Degrees of freedom is the number of freedom that are available to vary in a set of data. Supposing there are five people who are entering a tightly packed musical show and there are 5 seats available, though it is dispersed in various corners. The first person to enter has all 5 choices and he [...]
Archive for the ‘Basic Statistics’ Category
Degrees of Freedom (df) in Statistics
Posted in Basic Statistics on March 10, 2008 | Leave a Comment »
One Way Repeated Measures ANOVA
Posted in Basic Statistics on March 10, 2008 | Leave a Comment »
In this example of one way repeated measure, 5 participants are exposed to 4 conditions of training (walking, running, circuit training and weight training). Their response (enjoyment scale 0-dislike to 10-thouroughly enjoyable) is the dependent variable which we measure after the training. We wanted to know whether the elderly subjects had a similar liking to [...]
Two Way Independent ANOVA
Posted in Basic Statistics on March 5, 2008 | Leave a Comment »
We want to test the enjoyment scale (0-being dislike and 10-being highly enjoyable) of walking and weight lifting in a group of 10 young, middle aged and elderly persons. In each group, half (5) of them were assigned to perform walking and the other half (5) to perform weight training. So our independent variables are [...]
post hoc tests
Posted in Basic Statistics on March 5, 2008 | Leave a Comment »
It would be easy to understand if you have read the previous blog on ANOVA. If you have not, then post hoc is a procedure that can be done only if there are three or more groups in an independent variable. Its doing t-test on each pair of independent variables. In the previous blog, we [...]
ANOVA Basics
Posted in Basic Statistics on March 5, 2008 | Leave a Comment »
One way ANOVA: One independent variable Now, what if you have many means of data sets and you need to find the difference? If you try to keep doing t-test for every two means, you will be in a danger of inflating the type I error. That’s why you have Analysis of Variance (ANOVA). Again [...]
T-Test Basics
Posted in Basic Statistics on March 5, 2008 | Leave a Comment »
Measures true variation / error variation Dependent T-Test: True variation = Difference between the means of pre and post test; Error variation = standard error of the difference. T-test = true variation/error variation = (Mean 1 – Mean 2)/standard error Example : Supposing, there are 5 people with headache and their scores (pain score for [...]
Common Statistical Tests
Posted in Basic Statistics on March 5, 2008 | Leave a Comment »
We commonly use t-test, ANOVA or MANOVA to test our experimental hypotheses. Since null hypothesis says that all our means are equal, we try to use the above tests to find a difference between the mean if we have intervened the sets of data with our experiment. The statistical test not only measures the difference [...]
Regression Basics
Posted in Basic Statistics on March 5, 2008 | Leave a Comment »
You need to be clear with the basics before you try to understand regression. My best bet would be to read the previous blogs (especially linear relationship & the regression based on linear data) before starting this. We could say that an alternate word for regression would be prediction. It is widely used in in [...]
Basic Statistics
Posted in Basic Statistics on March 5, 2008 | Leave a Comment »
Null Hypothesis – When there is not a relationship between two variables. For example, if you are not pregnant, you will not have morning sickness. Or to put it in other words, there is no difference between two sets of data. Both the data have similar mean. True Hypothesis – When there is a relationship [...]
Pearson’s correlation
Posted in Basic Statistics on March 5, 2008 | Leave a Comment »
Before going into that, first of all what is a correlation? It is a linear relationship between two variables. If one variable changes positively, the other one also changes positively resulting in a positive correlation. Take a group of people. The taller the people are, the heftier they are going to be. Here, height and [...]
