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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。

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``````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 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 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,
• “some college”=14,
• “2-year”=14,
• “4-year”=16, and
# 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 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 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 code here
ii. The OLS model doesn’t say what P r(strongly disapprove of Biden \ ) is for this person. What
# 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``````

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