University of California San Diego Assumptions of Correlation & Linear Model HW the homework problems are attached if you have any questions please ask. th

University of California San Diego Assumptions of Correlation & Linear Model HW the homework problems are attached if you have any questions please ask. there are 10 problems. i will write down some of them.

2.) Compare the assumptions of correlations with the assumptions of regression. Please be specific.

3.) When a dataset is not normally distributed, what should you try in order to appropriately make statistical inferences from the data? Please list at least three possible options.

4.) You are attempting to answer the following question: Is there an association between rainfall and phytoplankton blooms in coastal waters?

A) What statistical technique would you use and why?

B) What assumptions must you make about the data to perform the analysis?

9.)You grew 100 plants and measured the root weight and shoot (above-ground) weight for each plant, in order to see whether plants with larger roots also had larger shoots. You conducted your experiment and found that root weight and shoot weight were negatively correlated. You computed a regression and found that the line does a good job of explaining most of the variation in the data.

a) Sketch a plot for the described scenario. Be sure to draw the data points, the fitted line, and label the axes. You do not need to have a hundred points on the plot! Just a mock up is fine.

b) Write down a plausible r (correlation coefficient), b (slope), and R 2 value for the data and line you drew.

c) Explain how you choose those values.

d) Would you expect the residuals for this fit line to be large or small?

10.) Design a straightforward experiment that you would analyze using simple linear regression. For this experiment, provide:

A) your research question

B) your explanatory and response variables

C) a general linear model describing your experiment

D) the assumptions you will check before beginning your analysis

E) why this dataset is more appropriate for linear regression than correlation analysis.