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Final Project PS 3780 Data Literacy & Visualization, Summer 2021 Due Date: Monday, August 2, 2021 at 11:59 p.m. Final Project Description This is an individual assignment. Your �nal project for this...

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Final Project
PS 3780 Data Literacy & Visualization, Summer 2021
Due Date: Monday, August 2, 2021 at 11:59 p.m.
Final Project Description
This is an individual assignment. Your �nal project for this class involves answering an
interesting question or testing an interesting theory using data visualization. In the �nal
paper, you will do the following:
1. State the question or theory explicitly and explain why you �nd it interesting in
the introduction.
2. State one or up to three hypotheses that you derive from your theory to make
empirical test.
3. Explain why the data you examine will help you to test your hypothesis/-es. De-
scribe the data and where you obtained them, what (if anything) you did to reformat
or transform them, how you analyzed them, and what they told you.
4. Create and include at least two (2) unique visualizations (maximum of 4). Fo
maximum credit, at least two (2) visualizations should be made using R. All visu-
alizations should be made using programs or websites that we learned in this class.
(A list is included below; this does not include Excel).
5. What do you know that you did not know before? Does the answer raise furthe
questions that might be worth investigating? If so, describe them
ie�y.
I anticipate the text portion of papers to be 3 - 4 pages long (before adding visualizations),
double-space, no smaller than 11-point type and 1-inch margins, with in text visualiza-
tions (but no larger than 1/4-page each), though succinct writers may take less space
and those with more complex problems or answers may take more. Papers should be
professional in quality: page numbers, formatting, and paper organization all count, with
citations either in text or in footnotes with a works cited page at the end. (Works cited
pages do not count toward the total page count.) Unless you collected the data yourself,
e sure to cite your data sources! Due to the University's strict timeline for �nal grades,
no extensions can be o�ered except in case of genuine emergency. We look forward to
eceiving your best e�ort by 11:59 p.m. on August 2. You will submit the �nal
paper to Carmen as well as the .csv �le(s) of your data and any R code that
you used to generate the visualizations within your paper.
1
Tips for Moving Forward
Imagine the Would-be World
Given the hypotheses that you have proposed, imagine the state of the world that would
exist if the hypotheses were true as well as the state of the world where the opposite
(your alternative hypotheses so to speak) was true. What evidence would be seen in both
cases? Having such expectations will not only help you �nd appropriate data to test
your hypotheses but also give you the hints about whether your original hypotheses are
favored by the empirical evidence from visualization.
Collect Data
Use your hypotheses from above to begin searching for data. For this part of the project,
focus in particular on specifying how you will measure the di�erent variables speci�ed
y your hypothesis/-es. For example, if your argument is that democracies do not �ght
one another, you will need to �gure out how you will measure both democracy and
international con�ict. Once you have accomplished this, you can begin searching for and
collecting data on these variables. Toward this end, it may help you to do the following:
1. Write a paragraph explaining what the relevant variables for your question are,
eing as speci�c as possible (including the relevant time frame for your question,
the relevant states/countries, etc.).
2. Find and download data measuring all of the variables needed to answer you
esearch question. Save this data as a .csv �le or �les.
3. Explain why the data you found will help you answer the question. Here you should
describe the data in detail and defend your decision to use it by explaining why it
is relevant to the question and why you trust it to be credible information. Make
sure you answer these questions: Where do the data come from? What do they tell
us generally? What is and is not measured? How is it measured?
Analyze the Data & Create Visualizations
Now that you have your data, you can begin cleaning and analyzing it. To learn more
about your data, I suggest using R to do any or all of the following:
1. Reformat or transform the data if necessary.
2. Do basic descriptive statistics in R, including: mean(), median(), summary(),
length(), and table () as appropriate for your speci�c dataset and variables
of interest.
Approved tools for creating visualizations:
ˆ R
ˆ World Bank Databank
ˆ Google Ngram
2
ˆ DataWrappe
ˆ Gapminde
Write Up Your Results
Work all of the above into a �nal na
ative that includes your question/theory, the reasons
for which you �nd it interesting, your hypothesis/-es, your data, your analysis, you
visualization(s), and the results. Be succinct. Too often, college students learn to pad
papers in order to reach high page limits. The suggested page length is meant to help
you un-learn that habit and get right to the point.
3

3780finalpape
u
ic-3qfy3hoz.docx
PS 3780
Final Paper Ru
ic
         Basic Requirements /6 pts
        Paper is submitted as one pdf, with visualizations included in text, and R code (if applicable) provided in the Appendix. Data file(s) in csv format are included in the submission.
        
2 pts
        Paper uses R to make the visualizations
        
/4 pts
         XXXXXXXXXXPaper Content /34 pts
        Paper proposes a theory, a hypothesis (or several), and the mechanisms by which the hypothesis can be supported or disproved.
These theories and hypotheses are considered an interesting political topic.
        
/10 pts
        Paper describes the data used – where it came from, what organization collected it, and why
it is a good dataset.
        
5 pts
        Paper provides at least 2 visualizations that fit all the properties of good graphical design, created by a program learned in the class.
        
10 pts
        Paper rigorously analyzes the visualization. It also has a clear conclusion where the hypothesis is either supported or refuted.
        
