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R语言课业代写 Writing Assignment代写

2022-01-18 11:57 星期二 所属: R语言代写 浏览:194

POLI 7000 Writing Assignment 3

R语言课业代写 A complete assignment consists of three parts: 1. First, the actual written document (white paper). You must produce the requireddocument in

A complete assignment consists of three parts:

1.First, the actual written document (white paper). You must produce the required document in a professional way. The required document is outlined in the section “Written Assignment.”

2.Second, the general questions and R script. You will be asked a series of questions about the statistics behind the written document (white paper). These questions are outlined in the section “R Instructions and Questions.”

(a) Answer these as comments in your R script.

(b) Then, to demonstrate your proficiency with the software, you must turn in the code you executed to obtain the results you use to construct the written document (memorandum).

3.Third, once all of the questions are answered, please run your R script to execute it in the R console. Copy and paste the R console at the end.

Thus, when turning in the assignment, you should have the written document (white paper) first.  R语言课业代写

Then, you should start a new page and paste the script, followed by the console, and save the whole thing as a .pdf. This is still a writing assignment. That means your sentences should be free of spelling and grammatical errors and should use complete sentences. You should also follow the guidelines for creating attractive documents, as outlined separately on Canvas.

Recall: you are allowed to consult with one another on coding for homework assignments, as long as

  1. You never “divide and conquer” the assignment. All students are responsible for all portions of each assignment, and
  2. You are not allowed to collaborate on the “applied” or “discussion” portions of questions. You can code together, but as soon as you start writing sentences, you must use your own words and your own words alone, and
  3. You explain your answers. Even if an answer is a simple mathematical solution, explain how you arrived at it. I can’t give partial credit for wrong numbers, but I can give partial credit for a thought process.

If you have questions . . .  R语言课业代写

  • Overall, please feel free to post to the relevant Discussion page on Canvas.
  • About R, please feel free to email me directly.
  • About style, please free to email Noemi ([email protected]), our TA.

Finally, you’ll notice that the point total below is 160. The assignment is worth 150 points.

To be clear: any points you earn above 150, you still get (so it’s like having a 10-point “bonus”). Since your final semester grade is just the total number of points you earn across all assignments, this is like having a 10-point bonus on any assignment.

The assignment starts on the next page.

 

R Instructions and Questions  R语言课业代写

Reminder: to download data and read it into R

  1. Download the dataset (usually XXX.csv) from Canvas
  2. Move it to wherever all of your course materials are on your computer
  3. Once it is on your computer, copy the filepath and read it into R
  • On a Mac, you can use option + command + c to copy a file path
  • On Windows, hold down shift and right-click to Copy as path. Seetinyurl.com/windows-file-path. Also, on Windows you must reverse the direction of the slashes in the filepath
  • As a reminder, there are scripts on Canvas to use as a helpful example

Read Thomsen and Sanders, “Gender Differences in Legislator Responsiveness.” (Note: you will be replicating Table 1, Models 1 and 2!)

Download the data from Canvas: thomsen.csv.1 It contains eight variables:  R语言课业代写


This is the actual dataset used to write the article we read.


  • X: A counter variable for each row
  • any_reply: An indicator (dummy) variable for if the legislator sent any reply

0 = No

1 = Yes

  • info_reply: An indicator (dummy) variable for if the legislator sent a helpful reply

0 = No

1 = Yes

  • female: An indicator (dummy) variable for if the legislator is female

0 = No

1 = Yes

  • republican: An indicator (dummy) variable for if the legislator is Republican

0 = No

1 = Yes

  • senate: An indicator (dummy) variable for if the legislator is in the state Senate

0 = No

1 = Yes

  • party_leader: An indicator (dummy) variable for if the legislator is a party leader

0 = No

1 = Yes

  • comm_chair: An indicator (dummy) variable for if the legislator is a committee chair

0 = No

1 = Yes

  • years_served: How many years the legislator has served (Number of years)
  • minority_party: An indicator (dummy) variable for if the legislator is in the minority party (doesn’t control the chamber)

0 = No

1 = Yes

  • prev_vote_share: The vote share for the legislator in the previous election

(Vote share in numeric)

  • up_for_reelection: An indicator (dummy) variable for if the legislator isup for reelection

0 = No

1 = Yes

The ideological conservatism of the district (higher numbers are more conservative)

(Ideology from liberal [low] to conservative [high])

  • dist_pop: The population of the district

(Number of population)

  • dist_income: The average income of the district

(Income in dollars)

  • state: An indicator variable for the state of the legislator

(Numeric indicator for the state, 1-50. Notice these are read as numbers, but the numbers have no order. They just indicate the state)

  • state_X: A series of indicator (dummy) variables for the state of the legislator. For each (like state_1), it is a dummy variable for if the legislator is from that state

0 = No

1 = Yes

Use the following instructions. If you see (XX points) beside a question, this means that this is an additional question you must answer in the R script for the assignment.

1.  R语言课业代写

Estimate the regression of any_reply (Y ) on female, republican, senate, party_leader, comm_chair, years_served, minority_party,prev_vote_share, up_for_reelection, dist_conservatism, dist_pop, dist_income, and all of the state_ dummy variables (state_2 through state_49, notice that there is no state_40).

2.

To ease interpretation, create two new variables: dist_pop_100000 (population in 100,000 units increments, so 1 = 100,000 people) and dist_income_10000 (income in $10,000 units increments, so 1 = $10,000 people).

3.(16 points [4 points each]):

Using ggplot, create four plots.   R语言课业代写

(a) The first two are barplots of the frequency of any_reply_fac (the first) and info_reply (the second), both filled by whether the legislator is female and faceted by if the legislator is republican.

