[SOLVED] MATH 4753 Assignment 1

1. Summarize how I will workout your final grade for the course. Give percentages etc. Give my
grading scale also e.g. What percentage is an A etc.2. A biologist wants to make a coplot of LENGTH Vs WEIGHT given RIVER*SPECIES for
fish caught in the Tennessee river and recorded in the DDT.csv data set, so that each point is
colored according to the variable MILE which is treated as a factor (Qualitative variable).
> head(ddt)RIVER MILE SPECIES LENGTH WEIGHT DDT
1 FCM 5 CCATFISH 42.5 732 10
2 FCM 5 CCATFISH 44.0 795 16
3 FCM 5 CCATFISH 41.5 547 23
4 FCM 5 CCATFISH 39.0 465 21
5 FCM 5 CCATFISH 50.5 1252 50
6 FCM 5 CCATFISH 52.0 1255 150
# The following code may help
m=with(ddt, as.numeric(factor(MILE))) # A
length(unique(m)) #B(a) Make the coplot as the biologist required Hint: Use coplot(), Lab 1, the code provided, and
plotting options pch and col to differentiate the MILE variable. You should be able to produce
something like what is shown below(b) Interpret the lower left three conditional plots.
(c) What does line A do?(d) What does line B do?
(e) Why are the top six plots empty?(f) What is the mean value of DDT found in the sample of CCATFISH caught in the FCM river?
Hint:
ddt=read.csv(..\CSV\DDT.csv)
head(ddt)
subset(ddt,RIVER==FCM & SPECIES==CCATFISH,) #or
ddt[ddt$RIVER==FCM & ddt$SPECIES==CCATFISH,]3. MS 1.14 pg 84. MS page 12,13 Read pages 12 and 13 about random sampling designs and answer the following:
MATH 4753 Assignment 1 Page 2 of 6
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500 1500
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500 1500
WEIGHT
LENGTH
FCM
LCM
SCM
TRM
Given : RIVER
CCATFISH
LMBASS
SMBUFFALO
Given : SPECIES(a) What are the names of the four random sampling designs (1 simple and 3 more complex).
(b) Give a brief description of each.5. MS 1.15 pg 15 Use sample(,replace=FALSE), if mtbe is the dataframe then we need
a random sample of the rows. If v is a vector containing a random sample of row indices then
mtbe[v,] will be the random sample.mtbe=read.csv(..\CSV\MTBE.csv, header=TRUE) # You will need to change the address
head(mtbe) # First six lines
dim(mtbe) # rows and columns
ind=sample(1:223,5,replace=FALSE) # random indices
mtbe[ind,](a) Answer the additional problems below
(i) Remove all the rows in mtbe that contain one or more NAs mtbeo=na.omit(mtbe)
(ii) Now calculate the standard deviation (sd() in R) of the depth of wells which have
Bedrock as the Aquifier (this is using the entire mtbeo data frame), Hint: You will
need to alter the following code
depth=mtbeo[mtbeo$Aquifier==Unconsoli,]$Depth
mean(depth)6. MS 1.16 pg 15 Use sample(,replace=FALSE), if eq is the dataframe then we need a
MATH 4753 Assignment 1 Page 3 of 6
random sample of the rows. If v is a vector containing a random sample of row indices then eq[v,]
will be the random sample.(a) Answer the additional problems below
(i) Make the following plot plot(ts(eq$MAG)) and record it here:
(ii) Using the entire eq data frame find the median (median()) of the MAGNITUDE variable.7. MS STATISTICS IN ACTION Read the story on page 18 then answer the following:
(a) What is the data collection method?
(b) What is the population?
(c) Give the names of all the qualitative variables.8. MS 2.1 pg 26 Use pareto() Hint:
freq=c(15,8,63,20)
RL=c(None,Both,LegsO,WheelsO)
l=rep(RL,freq)9. MS 2.4 pg 27 Please use the pareto() function I made.10. MS 2.10 pg 28 Use pie3D() from plotrix package (may need to install it) Hint:
swd=read.csv(..//CSV//SWDEFECTS.csv, header=TRUE)
head(swd)
library(plotrix)
tab=table(swd$defect)
rtab=tab/sum(tab)
round(rtab,2)
pie3D(rtab,labels=list(OK,Defective),main=pie plot of SWD)11. MS 2.72 pg 70 When answering this question you will need to do most of the construction
by hand. Unlike other questions please follow parts a) -m) in conjunction with MS as I have
given below. For constructing the histogram and table below use the left end point as 8.0 and
right end point as 10.6, with 9 classes.After constructing table 1 make the graph in R using
barplot(,space=0), use the classes as names to the vector containing the frequencies.(a) Fill out the table when constructing the Histogram in pt a). Then plot the histogram by first
creating a vector, v say, of relative frequencies, then use names(v) and assign class names to
each component, finally using barplot(v,space=0) make your plot.Class Class Interval Data Tabulation Frequency Relative Frequency
1 8.0000-8.2889
2
3
4
5
6
7
8
9
Total
Table 1: Histogram table
(b) Use the stem() function in R for part b).
(c) Use R to make the histogram. Do NOT use hist()Hint: You may wish to use the following functions subset(,subset=LOCATION==NEW),
cut(), table(), barplot(,space=0) and ?cut etc See in class instruction concerning
this and ..,
new<-subset(voltage.df,subset=LOCATION==NEW)
new$VOLTAGE->vtn
vtn
max(vtn)
min(vtn)
lept<-min(vtn)-0.05
rept<-max(vtn)+0.05
rnge<-rept-lept
inc<-rnge/9
inc
seq(lept, rept,by=inc)->cl
cl
cvtn<-cut(vtn,breaks=cl)
new.tab=table(cvtn)
barplot(new.tab,space=0,main=Frequency Histogram(NEW),las=2)
hist(vtn,nclass=10)(d) Now complete d)-m) You can use any of the built in R functions
12. MS 2.73 pg 70
13. MS 2.80 pg 72
14. MS 2.84 pg 7415. Using the ddt data set re-create the plot below using ggplot, the code must be pasted into this
document use courier new font. Make sure your plot is titled with your name. NB You MUST
use ggplot()
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40
50
FCM LCM SCM TRM
RIVER
LENGTH
SPECIES
CCATFISH
LMBASS
SMBUFFALO
Figure 1: GGPLOT used to make this image