First, make a table of all your data.
You could design an experiment in which you have a group of Democrats, a group of Republicans, and a group of Independents and give them a survey that asks them about their views on same-sex marriage.
This means that religious affiliation does influence opinions on the chamberlain coupon free shipping ideal number of children in a family.
Let's say that after asking all the people in all three groups what they consider the ideal number of children in a family to be, you record each person's answer and then calculate the mean, or average, number reported by each collective group.Null Hypothesis #2: There are no differences in the population mean due to the second factor.One group of participants could receive chemotherapy, another group could receive radiation treatment, still another could receive no treatment, and finally the last group would receive the new drug in question.Add them all up to get the sum of squares.You would likely do this by recruiting individuals from different religious groups and asking them to report what they consider the ideal number of children in a family should.An example of an interaction effect would be if the effectiveness of a diet plan was influenced by the type of exercise a patient performed.Unlock Content, over 75,000 lessons in all major subjects.1 - 3 Belmore Terrace, Sunshine Beach - Noosa.The previous example involving religion and number of children fits into this category.
This gives you three null hypotheses to test: Null Hypothesis #1: There are no differences in the population mean (of the measurement variable) due to the first factor.Analysis of Variance, or anova, is another statistical test that you can use to determine if there are differences between three or more groups.The fact that we have differing levels of the independent variable of religion is what allows us to carry out an anova.Other examples include the effect of political party on views of same-sex marriage.It can be used in any experiment that involves two (but usually three or more) levels of the independent variable.A two-way anova is actually testing three hypotheses.Additionally, this experiment includes three different levels of the independent variable.Not only this, but they can be used in medical research, and implemented in experiments in the natural sciences.These are not the only situations in which an anova can be useful.Now, there would be TWO factors (diet plan and exercise plan) that might affect the measured variable (weight loss).
Null Hypothesis #3: There are no interaction effects between the first and second factors.