HW 1 solutions
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GV900
Homework 1
Fall 2023
Homework 1: Answer Key
Question 1
Load the ggplot2 library and then the midwest dataset.
Display the column
names for the midwest dataset.
[3 marks]
library(ggplot2)
data(
"midwest"
)
# OR data(midwest) is also fine
?midwest
names(midwest)
[1] “PID” “county” “state”
[4] “area” “poptotal” “popdensity”
[7] “popwhite” “popblack” “popamerindian”
[10] “popasian” “popother” “percwhite”
[13] “percblack” “percamerindan” “percasian”
[16] “percother” “popadults” “perchsd”
[19] “percollege” “percprof” “poppovertyknown”
[22] “percpovertyknown” “percbelowpoverty” “percchildbelowpovert” [25] “per-
cadultpoverty” “percelderlypoverty” “inmetro”
[28] “category”
GV900
Homework 1
Fall 2023
# or colnames(midwest) is also fine
Question 2
What type of variable is state? How can you tell? How many different states
are there in this dataset? What is the modal state in the dataset? [2 marks]
# create a frequency table for variable state
table(midwest$state)
##
##
IL
IN
MI
OH
WI
## 102
92
83
88
72
The variable ‘state’ is a nominal/categorical variable, as there is no inherent
order among the states and no numbers attached to them either. Within this
dataset, there are five distinct states. The modal state, meaning the state with
the highest frequency, is IL (Illinois).
Question 3
Look at the summary statistics for the variable popwhite. Just based on looking
at these numbers, describe the likely distribution of the variable. That is, in your
own words and in 2-3 sentences, describe the range, and the likely skewness of
the variable (if any). Make sure to explain why you think the variable is/isn’t
skewed a certain way based on just these summary statistics numbers [4 marks]
GV900
Homework 1
Fall 2023
summary(midwest$popwhite)
##
Min. 1st Qu.
Median
Mean 3rd Qu.
Max.
##
416
18630
34471
81840
72968 3204947
range(midwest$popwhite)
## [1]
416 3204947
# for the range, it's also fine to use max(midwest$popwhite) - min(midwest$pop
Based on the summary statistics, we can observe that the variable has a substan-
tial range, extending from a minimum number of 416 white people in a county to
a maximum value of 3,204,947. This wide range indicates a high degree of vari-
ability in the data. The median (34,471) is notably less than the mean (81,840),
suggesting a right-skewed distribution, as the mean is pulled towards the higher
values by extremely high data points.
Question 4
Now, make a histogram for the popwhite variable. In doing so, make sure that
you change the default binwidth, color, and axes labels, and that you give your
histogram a relevant title. Also make sure that you save your histogram as an
object rather than displaying it directly. If you were to describe the distribution
of this variable based on the histogram, would your description change in any
way from the previous answer? If yes, in what way? If no, why not? Explain
why you think this variable is skewed or not, i.e., think about what the variable
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Homework 1
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measures and explain why it probably looks the way it does. (Note that this
last part is not about how you can tell whether the variable is skewed or not
but, rather, asks you think about what the variable measures and why it makes
sense, or doesn’t make sense, that the variable is skewed or not in the way that
it is.) [6 marks]
# create histogram for 'popwhite'
popwhite_hist
<-
ggplot(
data =
midwest, aes(
x =
popwhite)) +
geom_histogram(
binwidth =
100000
,
color =
"purple"
,
fill =
"skyblue"
) +
labs(
x =
"Number of white people"
,
y =
"Frequency"
,
title =
"Histogram of white people in every county"
)
popwhite_hist
GV900
Homework 1
Fall 2023
0
100
200
300
0e+00
1e+06
2e+06
3e+06
Number of white people
Frequency
Histogram of white people in every county
Observing the histogram, I would draw a similar conclusion to before that the
variable is very much right-skewed. As the figure shows, most counties have up
to 200,000 white people with some having more but still under approximately
1 million. There are a few large outliers, especially the maximum value, which
gives the distribution a very long tail. Thus the decription of the variable would
be similar to before. As for
why
the variable looks this way, that is likely because
it is a raw count of the number of white people in a given county without taking
into account the size of the county or the overall population. In other words,
geographically larger counties or densely populated counties (such as counties
containing big cities) will have a higher number of people living there and also
a high number of white people living there. However, there won’t be very many
such counties as not every county has a huge urban center in it so the dataset
GV900
Homework 1
Fall 2023
will have a handful of such observations with the other observations having much
lower numbers of white people living there.
Question 5
Make and display a density plot for the variable called percollege. In doing so,
make sure you change the axes labels, add a title, and use a different color than
the default settings.
What kind of variable is percollege?
Now, describe the
variable using the density plot, i.e., think about the range, skewness, what types
of counties seem to be the most common based on this figure etc?
