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An interaction occurs when considering the relationship among three or more variables, and describes a situation in which the simultaneous influence of two variables on a third is not additive. The simplest situation is when the effect of each independent variable is completely separate from the other independent variables. The more complicated situation is when the effect of one independent variable depends on another independent variable(s). We are familiar with examples in the area of drugs. A drug X might be desirable for treating a certain condition, but not if you are taking drug Y, because if you do take drugs X and Y together there is a bad consequence from their combination, a bad drug “interaction. The issue of statistical interaction potentially arises when there are two or more independent variables. The issue concerns how the effects of the independent variables cumulate.(Stipa), Your response
Sources of Variance in an Experiment
Watch “ANOVA (Part A) – Sources of Variance in an Experiment” on the YouTube website located at https://www.youtube.com/watch?v=JPjMUeTMOwg&feature=youtube_gdata What are some of the sources of variance in an experiment? Your response
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There is a lot of medical research being done on medications, diagnostics, medical equipment, and other technologies besides health care that must be tested before production and utilization. When testing the difference in populations it is almost impossible to test whole populations so samples gathered. There will always be sampling error because of this, which may bring false results. Tests are done to identify if the subject is effective or ineffective. When there are multiple groups, samples, or items that are being tested, it is necessary to use a comparison formula ANOVA because of false results that can occur. This formula compares the variation between groups and within groups. It allows you to conclude if the difference between the groups is more than random variation.
I have to schedule a lot of funraisers throughout the year and sometimes it is difficult to determine which one is effective. I break them down into catagories and time of year. The best is to have seasonal fundraising. Another unique part of fundraising is being aware of the population and community interests. You can have a false positive fundraising event on a schedule if you were not thinking about the time of year because if one fundraiser made a 1,000 dollars profit in the summer but did not do so well in the fall and nothing was changed in that fundraiser, then it is important to take into consideration the time of year. In this buisness it is good to analyze results by catagories of community, time, and community interests. Each fundraiser acts like a sample test to determine if communities are interested in it or not through the year. Your response
4I am using my teenager as test model because she has never had a statistics class as of yet. Explained analysis of variance or ANOVA in terms of the big picture and the little pictures that are within: categories. She has an older ford truck she is remodeling. She loves loves her truck, but it is expensive and thus she has a budget of expenses for it. Since it is already paid for, the categories are: Insurance/fees, engine repair, body repair, fluids/replaceables and ect. When she looks at the multitude of work to be done, she gets overwhelmed! But by breaking it into categories and then looking at the whole (her beloved truck) it seems much more manageable. The Minitab blog said it best The whole purpose of Analysis of Variance is to break up the variation into component parts, and then look at their significance. But there’s a catch: in statistics, Variance (the square of Standard Deviation) is not an “additive” quantity—in other words, you can’t just add the variance of two subsets and use the total as the variance of the combination” (Minitab, 2013).
Analysis of variance is a way to study a large chunk of information, to see it as not only categories, but the trends and thus gives you a tool for analysis. Your response
5.When two or more independent variables are involved in a research design, there is more to consider than simply the “main effect” of each of the independent variables (also termed “factors”). That is, the effect of one independent variable on the dependent variable of interest may not be the same at all levels of the other independent variable. Another way to put this is that the effect of one independent variable may depend on the level of the other independent variable.
In order to find an interaction, you must have a factorial design, in which the two (or more) independent variables are “crossed” with one another so that there are observations at every combination of levels of the two independent variables.
For example, if you were interested in the effects of practice and stress level on memory task performance, you might decide to employ a factorial design. You manipulate practice by having participants read a list of words either once or five times. You also manipulate stress level by having two conditions: in one (low stress), participants are told that the number of words that they recall is unimportant, and in the other (high stress), participants are told that most people can recall all words in the list, and that they are expected to be able to do so as well. Your dependent variable is the number of words recalled from the 30-word list.