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STAT 3032辅导、R设计讲解、辅导Canvas留学生、讲解R编程设计讲解留学生Processing|讲解留学生Prolog

Please do not share this handout outside the class.
STAT 3032 Homework4 Instruction (Fall 2019)
Due Thursday, Oct 17 @ 11:59pm in Canvas
20 points in total
Please show your work on each problem for full credit. A correct answer, unsupported by the
necessary explanation, R code or output will receive very little if any credit. Your work needs
to be organized in a reasonably neat and coherent way, and submitted as a pdf file on
Canvas.
You are welcome to discuss with your classmates, but you must write up your homework
individually!
Problem 1
This problem will use the RateMyProfeesor dataset we saw in the lectures! Download the data
file RateprofThree.csv from Canvas. Each observation is a professor. The variables are:
quality the quality score of the professor on a scale of 1 to 5, with 1 being the worst and
5 being the best.
gender the gender of the professor. The possible values are “female” and “male”.
pepper if the professor is considered “hot” (i.e. physically attractive). The possible values
are “yes” and “no”.
(a) Import the dataset into R. Among the professors in the sample, how many of them are
male? How many professors received “yes” for their pepper value? Hint: use
summary(dataName)
(b) Fit a model that uses pepper to predict quality. Provide the model summary. What
percentage of the variability in quality is explained by pepper?
Note: You may notice that the percentage is low. We are using Problem 2 to practice
using dummy variable, not to find a good model to predict quality.
(c) What is the base level of pepper (the level that corresponds to 0 in the dummy
variable)?
Please do not share this handout outside the class.
(d) Interpret the estimated slope of the model in Part (b) in the context.
(e) Interpret the estimated intercept of the model in Part (b) in the context.
(f) Now fit a model using both gender and pepper to predict quality. Provide the
summary of the model. Write down the fitted model. Please pay attention to the notation.
(g) What is the average of the quality score for male professors with pepper equal to “no”?
Hint: For these professors, what is the value of pepperyes? What is the value of
gendermale?
(h) Please order the following four groups according to their average quality score (from the
highest to the lowest).
-Group A: male professors with pepper equal to “yes”
-Group B: female professors with pepper equal to “yes”
-Group C: male professors with pepper equal to “no”
-Group D: female professors with pepper equal to “no”
Problem 2
This problem is adapted from Question 1 in Section 3.4 (pg. 103) in the textbook A Modern
Approach to Regression with R.
The data file airfares.txt on the book web site gives the one-way airfare (in US dollars)
and distance (in miles) from city A to 17 other cities in the US. Interest centers on modeling
airfare as a function of distance.
Please do not share this handout outside the class.
(a) Read in the dataset from a website (see url below) and fit the model that uses
Distance to predict Fare.Provide the summary of your model output.
The url for the data is: http://gattonweb.uky.edu/sheather/book/docs/datasets/airfares.txt
Hint: Use the R function read.table( )
(b) Create a scatterplot of Fare (y-axis) vs. Distance (x-axis) with the fitted regression
model. Color this fitted model red. Hint: Use the R functions plot(y~x, data =
dataName) and abline(modelName, col = ‘red’)
(c) Create a plot where the x-axis is Distance and the y-axis is the standardized residuals
from the model. Include two horizontal lines at -2 and 2 to mark the boundaries of
outliers. Hint: use the R functions rstandard(modName) ,
plot(rstandard(modName)~ dataName$x), and abline(h=2).
(d) Remove the two outliers found in Part (c) in the dataset. Note that one of the outliers has
a standardized residual just below -2. In real life, it is up to you to determine if you want
to remove this borderline case. Here, we will remove it. Fit a polynomial regression
model of order 2. Provide the output of your fitted model.
(e) Write down the fitted model equation from Part (d). Pay attention to the notation.
(f) If we conduct the test of H vs. , where is the slope of . 0
: β2 = 0 HA
: β2 =/ 0 β2 Distance
What is the test statistic value based on this sample? What distribution does the test
statistic follow under the null hypothesis? What is the p value?

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