Statistic discussion response 4

Statistic discussion response 4

Please respond to these discussions with reference

Discussion 1

Analysis of Variance also referred to as ANOVA very simply put is a test used to determine if survey or experiment results are significant or not. It helps you figure out whether you should reject the null hypothesis or possibly accept an alternate hypothesis or not in an experiment. You are testing groups to find if there is any difference between them. An example of this would be if you were trying three different procedures to test male urinary retention one procedure may be catheterization, while the next may be a voiding schedule and the last may be medication. If you have run these experiments, then the ANOVA can help you to choose which of these interventions worked best for the patients.

There are also different types of the ANOVA test listed as one-way ANOVA and two-way ANOVA. The one-way ANOVA has one independent variable. So, if we were testing catheters a one-way test would look at type of catheter. While a two-way has two independent variables so for example catheter testing may have the type of catheter and the French of the catheter. (“ANOVA Test: Definition, Types, Examples”)

Another factor to consider when using ANOVA is levels. Levels are different groups in the same variable. For example, if we were looking at the type of catheter used a level may be the brand of that type of catheter. (“ANOVA Test: Definition, Types, Examples”)

Reference

ANOVA Test: Definition, Types, Examples. (n.d.). Retrieved May 21, 2018, from http://www.statisticshowto.com/probability-and-sta…

Discussion 2

ANOVA- Analysis of variance is used for testing treatments. The ANOVA is used when there are two or more treatments that have to be tested for efficacy. To apply ANOVA, all individuals are to be affected by the treatment similarly. The one-way ANOVA differentiates three or more populations when there is one factor of interest (Anders, 2017). There are certain requirements that are need before the use of one-way ANOVA is appropriate. The population distribution normal, the populations having the same variance, simple random samples of quantitative data, samples are independent, different and classified in only one way (Wall, 2017). There are limitations to using ANOVA which is not showing if there is a difference in the treatments. Once an ANOVA has been calculated, analysis of variance components from summary data becomes possible. Simple calculations based on summary data provide inference on significance testing. Examples are given from laboratory management and method comparisons. It is emphasized that the usual criteria of the underlying distribution of the raw data must be fulfilled. By using a post hoc test which is a test that shows the difference in treatment and minimizes error and bias (Grove & Cipher, 2017). A post hoc test should only be run when you have a shown an overall statistically significant difference in group means.

References

Anders, K. (2017). Resolution of Students t-tests, ANOVA and analysis of variance components from intermediary data. Biochemia Medica, 27(2), 253-258. doi:10.11613/BM.2017.026

Grove, S. K., Cipher, D. J. (2017). Statistics for Nursing Research: A Workbook for Evidence-Based Practice, 2nd Edition. Retrieved from https://pageburstls.elsevier.com/#/books/978032335…

Wall Emerson, R. (2017). ANOVA and t-tests. Journal of Visual Impairment & Blindness, 111(2), 193-196.

Discussion 3

If I had to explain analysis of variance (ANOVA) to an audience who has never taken a statistics course, memorizing the definition is easy, but working on some practice example problems and coming up with examples on my own will make it easier to explain the importance of ANOVA. After I have an overall understanding of ANOVA, I will attempt to teach the subject to someone who has taken a statistics course to determine if there is anything I am missing in the information I am providing. I would also need to consider that there may be some terminology I would need to address first before implementing it in my teaching. For example, I would need to go over what the “mean” is and how to calculate a group of numbers or “data set” to come up with the mean. Another term is variance which is important to understand because it will assist with coming up with inferences about the mean. The null hypothesis will be another term that will I will need to spend some time explaining before moving on to ANOVA.

