Computer Science Homework Help

I WILL REQUIRE 2 PEER POSTS REPLIES ONCE YOU ANSWER THE ABOVE TWO QUESTIONS

1. How can you get a feel for whether or not there is a difference among several population means by examining the data?
2. For what purpose is the F-test used?
3. Describe a randomized block design.  How is this design different from an independent experimental design?

1)Inferential Statistics

A population refers to collecting individuals of common species who live and interbreed in the same environment. Often, members of the people depend on shared resources subjected to similar environmental restrictions and rely on other members to persevere over time. Scientists examine the population by studying individuals within that population interact with one another and the people (Weeks, 2020). Population ecologists depend on the statistical measurements, referred to as demographic parameters, to pronounce the population. An F-test refers to any statistical test that applies f-statistic or f-value.

The f-value is a proportion of any two model variances that have f-distribution in the void hypothesis testing. In the scrutiny of conflict, the f-test gets frequently applied in ANOVA to calculate the equality of means when three or more groups get involved. It executes its functions by assessing variations between and in the different groups (Wang et al., 2017). Also, in statistics, the F-test is often applied when comparing statistical samples that can fit a data set to spot the population’s selection from a sampled data. When performing an f-test, various properties and assumptions get into the application. Some of the assumptions made may include; the models are independent of one another, the data in the samples follow a normal distribution, and the groups’ standard deviation possess the same variances.

A randomized block design refers to the experimental design that has functional divisions in groups known as blocks. The action gets assigned indiscriminately to the experimental units within each block. Therefore, when all show at least once in every block, it has undergone a completely randomized design. On the other hand, the experimental design conveys experimental units to treatment situations (Shiraishi et al., 2018). A perfect experimental design performs three functions, namely; causation, control, and variability. Causation grants the experimenter to apply causal inferences association between independent and dependent variables. The control design presents the experimenter to rule out substitute purposes; the variability function reduces the variability in the treatment conditions, making it easy to check variations in the outcomes.

References

Shiraishi, T. A., & Matsuda, S. I. (2018). Closed testing procedures for all pairwise comparisons in a randomized block design. Communications in Statistics-Theory and Methods, 47(15), 3571-3587.

Wang, S., & Cui, H. (2017). Generalized F-test for high dimensional regression coefficients of partially linear models. Journal of Systems Science and Complexity, 30(5), 1206-1226.

Weeks, J. R. (2020). Population: An introduction to concepts and issues. Cengage Learning.

2) This 2nd peer post is from my Profesor so you can respond to this or add few lines on what you think :

When you compute a confidence interval on the mean, you compute the mean of a sample in order to estimate the mean of the population. Clearly, if you already knew the population mean, there would be no need for a confidence interval. However, to explain how confidence intervals are constructed, we are going to work backwards and begin by assuming characteristics of the population. Then we will show how sample data can be used to construct a confidence interval.