(b) The second two are point (scatter) plots of dist_conservatism and dist_pop, with the points colored by if the legislator is female, and the plot characters changed by if the legislator replied (any_reply) (the third) or replied helpfully (info_reply) (the fourth). Your plots should exactly match the ones shown below.

(c) To consider: last time you made plots, you sometimes made factor versions of numeric variables so that ggplot understood them as categories better.

(d) Also to consider: you can save plots from R directly by using the pdf() function. You call pdf(), then make the plot, then run dev.off() to close the connection.

Example:

pdf("filepath.pdf")
ggplot(...)
dev.off()
R语言课业代写
R语言课业代写

 

4.

Estimate the regression of any_reply (Y ) on female, republican, senate, party_leader, comm_chair, years_served, minority_party,prev_vote_share, up_for_reelection, dist_conservatism, dist_pop_100000, dist_income_10000, and all of the state_ dummy variables (state_2through state_49, notice that there is no state_40).

 

 

5.  

Estimate the regression of info_reply (Y ) on female, republican, senate, party_leader, comm_chair, years_served, minority_party,prev_vote_share, up_for_reelection, dist_conservatism, dist_pop_100000, dist_income_10000, and all of the state_ dummy variables (state_2 through state_49, notice that there is no state_40).

R语言课业代写
R语言课业代写

(e) (3 points): Do these predictors better explain if we receive any reply or a helpful reply?

(f) (4 points): Which is more substantively significant, female or comm_chair? Justify your answer.

(g) (4 points): Which is more substantively significant, female or party_leader? Justify your answer.

(h) (4 points): Which is more substantively significant, female or years_served? Justify your answer.

 

6.(16 points [2 points each]):  R语言课业代写

Predict the likelihood of any_reply and info_reply, for a Democrat male (female = 0 and republican = 0), a Democrat female (female = 1 and republican = 0), a Republican male (female = 0 and republican = 1), anda Republican female (female = 1 and republican = 1), holding the other variables constant at someone who is in the state Senate, who is not a party leader, who is not a committee chair, with average (mean) years of service, who is not in the minority party, with average previous vote share, who is not up for reelection, with average district conservatism, with average district population, with average district income, and who is from state_2 (and thus not from any other state). Note: this means you will generate eight total predictions.

 

7.(4 points):

Why does it not makes sense to just hold all of the variables at their means?

 

Written Assignment  R语言课业代写

You are an employee at a nonpartisan, nonprofit organization whose mission is to encourage and train women run for elected office. The executive director of your organization comes across a dataset that measures a legislator’s responsiveness to his or her constituency. The executive director asks you to perform statistical analysis on the dataset and identify the relationship between female legislators and responsiveness. (5 points: you should invent the organization and create a logo for the first page of the document.)

You must write a white paper. On Canvas, there are many, many examples of white papers. Specifically for the assignment, your white paper must disseminate your findings and make recommendations for how the findings should be incorporated into future training programs.

Although this is an internal document for your executive director and training staff, keep in mind that it may also be shared on the organization’s website. Your white paper should include the following sections and the following information within each section.

Note: this is also meant to practice using analysis to communicate with non-analysts. As you discuss your models, you should do so in plain English.

Specifically:  R语言课业代写

  • Avoid phrases like “effffect of X on Y ,” instead focusing on the actual variables.
  • Where possible, replace the discussion of “units” of variables with the actual unit (for instance, if the variable is age, talk about a one-year increase in age, not a one-unit increase in age).
  • Use consistent, non-obnoxious rounding (like to the tenths place).
  • At no point should your document include R code.
  • Cite any research in a footnote.
  • In all other places, follow the white paper style guide on Canvas.

Executive Summary (10 points)

  • In less than a page:

Summarize the original study that the authors conducted, including any experimental manipulations.

Summarize the data, including explaining the unit of analysis (what a row in the dataset represents).

Summarize how the data were analyzed.

Summarize the findings.

Argue for the importance of the findings.

Background (6 points)  R语言课业代写

  • In less than a half page:

Using the literature review of Thomsen and Sanders (2020) as a guide, provide a brief background regarding the relationship between legislators and responsiveness.

Bring in any outside sources as you see fit.

Literature Review (6 points)

  • In less than a half page:

Using the literature review of Thomsen and Sanders (2020) as a guide, provide a brief background regarding why the relationship between legislators and responsiveness should be dependent on whether the legislator is male or female.

Bring in any outside sources as you see fit.

Data and Methods (8 points)  R语言课业代写

  • In less than a page:

Identify all of the variables that were collected.

Identify the measurement of the key independent variable (gender).

Identify the measurement of the two dependent variables (any response and helpful response).

Explain what the regressions that you run are doing to summarize the relationship.

At the end of the section, identify how the analysis you perform allows you to add to the discussion you outlined in the Literature Review section.

Findings (15 points)

  • In one or two pages:

Show your four plots. Interpret each.

What did your regressions find about the determinants of receiving replies and helpful replies?

Be sure to fully interpret the effect of being female.

Interpret the statistical and substantive significance of being Republican, being a party leader, and district conservatism in each regression.

In a footnote: explain what each regression “controls” for. Mention the two measures of fit for each regression.

Show and interpret your four predictions.

Proposed Solutions and Recommendations (6 points)  R语言课业代写

  • In one page:

Explain what actions your nonprofit should take, based on the data you analyzed.

How should the women taking part in the training program incorporate this information into their campaigns?

Keep in mind that although this is an internal document, it may be shared externally and used to persuade voters.

Conclusion (6 points)

  • In less than half a page:

Connect the findings back to the purpose and mission of your organization.

Explain how these findings reflect on politics and future directions in politics.

Remember, you must follow the rules for Document Design Best Practices (available on Canvas). The white paper has a maximum length of eight pages.

 

R语言课业代写
R语言课业代写

 

 

 

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