# R probability functions

(1) R has probability functions available for use. Using one distribution to approximate another is not uncommon.

(1)(a) (6 points) The normal distribution may be used to approximate the binomial distribution if np > 5 and

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np(1-p) > 5. Find the following binomial probabilities using dbinom() and pbinom() with a probability, p =

0.5, and n = 100. Then, estimate the same probabilities using the normal approximation with continuity

correction and pnorm().

(i) The probability of exactly 50 successes.

(ii) The probability of fewer than 40 successes.

(iii) The probability of 60 or more successes.

(1)(b) (4 points) With n = 100 and p = 0.02, use the binomial probabilities from dbinom() to calculate the

expected value and variance for this binomial distribution using the general formula for mean and variance of

a discrete distribution (To do this, you will need to use integer values from 0 to 100 as binomial outcomes

along with the corresponding binomial probability). Calculate the same using the formulae np and np(1-p).

n <- 100

p <- 0.02

n * p # expected value

## [1] 2

1

n * p * (1 – p) # variance

## [1] 1.96

(2) A recurring problem in statistics is the identification of outliers. This problem involves

plotting data to display outliers, and then classiying them.

(2)(a) (5 points) Generate a random sample, “x”, of 100 values using set.seed(123) and rexp(n = 100, rate =

1). Do not change this number. If you must draw another sample, start the process with set.seed(123) to

maintain comparability with the answer sheet. Present “x” in side-by-side box- and QQ-plots, using boxplot()

and qqnorm()qqline(). Use boxplot.stats() and/or logical statements to identify the extreme outliers, if any.

(2)(b) (5 points) Transform the random sample, “x”, generated in (a), to form a different variable, designated

“y”, using the Box-Cox Transformation: y = 3*(xˆ(1/3)) – 1). Display the values for “y” as in (a) and identify

outliers similarly.

(3) Performing hypothesis tests using random samples is fundamental to statistical inference.

The first part of this problem involves comparing two different diets. Using “ChickWeight”

data available in the base R, “datasets” package, execute the following code to prepare a data

frame for analysis.

data(ChickWeight)

# Create T | F vector indicating observations with Time == 21 and Diet == “1” OR “3”

index <- ChickWeight\$Time == 21 & (ChickWeight\$Diet == “1” | ChickWeight\$Diet == “3”)

# Create data frame, “result,” with the weight and Diet of those observations with “TRUE” “index”” values

result <- subset(ChickWeight[index, ], select = c(weight, Diet))

# Encode “Diet” as a factor

result\$Diet <- factor(result\$Diet)

str(result)

## Classes ‘nfnGroupedData’, ‘nfGroupedData’, ‘groupedData’ and ‘data.frame’: 26 obs. of 2 variables:

## \$ weight: num 205 215 202 157 223 157 305 98 124 175 …

## \$ Diet : Factor w/ 2 levels “1”,”3″: 1 1 1 1 1 1 1 1 1 1 …

The data frame, “result”, will have chick weights for two diets, identified as diet “1” and “3”.

Use the data frame, “result,” to complete the following item.

(3)(a) (4 points) Use the “weight” data for the two diets to test the null hypothesis of equal population

weights for the two diets. Test at the 95% confidence level with a two-sided t-test. This can be done using

t.test() in R. Assume equal variances. Display the results.

Working with paired data is another common statistical activity. The “ChickWeight” data

will be used to illustrate how the weight gain from week 20 to 21 may be analyzed. Use the

following code to prepare pre- and post-data from Diet == “3” for analysis.

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data(ChickWeight)

# Create T | F vector indicating observations with Diet == “3”

index <- ChickWeight\$Diet == “3”

# Create vector of “weight” for observations where Diet == “3” and Time == 20

pre <- subset(ChickWeight[index, ], Time == 20, select = weight)\$weight

# Create vector of “weight” for observations where Diet == “3” and Time == 21

post <- subset(ChickWeight[index, ], Time == 21, select = weight)\$weight

(3)(b) (6 points) Conduct a paired t-test and construct a two-sided, 95% confidence interval for the average

weight gain from week 20 to week 21. Do not use t.test(). Write the code for determination of the

confidence interval endpoints. Present the resulting interval.

(4) Statistical inference depends on using a sampling distribution for a statistic in order to make

confidence statements about unknown population parameters. The Central Limit Theorem is

used to justify use of the normal distribution as a sampling distribution for statistical inference.