Based on
just this figure, is the mean or median of this variable likely to be higher? How
can you tell? Which of the two (i.e., mean or median) do you think is the more
representative summary statistic for this variable, and why? [7 marks]
# create density plot for 'percollege'
percollege_density
<-
ggplot(
data =
midwest,
aes(
x =
percollege)) +
geom_density(
color =
"purple"
,
fill =
"skyblue"
) +
labs(
x =
"Percent college educated"
,
y =
"Density"
,
title =
"Density of college educated (percentage)"
)
percollege_density
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Homework 1
Fall 2023
0.000
0.025
0.050
0.075
10
20
30
40
50
Percent college educated
Density
Density of college educated (percentage)
‘Percollege’ represents the percentage of college-educated individuals in each
county.
Therefore, the variable is quantitative and continuous.
The variable
exhibits a right-skewed distribution, which we can see from the long right tail.
The percentage of people in a county with college degrees ranges from approx-
imately 5% to close to 50%. From the plot, it seems that most counties have
approximately 15% people with college education. Given the heavy right-skew,
the mean is likely to be higher than the median as it gets pulled in the direction
of the tail.
In this skewed scenario, the median is a more suitable summary
statistic for describing the central tendency of the variable because the median
is not influenced by outliers in the way the mean is.
GV900
Homework 1
Fall 2023
Question 6
For this last question, you will not use the midwest dataset.
a
Create, store and display a sequence from 0 to 1000 in increments of 20. Next,
create, store and display a sequence of the same length as the first vector you
have created that goes from 2000 to 4000. Finally, create, store and display a
vector where you subtract the second vector from the first, i.e., vector 1 - vector
2. [3 marks]
# Create the first vector
seq_a
<-
seq(
from =
0
,
to =
1000
,
by =
20
)
seq_a
[1] 0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 [16] 300 320 340 360
380 400 420 440 460 480 500 520 540 560 580 [31] 600 620 640 660 680 700 720
740 760 780 800 820 840 860 880 [46] 900 920 940 960 980 1000
# Create the second vector
seq_b
<-
seq(
from =
2000
,
to =
4000
,
length.out =
51
)
# It is also fine to use length.out = length(seq_a)
seq_b
[1] 2000 2040 2080 2120 2160 2200 2240 2280 2320 2360 2400 2440 2480 2520
2560 [16] 2600 2640 2680 2720 2760 2800 2840 2880 2920 2960 3000 3040 3080
3120 3160 [31] 3200 3240 3280 3320 3360 3400 3440 3480 3520 3560 3600 3640
3680 3720 3760 [46] 3800 3840 3880 3920 3960 4000
GV900
Homework 1
Fall 2023
# Add vectors
seq_c
<-
seq_a - seq_b
seq_c
[1] -2000 -2020 -2040 -2060 -2080 -2100 -2120 -2140 -2160 -2180 -2200 -2220 [13]
-2240 -2260 -2280 -2300 -2320 -2340 -2360 -2380 -2400 -2420 -2440 -2460 [25]
-2480 -2500 -2520 -2540 -2560 -2580 -2600 -2620 -2640 -2660 -2680 -2700 [37]
-2720 -2740 -2760 -2780 -2800 -2820 -2840 -2860 -2880 -2900 -2920 -2940 [49]
-2960 -2980 -3000
b
Say I want to conduct a survey among 150 respondents where one of the ques-
tions I ask them is about their education level. Suggest an ordinal variable that
I can create to measure this; note that the variable should have at least three
categories and no more than five categories. Justify your answer, i.e., explain
why your suggested measure is a reasonable one. [2 marks]
• Less than High School (or equivalent)
• High School (or equivalent)
• Bachelor’s Degree
• Master’s Degree
• Doctorate or Professional Degree
I have suggested a five category ordinal variable based on common education
milestones around the world. Each category represents a higher education level
than the previous one and ensures that all possible education levels are covered.
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It also ensures no overlap between categories meaning that no individual will be
left out and no one will be counted twice. That is, for example, someone with no
formal education or only a few years of it would fit into the first category as long
as they have not completed a high school degree or equivalent in that country.
