Posts Tagged ‘definition’

Correlation

Published on Apr 24th, 2010 by

Correlation

Indicates the extent to which two things are related. For example when it is cold ice-cream sales are low, then as the temperature increases so do ice-cream sales. A correlation is reported as an r value and can be anywhere between 1 and -1. A correlation of 1 means the two things increase at the same rate (temperature increases as ice-cream sales increase). -1 means that as one increases, the other decreases, (for example the more exercise someone does, the lower their risk of heart problems become). 0 means the two things are unrelated. A correlation (r) below .04 is typically considered low, from 0.4 to 0.6 is considered good and above 0.6 very good.

Myth: Causality- You can’t say based on correlation that a change in one thing will cause a change in another. For example people that do more exercise may also have a better diet and not smoke, so these other things help to lower the risk of heart problems not just the exercise. For this you need Multiple Regression.

Strong positive correlation (r=1)               No correlation (r=0)               Strong negative correlation (r=-1)

Multiple Regression

Published on Apr 14th, 2010 by

Multiple Regression

Measures the extent to which the level of one thing is dependent on several others. For example, a shop may measure customer satisfaction along with quality of products, range of products and helpfulness of staff. It gives a percentage figure (called the r2 value), the closer this is too 100% the better these items are at predicting customer satisfaction. Each individual  attribute is given a beta value which indicates how much influence each has. With this statistic the items that affect satisfaction can be identified and budget can be best allocated between them to achieve the highest increase in satisfaction.

Myth: 100%- A statistic close to 100% is virtually never achieved; anything approaching 50% is a good result