if this is true (and i doubt it is) it remains a perfect example of a causative effect. Causative effects don't have to be direct to be causative. In both science and law, what you just described is known as a proximate cause. To give you a more obvious example: Being shot in the heart is a proximate cause of death. But in science, law and common speech, we refer to being shot in the heart as being a cause of death. A relationship doesn't have to be direct to be considered causative. A relationship only becomes purely correlative when the two factors are not part of a causal chain.

THE DREAD TOMATO ADDICTION Ninety-two point four per cent of juvenile delinquents have eaten to- matoes. Eighty-seven point one per cent of the adult criminals in penitentiaries throughout the United States have eaten tomatoes. Informers reliably inform that of all known American Communists ninety-two point three per cent have eaten tomatoes. * It is suggested that best results will be obtained by using an experimental subject who is thoroughly familiar with and frequently uses the logical methods demonstrated herein, such as: (a) The average politician. This was originally published in the February 1958 edition of Astounding. PreviousHomeNext

Correlation (I) Correlation Association Between Variables Prerequisites Before reading this tutorial, you should already be familiar with the concepts of an arithmetic mean, a z-score, and a regression line. If you are unfamiliar with arithmetic means, see the tutorial on Mean, Median, and Mode. Introduction Two variables are said to be "correlated" or "associated" if knowing scores for one of them helps to predict scores for the other. In this tutorial you will examine the following concepts: Correlation Units of Analysis in Frequency Distributions Correlation and Standardized Distribution Scores Correlation vs. Correlation Here is a scatterplot of heights and weights for a sample population: Looking at this graph, you should get the sense that there is some relationship between a person's height and their weight. What should a measure of correlation r depend on? In this equation, n is the sample size, is the observed sample mean for variable x, Regression and correlation are intertwined. Activity 1 Activity 2

Correlation Introductory Statistics: Concepts, Models, and Applications David W. Stockburger The Pearson Product-Moment Correlation Coefficient (r), or correlation coefficient for short is a measure of the degree of linear relationship between two variables, usually labeled X and Y. While in regression the emphasis is on predicting one variable from the other, in correlation the emphasis is on the degree to which a linear model may describe the relationship between two variables. In regression the interest is directional, one variable is predicted and the other is the predictor; in correlation the interest is non-directional, the relationship is the critical aspect. The computation of the correlation coefficient is most easily accomplished with the aid of a statistical calculator. The correlation coefficient may take on any value between plus and minus one. The sign of the correlation coefficient (+ , -) defines the direction of the relationship, either positive or negative. Scatterplots r = 1.00

Spearman's Rank-Order Correlation - A guide to when to use it, what it does and what the assumptions are. This guide will tell you when you should use Spearman's rank-order correlation to analyse your data, what assumptions you have to satisfy, how to calculate it, and how to report it. If you want to know how to run a Spearman correlation in SPSS Statistics, go to our guide here. When should you use the Spearman's rank-order correlation? The Spearman's rank-order correlation is the nonparametric version of the Pearson product-moment correlation. What are the assumptions of the test? You need two variables that are either ordinal, interval or ratio (see our Types of Variable guide if you need clarification). What is a monotonic relationship? A monotonic relationship is a relationship that does one of the following: (1) as the value of one variable increases, so does the value of the other variable; or (2) as the value of one variable increases, the other variable value decreases. Why is a monotonic relationship important to Spearman's correlation? How to rank data? where i = paired score.