/5 pts
        Paper is well-written with solid structure,
flow and grammar.
         /4 pts
        TOTAL
         /40 pts
assignment-92021su-mh
maxp.pdf
Assignment 9
PS 3780 Data Literacy & Visualization, Spring 2021
Due Date: Friday, July 23, 2021 at 11:59 p.m.
Please save your visualizations and answers to these questions as one .pdf
�le (use the �save as� function in most word processors). Be sure to include your name,
your teammate's name if there is anyone, and the assignment number. Submit the �le
to Carmen by the due date. Remember we are looking for professional visualizations so
please include a meaningful title as well as axis labels and a legend.
Make Money, Feel Good
Use R to load the assignment9.csv dataset from Carmen. This data was collected from
OSU's subscription to Gallup. There are 5 variables: State, Year, Getting.better, Eco-
nomic.con�dence, and Region. Getting.better and Economic.con�dence are survey re-
sponses that indicate whether the respondent feels their local situation is `getting better'
and whether they have con�dence in the state of the national economy. Choose one item
from each section below. Create and analyze an appropriate visualization (or 2) for the
chosen questions. When analyzing relationships or trends, use a line plot or add summary
lines to the plot (ie. geom_smooth). Write down what commands that you use for each.
(4 pt each)
1 Maps
ˆ Choose a year: Was there any geographic clustering of the feeling that things are
getting better and con�dence in the economy? How similar is the geographic vari-
ation for those two questions? (2 maps)
ˆ Choose two years: Was there any geographic clustering to the change in eithe
(choose one: economic con�dence or the feeling that the situation is getting better)
etween the chosen years? (1 map)
2 Plots
ˆ What was the general relationship between economic con�dence and the feeling
that things are getting better across the years? How varied is the relationship in
di�erent years? (1 plot)
1
ˆ Is there any variation in the relationship between economic con�dence and the
feeling that things are getting better across di�erent regions of the United States?
(1 plot)
ˆ Are the time trends of (choose one: economic con�dence or the feeling that the
situation is getting better) di�erent by region? (1 plot)
ˆ Pick 2 regions: How similar are the time trends of (choose one: economic con�dence
or the feeling that the situation is getting better) among the states within the chosen
egions? (2 plots)
2
        Maps
        Plots
assignment-9-fin-avwmh21w.docx
Alexandra G Albanese
July 20, 2021
Assignment 9
li
ary(magrittr)
li
ary(dplyr)
li
ary(ggplot2)
li
ary(GGally)
li
ary(skimr)
li
ary(readr)
assignment9 <- read_csv("assignment9.csv", col_types = cols(State = col_factor(levels = c()), Region = col_factor(levels = c())))
skim(assignment9)
Data summary
        Name
        assignment9
        Number of rows
        306
        Number of columns
        5
        _______________________
        
        Column type frequency:
        
        facto
        2
        numeric
        3
        ________________________
        
        Group variables
        None
Variable type: facto
        skim_variable
        n_missing
        complete_rate
        ordered
        n_unique
        top_counts
        State
        0
        1
        FALSE
        51
        ala: 6, ala: 6, ari: 6, ark: 6
        Region
        0
        1
        FALSE
        9
        Sou: 54, Mou: 48, Wes: 42, New: 36
Variable type: numeric
        skim_variable
        n_missing
        complete_rate
        mean
        sd
        p0
        p25
        p50
        p75
        p100
        hist
        Yea
        0
        1
        2010.50
        1.71
        2008.00
        2009.00
        2010.50
        2012.00
        2013.0
        ▇▃▃▃▃
        Getting bette
        0
        1
        0.58
        0.06
        0.41
        0.53
        0.58
        0.61
        0.8
        ▁▆▇▂▁
        Economic confidence
        0
Answered 3 days After Jul 30, 2021

Solution

Pritam Kumar answered on Aug 03 2021
127 Votes
Hypothesis Testing
Introduction
Road accidents are vehicular collisions other vehicles, pedestrians, animals, or other stationary obstructions. Most often, these traffic collisions result in either injury or disability, although not leading to death in the cu
ent world. These accidents cause property damage, sometimes with severe financial consequences. Governments across the world vigorously try to improve the road infrastructure so as to make road travel safer and smoother. Number of reasons might be routed into when investigations are ca
ied on after any such unfortunate incidents. However, with the help of data, one wonders whether there are things inherent in these causes for accidents. For example, we can consider that speed limits might be one of the ways to mitigate road accidents. However, one might ask whether this is a feasible solution. What about the potential economic loss because of the wastage of time due to these speed restrictions? Is there a better way to address these challenges?
Proposed hypotheses
We maybe are able to find some things curious enough to say that whether a particular type of vehicle, such as cars, buses, trucks, etc. has something to say about the road accidents. Similarly, does smaller roads with high traffic potentially leads to congestion, frustration on the part of the drivers, and this ultimately leading to rash driving and accidents. Let’s consider two research hypotheses:
· Whether higher number of trucks on roads lead to more accidents
· Whether high traffic leads to more accidents
About the dataset
For the proposed hypotheses, we need a scientific way for analysis and reaching for any conclusions. Statistical techniques such as hypothesis testing perfectly suits to this scenario. However, for such analysis, we need a dataset that has variables we desire to have. RDatasets (Arel-Bundock) is a fantastic repository of many datasets that can be readily used for statistics and data science related projects. For our project, we will be using the Highway1 dataset.
The data comes from an unpublished master's paper by Carl Hoffstedt. In this dataset, a variable automobile accident rate (in accidents per million vehicle miles) is accompanied with several potential causes as other variables. The dataset claims to have data of 39 sections of large highways in the state of Minnesota in 1973. It is clear that the accident rate becomes our target variable in our data. There are several feature variables such as length of the highway, average daily traffic count, truck volume, total number of lanes of traffic, speed limit, etc. The goal of this analysis was to understand the impact...
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