Anyone who has completed their high school education, or equivalent, but does
not have a college degree will go into the next category of ‘High School’. Simi-
larly, those whose highest education degree is a Bachelor’s degree or equivalent
will go into that category, and so on. I have chosen to use common milestones
also because it makes it easier to code individuals and sort them into a clear
category easily and quickly.
c
Now, using the categories you suggested in the previous part of the question,
create a hypothetical variable where you randomly sample from the suggested
values making sure that you change the default probabilities in some way. You
can use whatever probabilities you would like. Display the final output of this
variable. [3 marks]
# Generate the categorical variable and
# allocate random counts to each category.
education
<-
sample(
x =
c(
"Less than High School"
,
"High School"
,
"Bachelor's Degree"
,
GV900
Homework 1
Fall 2023
"Master's Degree"
,
"Doctorate or Professional Degree"
),
size =
150
,
prob =
c(
0.1
,
0.2
,
0.3
,
0.3
,
0.1
),
replace =
TRUE
)
# Display
education
[1] “Master’s Degree” “Master’s Degree”
[3] “Less than High School” “Master’s Degree”
[5] “High School” “Doctorate or Professional Degree” [7] “High School” “Doc-
torate or Professional Degree” [9] “Master’s Degree” “Master’s Degree”
[11] “High School” “Doctorate or Professional Degree” [13] “Bachelor’s Degree”
“Bachelor’s Degree”
[15] “Less than High School” “Master’s Degree”
[17] “Bachelor’s Degree” “Master’s Degree”
[19] “Master’s Degree” “Bachelor’s Degree”
[21] “High School” “Less than High School”
[23] “Master’s Degree” “Master’s Degree”
[25] “Master’s Degree” “Master’s Degree”
[27] “Master’s Degree” “Less than High School”
[29] “Bachelor’s Degree” “Less than High School”
[31] “Bachelor’s Degree” “Less than High School”
GV900
Homework 1
Fall 2023
[33] “High School” “Doctorate or Professional Degree” [35] “Doctorate or Pro-
fessional Degree” “Master’s Degree”
[37] “High School” “Master’s Degree”
[39] “Doctorate or Professional Degree” “Master’s Degree”
[41] “Less than High School” “Bachelor’s Degree”
[43] “Master’s Degree” “Bachelor’s Degree”
[45] “High School” “Bachelor’s Degree”
[47] “Less than High School” “High School”
[49] “Bachelor’s Degree” “Bachelor’s Degree”
[51] “Master’s Degree” “High School”
[53] “Bachelor’s Degree” “Master’s Degree”
[55] “Master’s Degree” “Bachelor’s Degree”
[57] “High School” “Master’s Degree”
[59] “High School” “Master’s Degree”
[61] “Master’s Degree” “High School”
[63] “Doctorate or Professional Degree” “Bachelor’s Degree”
[65] “Bachelor’s Degree” “High School”
[67] “Bachelor’s Degree” “Bachelor’s Degree”
[69] “Master’s Degree” “Less than High School”
[71] “Bachelor’s Degree” “Bachelor’s Degree”
[73] “High School” “Master’s Degree”
[75] “Master’s Degree” “High School”
[77] “Less than High School” “Bachelor’s Degree”
[79] “Bachelor’s Degree” “High School”
[81] “Less than High School” “Master’s Degree”
[83] “Bachelor’s Degree” “Bachelor’s Degree”
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[85] “Master’s Degree” “Less than High School”
[87] “Bachelor’s Degree” “High School”
[89] “Master’s Degree” “Less than High School”
[91] “Master’s Degree” “Bachelor’s Degree”
[93] “High School” “Master’s Degree”
[95] “Doctorate or Professional Degree” “Bachelor’s Degree”
[97] “Bachelor’s Degree” “Doctorate or Professional Degree” [99] “Master’s De-
gree” “Bachelor’s Degree”
[101] “Doctorate or Professional Degree” “Master’s Degree”
[103] “Master’s Degree” “Bachelor’s Degree”
[105] “Bachelor’s Degree” “High School”
[107] “High School” “Master’s Degree”
[109] “Less than High School” “Bachelor’s Degree”
[111] “Doctorate or Professional Degree” “Master’s Degree”
[113] “Master’s Degree” “High School”
[115] “Bachelor’s Degree” “Bachelor’s Degree”
[117] “Bachelor’s Degree” “Bachelor’s Degree”
[119] “Master’s Degree” “Master’s Degree”
[121] “High School” “Bachelor’s Degree”
[123] “High School” “Bachelor’s Degree”
[125] “Bachelor’s Degree” “Bachelor’s Degree”
[127] “Master’s Degree” “Bachelor’s Degree”
[129] “Master’s Degree” “High School”
[131] “Bachelor’s Degree” “High School”
[133] “Bachelor’s Degree” “Bachelor’s Degree”
[135] “Bachelor’s Degree” “Bachelor’s Degree”
GV900
Homework 1
Fall 2023
[137] “Doctorate or Professional Degree” “Bachelor’s Degree”
[139] “Bachelor’s Degree” “Bachelor’s Degree”
[141] “High School” “Less than High School”
[143] “Bachelor’s Degree” “Bachelor’s Degree”
[145] “Less than High School” “Master’s Degree”
[147] “Master’s Degree” “Less than High School”
[149] “Master’s Degree” “Less than High School”
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Female
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9.3
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9.5
9.5
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9.5
9.5
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If there is more than one mode, write them separated by commas.