I would first define what Analysis of Variance means. It is a “statistical method to test the difference between two or more means (Lane, 2016).”Analyzing the variance will help to determine the inference about the means.I would engage the audience by telling me what the null hypothesis would be for the example being used since null and alternative hypotheses were discussed prior to discussing ANOVA. The example I use will be something everyone can relate to like measuring the stress level among several groups of employees. The first sample are 5 people under normal times, another group with announced layoffs, and another group during layoffs (same 5 people in each sample), this measures the impact of announced layoffs. It allows you to compare samples at different points of times. (Explorable.com, 2009).In my opinion, I would not expect the audience to calculate ANOVA after one day of teaching analysis of variance, although I would expect the audience to understand why it is used and be able to come up with scenarios in which ANOVA can be used. After I am finished with my explanation of Analysis of Variance, I would recommend to everyone to take a statistics course.

References

Explorable.com. (2009, June 6). ANOVA. Retrieved from Explorable.com: https://explorable.com/anova

Lanve, D. (2016). Chapter 15: Analysis of Variance. Retrieved from onlinestatbook: http://onlinestatbook.com/2/analysis_of_variance/i…

Discussion 4

It is always hard to explain a technical issue to somebody without knowledge about it. However, any fast learner can understand analysis of variance easily. Analysis of variance (ANOVA) is a technique that helps to study the variation between two or more groups to determine if there are disparities in the mean of those individual groups. At the same time, it can be used to study general as opposed to specific variation between means and in that case the samples used must be taken from a population with a normal distribution.

ANOVA tests can be either one-way or two-way. In one -way ANOVA, what is measured is the variations among different groups and this type is the easiest type because you only use one grouping to describe the groups. This is applicable in different circumstances such as when checking how tutorial performance is related with a student’s final grade. In that case, you can compare the grades of the students that learnt through type A tutorial technique against the grades for the ones that learnt through type B tutorial technique. On the other hand, two -way ANOVA is used when handling complex groupings. For instance, you can compare teaching methodologies in overseas as compared to local institutions.

References

Statistics Solutions. (2013). ANOVA (Analysis of Variance). Retrieved from http://www.statisticssolutions.com/academic-soluti…

Discussion 5

Variance is the measure of how far numbers in a set are far from the mean; it is common in statistics for the probability distribution. Variance is obtained by the differences of each number in a set and the mean, squaring the difference and diving the sum of the squares by the number of values in the set. Practically, variance assists investors in measuring the risk in the purchase of securities. Significant variations show that numbers in the set are far from the mean, the smaller the variance the closer the numbers are to the average in the set (Weiss 6). However, the deviation is necessary for statistics to show the relation between numbers within the group. A primary parameter in asset allocation, variance assists investors to create optimal portfolios by optimizing the return to volatility trade-off in investment portfolios. Standard deviation is primarily used to express risk or volatility due to its natural interpretation.

David Weiss recognizes the importance of analysis of variance assist in finding out if the results of research are significant. Furthermore, he explains acquiring the analysis of variance skilled is easy through a practical approach (Weiss 3). For example, through variance analysis investor understand the causes of variation in earnings and expenditure throughout the budget plan period. There exist three kinds of analysis variance, two-way, one-way, and N-way analyses. One-way variance analysis has one independent variable, two-way on the other hand has two undependable variables. When researchers use more than two separate variables, they are said to have used the N-way variance analysis. Extensively used in probability theories, difference allows researchers to make general conclusions from a small set of the population. It is because of the ability of variance to provide a general idea of the data distribution

Reference

Weiss, David. Analysis of Variance and Functional Measurement: A Practical Guide. New York : Oxford University, 2006. Print

Discussion 6

An interaction arises when one is considering the relationship between three or even more variables. It describes situations where the simultaneous influence of two variables basically on a third is not additive. They are mostly considered in the field of regression analyses. When there are 2 or even more variables that are independent within any study, there is likelihood for concern for statistical interaction. The central area of target here would be whether one independent factor results to alter or affect the performance of other elements. This is about the consideration if an interaction exists between the two most important factors.

A good example includes relating to a patient having type 2 of Diabetes. One of the primary factors would be to use oral medication for the condition like Metformin versus a placebo. Another factor could be the usage of a 1500 to 1800 calorie ADA diet. This means that when we are looking for interaction, we will result to examine if the placebo or medication factor had affected the 1500-1800 calorie ADA factor of diet.

Reference:

www.statisticshowto.com/interaction-effect-interac…