Using Nile River flow data from 1871 to 1970, this problem demonstrates sampling distribution

convergence to normality. Use the code below to prepare the data.

data(Nile)

(4)(a) (3 points) Using Nile River flow data and the “moments” package, calculate skewness and kurtosis.

Present side-by-side displays using qqnorm()qqline() and boxplot(); i.e par(mfrow = c(1, 2)). Add features

to these displays as you choose.

library(moments)

(4)(b) (3 points) Using set.seed(124) and the Nile data, generate 1000 random samples of size n = 16, with

replacement. For each sample drawn, calculate and store the sample mean. This will require a for-loop and

use of the sample() function. Label the resulting 1000 mean values as “sample1”. Repeat these steps

using set.seed(127) – a different “seed” – and samples of size n = 64. Label these 1000 mean values

as “sample2”. Compute and present the mean value, sample standard deviation and sample variance for

“sample1” and “sample2”.

(4)(c) (4 points) Using “sample1” and “sample2”, present separate histograms with the normal density curve

superimposed (use par(mfrow = c(2, 1))). To prepare comparable histograms it will be necessary to use “freq

= FALSE” and to maintain the same x-axis with “xlim = c(750, 1050)”, and the same y-axis with “ylim

= c(0, 0.025).” To superimpose separate density functions, you will need to use the mean and

standard deviation for each “sample” – each histogram – separately.

(5) This problem deals with 2 x 2 contingency table analysis. This is an example of categorical

data analysis. The method shown in this problem can be used to

screen data for potential predictors that may be used in building a model.

The “Seatbelts” dataset contains monthly road casualties in Great Britain, 1969 to 1984. Use

the code below to organize the data and generate two factor variables: “killed” and “month”.

These variables will be used for contingency table analysis.

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data(Seatbelts)

Seatbelts <- as.data.frame(Seatbelts)

Seatbelts\$Month <- seq(from = 1, to = nrow(Seatbelts))

Seatbelts <- subset(Seatbelts, select = c(DriversKilled, Month))

summary(Seatbelts)

## DriversKilled Month

## Min. : 60.0 Min. : 1.00

## 1st Qu.:104.8 1st Qu.: 48.75

## Median :118.5 Median : 96.50

## Mean :122.8 Mean : 96.50

## 3rd Qu.:138.0 3rd Qu.:144.25

## Max. :198.0 Max. :192.00

killed <- factor(Seatbelts\$DriversKilled > 118.5, labels = c(“below”, “above”))

month <- factor(Seatbelts\$Month > 96.5, labels = c(“below”, “above”))

(5)(a) (3 points) Using “Seatbelts,” generate a scatterplot of the variables DriversKilled versus Month. This

is a time series, and Seatbelts\$Month should be on the horizontal axis. Show vertical and horizontal lines to

indicate the median of each variable. Label as desired.

(5)(b) (2 points) A chi-square test of independence will be used to test the null

hypothesis that the factor variables, “killed” and “month”, are independent. Use table() to generate a 2 x

2 contingency table showing the fatality count classified by “killed” and “month”. Use the uncorrected

chisq.test() to test the null hypothesis that “killed” and “month” are independent at the 95% confidence level.

Present these results.

(5)(c) (5 points) Write a function that computes the uncorrected Pearson Chi-squared statistic based on

the a 2 x 2 contingency table with margins added (check Davies, Section 11.1.1, pp. 216-219, and Kabacoff,

Section 20.1.3, pp. 473-474). Add margins to the contingency table from (b) using the function addmargins().

Submit this augmented table to the function you have written. Compare the result with (b). Your function

should duplicate and output the X-squared value (chi-squared) and p-value. Present both results.

The statements shown below calculate the expected value for each cell in an augmented contingency table

with margins added. Using these statements, the Pearson Chi-square statistic may be calculated. Other

approaches are acceptable.

e11 <- x[3, 1] * x[1, 3] / x[3, 3], e12 <- x[3, 2] * x[1, 3] / x[3, 3], e21 <- x[3, 1] * x[2, 3] / x[3, 3], e22 <- x[3,

2] * x[2, 3] / x[3, 3]

# Write function for computing uncorrected Pearson Chi-squared statistic and associated p-value

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