Description
1. Use R functions search and objects to find all possible R built-in functions related to
exp distribution. rexp should be one of them. dexp is another one. Explain what the
following two lines of codes do:
curve(dexp, from=0, to=4)
2. Use for loop to find the sum of 1,2, to, 100 (which can be created by : operator) as well
as use R function sum directly. Modify your for loop codes to get the mean (average)
instead of sum and use R function mean directly. Comment your findings and which way
is better?
3. Use for loop to calculate the sum Pn
j=0 r
j
, where r = 1.08, and compare with (1 −
r
n+1)/(1 − r), for n = 10, 20, 30, 40. Use sum instead of for loop to find the answers.
Compare and comment those two approaches.
4. The empirical rule states that approximately 95% of data from a normal distribution with
a mean of 0 and a standard deviation of 1 will have an absolute value less than 2. Use
the mean and rnorm functions to find the proportion of 1000 random normal variables
whose absolute values are less than 2. Repeat several times and see how widely the results
vary. Try sample size 10000 to see if the variation does become smaller.
5. Eight patients are enlisted in a diet experiment. We have a full 23
factorial design. Please
create an R matrix object representing the design matrix:
1 1 1 1
2 1 1 2
3 1 2 1
4 1 2 2
5 2 1 1
6 2 1 2
7 2 2 1
8 2 2 2
Notice that no for loop is allowed to generate such a matrix object. Hint: check help(rep)
6. Consider the built-in data frame cars.
(a) Consult the help page to determine the number of observations in the dataset as
well as the number of variables. Also, what are the names of the variables?
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(b) Find the mean stopping distance for all observations for which the speed was 20
miles per hour.
(c) Construct a scatterplot relating stopping distance to speed. What kind of relationship do you observe?
7. Consider the built-in data frame USArrests.
(a) Determine the number of rows and columns for this data frame.
(b) Calculate the median of each column of this data frame.
(c) Find the average per capita murder rate (Murder) in regions where the percentage
of the population living in urban areas (UrbanPop) exceeds 77%. Compare this with
the average per capita murder rate where urban area population is less than 50%.
(d) Construct a new data frame consisting of a random samples of 12 of the records of the
USArrests data frame, where the records have been sampled without replacement.
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