R 语言代写:100%高分Pass,可接急单

R是 Ross Ihaka 和 Robert Gentleman 于 1993 年开发的一种编程语言和免费软件。R 拥有广泛的统计和图形方法目录。它包括机器学习算法、线性回归、时间序列、统计推断等等。大多数 R 库都是用 R 编写的,但对于繁重的计算任务,首选 C、C++ 和 Fortran 代码。

R不仅受学术委托,很多大公司也使用R编程语言,包括Uber、Google、Airbnb、Facebook等。使用 R 进行数据分析是通过一系列步骤完成的;编程、转​​换、发现、建模和交流结果。

程序:R是一个清晰易懂的编程工具

转换:R 由一组专为数据科学设计的库组成

发现:调查数据,完善你的假设并分析它们

模型:R 提供了广泛的工具来为您的数据捕获正确的模型

沟通:使用 R Markdown 将代码、图表和输出集成到报告中,或构建闪亮的应用程序以与世界分享

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下面是一个R语言的作业代写案例:

1. Using cces.dta, examine Americans’ support for expanding “concealed carry’ ’ permits, which allow
individuals to obtain a license that allows them to carry concealed firearms in public places. The
relevant variable is CC18_320d.
(a) Using margins, what is P r(support concealed carry) \ for women, holding all other variables at
their actual values? For men? What is the difference between these two average predictive margins,
and what is the 95% CI around that difference?
Note: I would recommend using cplot() or prediction() to get the average predictive margins
for women and men. Then construct a confidence interval around the difference in means by
manually constructing a standard error for the difference in means.
insert written answer here
# insert code here

2. Using margins, construct a table like that in Lecture 10 (on the slide titled “LPM, or Logit/Probit?”
with the table “Estimated difference in probability of voting associated with. . . ”) for the concealed-carry
dependent variable. Include calculations of differences in predictive margins for race (compare whites
and non-whites), size of place (compare suburban and rural residents), education for whites (compare
college educated and high school educated whites), and education for non-whites (compare college
educated and high school educated non-whites). Include separate columns for your probit and LPM
estimates. Interpret this table in a few sentences, describing what seems interesting and important.
Note: I would recommend using cplot() or prediction() to obtain the relevant average predictive
margins. Then manually take the difference and construct a table.
insert written answer here
# insert code here

3. (Harder.) Using the plotting feature of margins, construct a graph showing average predictive margins
of support for concealed carry by race (whites and non-whites) and education.
# insert code here

1. Now using cces2021.dta,
(a) Replicate the ordered probit model (“DV: approval of Biden”) estimated in Lecture 10. Use the
variables cc21_315a, birthyr, race, educ, and inputstate.
Some recoding is necessary, including recoding “Not sure” in cc21_315a as missing, creating the
variable age = 2021- birthyr, and recoding educ as:
• “no hs”=8,
• “high school graduate”=12,
• “some college”=14,
• “2-year”=14,
• “4-year”=16, and
• “post-grad”=20.
# insert code here

(b) Now run the same model using OLS. To do so, score the dependent variable in some way that you
think makes substantive sense. Explain your reasoning for your choices of the scores.
insert written answer here

# insert code here
(c) Using stargazer, display these two models (with their relevant goodness-of-fit statistics) side-byside. Compare the two estimates in a few sentences.
insert written answer here
# insert code here
(d) Consider a hypothetical respondent who is a 45-year old white person with a 4-year college degree
who lives in the state of Minnesota:
i. What does the ordered probit model say P r(strongly disapprove of Biden \ ) is for this person?
insert written answer here
# insert code here
ii. The OLS model doesn’t say what P r(strongly disapprove of Biden \ ) is for this person. What
does it say instead?
insert written answer here
# insert code here
(e) (Harder.) Using the plotting feature of margins, construct a graph that uses the estimates from
the ordered probit model to plot strong dissapproval of Biden by race and education.
# insert code here

2. The 2021 CES (ces.hw10.dta) includes four items designed to assess respondents’ support for various
environmental policies and laws with variable numbers CC21_324a, CC21_324b, CC21_324c, and
CC21_324d. You will use responses to these four variables to explore the properties of count regression
models vis-a-vis OLS and ordered probit.
(a) Create a new variable enviro_sum ∈ {0, 1, 2, 3, 4} indicating the number of policies and laws
supported by the respondent.
# insert code here
(b) Construct a histogram of enviro_sum.
# insert code here
(c) Treating enviro_sum as a count variable, regress it on the variables gender4, race, hispanic,
and the ideology variable cc21_330a, treating ideology as a categorical regressor. Run this two
ways, first using Poisson regression and then using negative binomial regression.
# insert code here
(d) Now treat enviro_sum as an ordered variable. Perform the proper regression with the same set of
regressors.
# insert code here
(e) Finally, treat enviro_sum as a continuous, interval-level variable. Perform the proper regression
with the same set of regressors.
# insert code here
(f) Put the regression coefficients and relevant goodness-of-fit statistics from these four models into a
table using stargazer.
# insert code here

i. What can be said about the ceteris paribus relationships between the predictors and
enviro_sum by looking at the signs on the coefficients and their statistical significance?
insert written answer here
ii. Are there any important differences of note among the four estimated models?
insert written answer here
iii. According to the test for overdispersion, is Poisson or negative binomial regression more
appropriate if we treat enviro_sum as a count variable?
insert written answer here
(g) Now use margins to construct a table displaying the following estimated quantities of interest,
holding all other variables at their actual values. Calculate these margins for each of the four
models you have estimated:
• The estimated difference in enviro_sum between...
– ...men and women
– ...white and Black Americans
– ...those who are “very conservative” and those who are “very liberal”
# insert code here

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