If there is no mode, click on "No mode."
No
mode
?
Check
MacBook
esc
888
AA
F1
F2
F3
F4
F5
F6
#3
$
3
7
Q
W
E
R
T
Y
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milli, etc)
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2. 2x 10
3. .03
4. 2 Mega
5. .005
6.
.000001
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8. 417 x 10
9. 4x 10
10. 6 Tera
11. .000000003
12..1
13. 12 x 10
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16. 37 x 10
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1876
38
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1846
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1788
7
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1821
24
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1787
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1803
17
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1848
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16
2537
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- UOu9Lx4VyFM4RZrTQxDYme Walmart Credit Car.. Log in to Blackboar. 14 Friv : ONLY THE VE. Teaching Vocabula.. O ePortfolio System L. About the 5 ctivity @ D Present Tools Add-ons Help Last edit was 5 minutes ago Background Layout- Theme Transition USE THE TYPING TOOL TO TYPE AN "X" IN ANY COLUMN THAT CORRECTLY CLASSIFIES THE GIVEN VALUE IN THE ROW. NATURAL WHOLE INTEGER RATIONAL IRRATIONAL REAL 25 77 -1.75 V16 EManerngha Mdda LLC, 219 CLASSIFYING REAL NUMBERSarrow_forwardResearchers conducted a study on brain size as measured by pixels in a magnetic resonance imagery (MRI) scan. The numbers are in hundreds of thousands of pixels. The data table provides the sizes of the brains and the gender. Complete parts a through c. Click the icon to view the data table of brain size and gender. a. Is the format of the data set stacked or unstacked? The format of the data is stacked. b. Explain the coding. What do 1 and 0 represent? The data value 1 represents and the data value 0 represents c. If you answered "stacked" in part a, then unstack the data into two columns labeled Male and Female. If you answered "unstacked," then stack the data into one column with an appropriate name for the stacked variable. O A. В. С. Brain Size Female Male Male Female Male 9.3 9.5 9.3 9.3 9.5 9.5 9.7 9.5 9.5 9.7 9.5 9.9 9.7 1 9.7 9.9 9.7 9.9 9.9 9.9 9.9 9.7 9.9 1 9.9 9.9 9.9 9.9 1 D. B.arrow_forwardnote***** Answer Options for question A are: Row reduction, permutation, combination, or filing slotsarrow_forward
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- If a data set contains three groups that each have ten people in them, how many rows will the “data view” have? 30 3 100 10arrow_forwardhow to find bin width?arrow_forwardNumber Systems Worksheet Make a data table with 6 equal columns. Column 1-write the numbers as shown below Column 2- convert each of these numbers into a whole number or fraction Column 3- sort these converted numbers and fractions from lowest value on top to highest value on bottom Column 4- convert these sorted numbers and fractions as a decimal or N/A if not applicable. Column 5- convert these sorted numbers and fractions into scientific notation. Do NOT reduce these numbers/fractions when converting into scientific notation. Column 6 - write these sorted numbers and fractions with the appropriate Metric Prefix (eg. Kilo, milli, etc. Be sure to include any numerical value if appropriate (eg. 5 Kilo, 33 milli, etc) 1. 10 2. 2x 10 3. .03 4. 2 Mega 5. .005 6. .000001 7. 78 nano 8. 417 x 10 9. 4x 10 10. 6 Tera 11. .000000003 12..1 13. 12 x 10 14. 9,9930 15. 102 16. 37 x 10 17..00000009 18. 88 Hecto 19.77 Deka 20. 0arrow_forward
- Please solve only d, e, and f.arrow_forwardWe are interested in whether there is a relationship between the ranking of a state and the area of the state. State # letters inname Year entered theUnion Rank for entering theUnion Area (squaremiles) Alabama 7 1819 22 52,423 Colorado 8 1876 38 104,100 Hawaii 6 1959 50 10,932 Iowa 4 1846 29 56,276 Maryland 8 1788 7 12,407 Missouri 8 1821 24 69,709 New Jersey 9 1787 3 8,722 Ohio 4 1803 17 44,828 SouthCarolina 13 1788 8 32,008 Utah 4 1896 45 84,904 Wisconsin 9 1848 30 65,499 Part (a) Part (b) Part (c) Part (d) Calculate the least-squares line. Put the equation in the form of: ŷ = a + bx. (Round your answers to two decimal places.) ŷ = + x Part (e) Find the correlation coefficient. (Round your answer to two decimal places.)What does it imply about the significance of the relationship? It implies there is a linear relationship between the variables. Part (f) Find the estimated areas…arrow_forwardFind the number of permutations of a rectangle.arrow_forward
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