Biometry - ANOVA - Physiographic - Parametric and Non-Parametric - Statistics Assignment Help

Biometry Statistics Assignment Help

1.  Use the data in the file (plants.csv) to build the best possible model to explain the relationship between number of plant species and the various physiographic characteristics of islands, use transformations of data if deemed necessary. Check for model adequacy and for data points that might be outliers or exert undue influence. Be explicit about the criteria used to add variables to the overall model.
2. Examine the data in the file (baserunning.csv) to determine if these three techniques for rounding first base differ in the average time it takes to reach second base, and to determine which is the better technique - round-out, narrow angle, or wide angle?
3. Name that design! Remember Gn denotes a group of subjects, An, Bn, or Cn is a level of factor A, B, or C. State which factors are between subject, which are within subject and which are nested factors.

4.  What are the assumptions of regression and how do you determine if your data meet those assumptions.
5. Under what circumstances would a repeated measures ANOVA be preferable to a factorial ANOVA? What is a repeated measures ANOVA designed to achieve?
6.  When choosing between two alternative regression models for the same data, what criteria should be used to select the best model?
7. What are the three reasons for using transformations in regression?
8. What is the difference between the forward, backwards, and stepwise algorithms for model selection in regression?
9.  What are the differences between parametric, non-parametric and bootstrap based tests?
10. Use the data in the file (manzanita.csv) to build the best linear model possible to explain the number of species of herbivorous insects and the total abundance of herbivorous insects on species of manzanita in coastal California. Use quantitative and/or graphical techniques to show that the data meet the assumptions of regression, and that the model is an adequate model. (variables: site - site number (do not use as an independent variable), lfsize – mean leaf area in cm2, cong – number of sympatric manzanita species, range – geographic range as number of counties in California, aveden – average density (mm2) of foliar pubescence, avelen – average length of foliar pubescence (mm), abherb – abundance of herbivores, slarea – total leaf area of sample in cm2, srherb – species richness of herbivorous insects). Remember that site is just a name for each site so it should not be used as a variable in any model. Abherb and srherb are the two possible dependent variables. Treat slarea as a nuisance variable that must be entered on the first step of the regression (25 pts).

This Biometry Assignment has been solved by our Biometry experts at My Uni Paper. Our Assignment Writing Experts are efficient to provide a fresh solution to this question. We are serving more than 10000+ Students in Australia, UK & US by helping them to score HD in their academics. Our Experts are well trained to follow all marking rubrics & referencing style.