Download dataset http://www.cse.sc.edu/~rose/587/CSV/MaunaLoa.csv The description of dataset is ftp://aftp.cmdl.noaa.gov/products/trends/co2/co2_mm_mlo.txt

1. Explore the first two columns which contain real numbers: a. Plot the column labelled interpolated (Y) against the column labelled DecimalDate (X).

Save the plot to a pdf file.

b. Try fitting these two columns with a linear model lm(). Hint: You might want to review the linear regression lab.

c. As in the linear regression lab, visualize the model with the commands, where m is the variable you used to hold the model:

Save this plot to a pdf file.

d. Explainthetopleftfigure.Whatdoesthistellusaboutthefitofourmodel? e. Visualizethepredictedandobservedyvaluessimilartowhatwedidinslide6ofthe

linear regression lab. Save this graph to a pdf file. f. Looking at the plot from 1.a, explain whether this data is i.i.d. Justify your answer.

2. Download dataset http://www.cse.sc.edu/~rose/587/CSV/icu.csv The description of this data can be found at https://www.umass.edu/statdata/statdata/data/icu.txt Explore column 4 versus columns 1 and 2.

Plot column 2 STA (Y) versus column 3 AGE (X). Save this plot to a pdf file.

Plot column 2 STA (Y) against column 11 SYS (X). Save this plot to a pdf file.

Try fitting column 2 versus column 3 with a logistic model glm(). Hint: You might want to

review the logistic regression lab.

Now try fitting column 2 versus columns 3 and 11 with the logistic model glm(). How can

you accomplish this? When you only have Y versus X, you use Y~X as you did in step c.

When you have X1 and X2 then you use Y~X1+X2.

Compare the models from step c with that of step d using the function summary(). In

particular, compare the estimated coefficient for AGE. What are the two values? How have the confidence values for these estimates changed? (Hint: look at the significance codes.) How confident should we be? Explain!!!

Compare the estimated coefficients with each other from step d. Explain what the model says about age and the odds of surviving an icu visit. Explain what the model says about systolic blood pressure and the odds of surviving an icu visit.

Be sure to also submit your R code.

par(mfrow=c(2,2)) 

plot(m)