Optional Midterm Project Optional Midterm Project Directions: 1. You will have almost one weeks to complete this examination. 2. You may use your notes, homework examples, code files from class, your...

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Optional Midterm Project
Optional Midterm Project
Directions:
1. You will have almost one weeks to complete this examination.
2. You may use your notes, homework examples, code files from class, your textbook, other statistical
eferences, and your personal knowledge. You may NOT consult other students or faculty members.
3. If you have questions or need clarification, contact me by email at XXXXXXXXXX.
Please type the following pledge at the beginning of your na
ative document and type your name after it as
your signature if you comply with this pledge. If you do not include this pledge, your exam may be returned
to you ungraded.
On my honor as a student, I have neither given nor received aid on this examination outside
the scope of the directions.
To Complete this assignment you would submit one or two on documents on Canvas.
Either:
1. A R code script with the code used to prepare variables, tables, and graphs. (.R)
2. A text or word processing document with the na
ative and any relevant tables and graphs (e.g. .docx,
.pdf, .html). This document should stand alone, meaning that all relevant statistics and other information
should be included in tables or graphs within this document.
Or:
1. An html file with the R code and text write up in the same document. This document should include
all the content mentioned above.
DO NOT TURN IN A Lname_midterm.Rmd FILE
For each document use the following naming convention,
Lname_midterm.suffix
where Lname is your capitalized last name, and suffix is the relevant suffix (.R, .html, .docx). For example,
if I were turning in homework, I could include the following files:
Mu
ah_midterm.R
Mu
ah_midterm.docx
O
Mu
ah_midterm.html
NOTE: the text or Word document should be self-contained. That means all the output
needed for the arguments made must also be in this document. It is NOT sufficient to refe
to the R code script.
1
XXXXXXXXXX
I. (100 points)
Import the mo
ison2.csv file for this question.
This study is aimed at understanding which characteristics of a course instructor are important to students.
Using course instructor evaluations we sought to determine if clarity of presentation and instructor content
knowledge are important in predicting how well students’ rate the instructor. The data contain information
elated to student evaluations of instructors of Master’s of Business (MBA) courses. The goal is to estimate
the effect of clarity and knowledge as predictors of the overall instructor evaluation. The outcome is instructo
course evaluation of an MBA course, with two predictors being clarity and knowledge.
The definition of the variables in the data follow.
Variable name Variable Label
insteval Course evaluation of instructo
clarity Clarity of presentations
knowledg Instructors content knowledge
Guiding questions:
The following questions should guide your write up. BUT THE WRITE UP SHOULD BE FORMAT-
TED AS A VERY ABBREVIATED MANUSCRIPT. It should look like the examples given in class.
Do not include the question followed by the answers.
a. Generate a concise table with descriptive statistics for the important variables in the data set.
. Generate a concise co
elation table for the important variables in the data set.
c. Determine the regression model (equation) in raw score form. Include the estimated equation for each
and provide your interpretation for the meaning of the regression coefficients.
d. Evaluate the overall fit of the model using the multiple R2 and the co
esponding statistical test, and
explain the meaning of this test. Explain the R2 as an effect size measure.
e. Determine if both predictors are meaningful in the regression model. Be sure to discuss effect size and
tests of the regression coefficients. Explain your results.
f. Check the data for assumptions, outliers, and influential cases. Write a
ief paragraph summarizing
what you found.
g. Interpret these results and how they are related to the original research question.
2
    Directions:
    I. (100 points)
    Guiding questions:

