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 references, 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 [email protected]. Please type the following pledge at the beginning of your narrative 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 narrative 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: Murrah_midterm.R Murrah_midterm.docx Or Murrah_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 refer to the R code script. 1 [email protected] I. (100 points) Import the morrison2.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 related 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 instructor 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 instructor 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. b. Generate a concise correlation 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 corresponding 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 brief 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 Rubric (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 Correlations 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. Correlation 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 Rubric (2)
Answered Same DayApr 14, 2022

Answer To: Optional Midterm Project Optional Midterm Project Directions: 1. You will have almost one weeks to...

Atreye answered on Apr 15 2022
85 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 :1.4
38
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 correlation table, it is obtained that the correlation between clarity and insteval variable is 0.861 which implies there is very strong positive correlation between clarity and insteval. The correlation between insteval and knowledg is 0.282 which implies there is poor positive association between these two variables. The correlation between clarity and knowledge is 0.057 which implies there is very poor positive correlation 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. Error 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 error: 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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