Discussion: Exploratory Data Analysis

Discussion: Exploratory Data Analysis ORDER NOW FOR CUSTOMIZED AND ORIGINAL ESSAY PAPERS ON Discussion: Exploratory Data Analysis Use the provided file homework3.Rmd to complete this homework. When saved into the same folder as the data files below, the knitted Markdown document will provide the diagnostic plots for problem 1 and some data handling for problems 2 and 3. Make sure to update the document with your name. Discussion: Exploratory Data Analysis In problem 1 you are simply analyzing the provided diagnostic plots. For Problem 2 and Problem 3 perform a complete analysis of the described problem, this includes: Exploratory data analysis Checking the underlying assumptions Proper statistical inference Any follow-up procedures Conclusions in the context of the problem (You need to report the F statistics along with the degrees of freedom and the p -value) Problem 1 (10pts) – Model Diagnostics In this problem, an ANOVA model is fit three data sets and the diagnostic figures are provided. Please provide comments on these figures and discuss whether the underlying assumptions of ANOVA are satisfied or not. Make sure to specific which, if any, assumptions are violated. Data 1 – Seaweed grazers Description: To study the influence of ocean grazers on the regeneration of seaweed in the Intertidal zone, a researcher scraped rock plots free of seaweed and observed the degree of regeneration when certain types of seaweed-grazing animals were denied access. The grazers were limpets (L), small fishes (f) and large fishes (F). A plot was taken to be a square rock surface, 100 cm on each side. Each plot received one of six treatments, named here by which grazers were allowed access: LfF, fF, Lf, F, L and Control. Because the intertidal zone is a highly variable environment, the researcher applied the treatment in eight blocks of 12 plots each. With each block, she randomly assigned treatments to plots so that each treatment was applied to two plots. The data set is in file seaweed.csv Data Source: Ramsey, F.L. and Schafer, D.W. (2013), “The Statistical Sleuth: A Course in Methods of Data Analysis (3rd ed)”, Cengage Learning. Data 2 – Soybeans Description: In a completely randomized design with a 2x3x5 factorial treatment structure, researchers randomly assigned one of 30 treatment combinations to open-topped growing chambers, in which two soybean cultivars were planted. The responses for each chamber were the yields of the two types of soybean. The diagnostic figure provided is for a model with only one factor. The data set is in file soybean1.csv Data Source: Ramsey, F.L. and Schafer, D.W. (2013), “The Statistical Sleuth: A Course in Methods of Data Analysis (3rd ed)”, Cengage Learning. Data 3 – Mice lifetimes Description: Female mice were randomly assigned to six treatment groups to investigate whether restricting dietary intake increases life expectancy. There are six diet treatments. The data set is in file lifetime.csv Data Source: Ramsey, F.L. and Schafer, D.W. (2013), “The Statistical Sleuth: A Course in Methods of Data Analysis (3rd ed)”, Cengage Learning. Problem 2 (15pts) Description : A completely randomized factorial laboratory experimental design was used to study the effects of washing cycles and pre-washed methods on the abrasion of denim jeans. Pre-washed, stone-washed, and cellulase enzyme washed jeans were the garment washed denim treatments. The laundering cycles were zero (control group), five, and 25; Edge abrasion is the measure response. A total of 90 samples were utilized; 30 of each of the three garment washed denim treatments (pre-washed, stone-washed, and cellulase enzyme washed). From each group of 30 samples, ten samples were randomly assigned to each of the three laundering cycles (0/5/25). Samples were independently rated for edge abrasion after a fixed laundering interval. The data set is in file denim_abrasion.csv Below is the information for each column of the data set: Laundry Cycles (1= Control (0), 2=5 Launderings, 3=25) Denim Treatment (1=Pre-washed, 2=Stone-Washed, 3=Enzyme Washed) Edge abrasion Score Data Source : A. Card, M.A. Moore, M. Ankeny (2006). “Garment Washed Jeans: Impact of Launderings on Physical Properties,” International Journal of Clothing Science and Technology, Vol. 18, 1/2, pp. 43-52. Problem 3 (15pts) Description: The effect of germination time (48, 96, and 144h) on malt quality of four sorghum varieties was investigated to determine the potential of grain sorghum cultivars in the local brewery industry. The four evaluated sorghum varieties were Gambella 1107, Macia, Meko, and Red-Swazi. It is known that germination time will be influenced by other environmental effects (temperature, humidity, etc…). Due to limitations in the availability of equipment to perform the experiment, 12 samples were randomly assigned to each treatment and the experiment was repeated at three distinct time points (different days) resulting in 36 total observations. The data set is in file mat_var_germ1.csv Below is the information for each column of the data set: Variety (1-4 for 4 varieties) Germination (1-48h, 2-96h, 3-144h) Malting weight loss (MWL) Time (1-3, three time points) Data source: A. Bekele, G. Bultosa, and K. Belete (2012). “The Effect of Germination Time on Malt Quality of Six Sorghum (Sorghum Bicolor) Varieties Grown at Melkassa, Ethiopia,” Journal of Brewing, Vol. 118, Issue 1, pp. 76-81. Notes: Use headers to separate each question part, and label them meaningfully (e.g. “Problem 3, Part 2”). See in-class Markdown examples of this and use them in your assignment. All questions must include written answers in full problem context. Submitting only a Markdown with compiled R code but no supporting answers will only receive limited credit. You will upload your final knitted HTML to Canvas for grade. Make sure you place your name and homework number in the Markdown header, e.g. title: “Homework #3” author: “Your Name Here” date: “September .., 2020” output: html_document Reminder: Assignments in STA 363 are designed in such a way that we will be able to detect academic dishonesty. If you turn in another student’s generated Markdown document, we will know and proceed with an academic dishonesty claim. Discussion: Exploratory Data Analysis homework03.rmd Get a 10 % discount on an order above $ 100 Use the following coupon code : NURSING10

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