    Midterm Ru
ic (2)
    Criteria
    Ratings
    Pts
    This criterion is linked to a Learning OutcomeQ1.a Descriptives
        5 pts
Full Marks
    0 pts
No Marks
    5 pts
    This criterion is linked to a Learning OutcomeQ1.b Co
elations
        5 pts
Full Marks
    0 pts
No Marks
    5 pts
    This criterion is linked to a Learning OutcomeQ1.c Equations
        10 pts
Full Marks
    0 pts
No Marks
    10 pts
    This criterion is linked to a Learning OutcomeQ1.d R squared significance
        5 pts
Full Marks
    0 pts
No Marks
    5 pts
    This criterion is linked to a Learning OutcomeQ1.e predictors meaning
        10 pts
Full Marks
    0 pts
No Marks
    10 pts
    This criterion is linked to a Learning OutcomeQ1.f Iterpret coefficients
        5 pts
Full Marks
    0 pts
No Marks
    5 pts
    This criterion is linked to a Learning OutcomeQ2.a.1. Descriptive statistics
        5 pts
Full Marks
    0 pts
No Marks
    5 pts
    This criterion is linked to a Learning OutcomeQ2.a.2. Co
elation Table
        5 pts
Full Marks
    0 pts
No Marks
    5 pts
    This criterion is linked to a Learning OutcomeQ2.b Regression Equation
        10 pts
Full Marks
    0 pts
No Marks
    10 pts
    This criterion is linked to a Learning OutcomeQ2b.2. Adequacy of equation
        10 pts
Full Marks
    0 pts
No Marks
    10 pts
    This criterion is linked to a Learning OutcomeQ2.c.1. Determine predictor meaningfulness.
        10 pts
Full Marks
    0 pts
No Marks
    10 pts
    This criterion is linked to a Learning OutcomeQ2.c.2. Describe results in APA style
        10 pts
Full Marks
    0 pts
No Marks
    10 pts
    This criterion is linked to a Learning OutcomeQ2.d. Interpret results
        10 pts
Full Marks
    0 pts
No Marks
    10 pts
    Total Points: 100
Midterm Ru
ic (2)
Answered Same DayApr 14, 2022

Solution

Atreye answered on Apr 15 2022
12 Votes
Solution a:
Code & Output:
summary(data)
insteval clarity knowledg
Min. :1.000 Min. :1.000 Min. :1.000
1st Qu.:2.000 1st Qu.:2.000 1st Qu.:1.000
Median :2.000 Median :3.000 Median :1.000
Mean :2.406 Mean :2.844 Mean :1.438
3rd Qu.:3.000 3rd Qu.:4.000 3rd Qu.:2.000
Max. :4.000 Max. :5.000 Max. :3.000
Interpretation:
The minimum and maximum for “insteval” variable is 1 and 4. The mean and median for this variable is obtained as 2.406 and 2 respectively. The first and third quarter for the variable is 2 and 3 respectively.
The minimum and maximum for “clarity” variable is 1 and 5. The mean and median for this variable is obtained as 2.844 and 3 respectively. The first and third quarter for the variable is 2 and 4 respectively.
The minimum and maximum for “knowledg” variable is 1 and 3. The mean and median for this variable is obtained as 1.438 and 1 respectively. The first and third quarter for the variable is 1 and 2 respectively.
Solution b:
Code & Output:
cor(data)
insteval clarity knowledg
insteval 1.0000000 0.86180913 0.28182052
clarity 0.8618091 1.00000000 0.05725983
knowledg 0.2818205 0.05725983 1.00000000
Interpretation:
From the co
elation table, it is obtained that the co
elation between clarity and insteval variable is 0.861 which implies there is very strong positive co
elation between clarity and insteval. The co
elation between insteval and knowledg is 0.282 which implies there is poor positive association between these two variables. The co
elation between clarity and knowledge is 0.057 which implies there is very poor positive co
elation between clarity and knowledge.
Solution c:
Code & Output:
fit=lm(insteval~.,data=data)
summary(fit)
Call:
lm(formula = insteval ~ ., data = data)
Residuals:
Min 1Q Median 3Q Max
-0.74654 -0.31129 0.00139 0.25346 0.70083
Coefficients:
Estimate Std. E
or t value Pr(>|t|)
(Intercept) 0.19391 0.23712 0.818 0.42016
clarity 0.62604 0.06184 10.123 5e-11 ***
knowledg 0.30055 0.10801 2.783 0.00938 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard e
or: 0.3716 on 29 degrees of freedom
Multiple R-squared: 0.7969,    Adjusted R-squared: 0.7829
F-statistic: 56.91 on 2 and 29 DF, p-value: 9.134e-11
Interpretation:
From the regression analysis, the regression equation for predicting is obtained as below:

From the equation the coefficient for clarity is obtained as 0.62604 and coefficient for knowledge is obtained for 0.30055 which implies for one unit change in clarity, keeping others constant, insteval will increase by 0.19391 + 0.62604 unit and one unit change in knowledge , keeping others constant, insteval will increase by 0.19391 + 0.30055...
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