
Contact Information

Course Description

Course Outcomes

Prerequisites

Required Text(s) and Materials

Assessment Method(s) and Evaluation

Grading

Attendance Policy

Academic Integrity

Course Expectations

Tentative Detailed Course Content and Recommended Readings

Student Opinion of Instruction

Title IX Statement

Access Statement

Suplemental Information Statement
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In contrast to the most widely used statistical package programs, R, which is free of charge and open source, has an active structure that is constantly evolving with the contributions of researchers from around the world. Thus, R is an ideal tool while analysing the main statistical concepts of descriptive statistics and inferential statistics. From this point of view, the main aim of this course is to provide students with adequate knowledge in both programming in R software and theoretical statistical concepts. Hence, students will be able to use R in their statistical analysis related to their field of research.
Upon the completion of this course, the student will be able to:
 comprehend knowledge in working with R software for statistical analysis.
 interpret the results of the research according to the statistical methods applied to data.
 utilize the R for describing and analyzing the quantitative data.
 understand and apply mathematical concepts and reasoning, analyze and interpret various types of data.
 have the working ability both individually or as a group member.
 make the accurate presentation of the results in the framework of science and ethics.
 conduct a research related to realworld context.
These course outcomes will be assessed by 5 short exams, 1 midterm exam, 1 termproject and 1 final exam. Details are stated in assessment methods and evaluation part of the syllabus.
AVL Learning Outcomes:
Upon successful completion of the program, students will be able to:
 have adequate knowledge in aviation, logistics and supply chain and computing tools to make decisions in new or unpredictable environments of aviation logistics.(knowledge)
 formulate and solve a complex aviation logistics problems involving human, material, machinery, money, information, time and energy elements; analyze and design it under realistic constraints and conditions. (formulate and solve analyse and design)
 use information technologies effectively with the knowledge of aviation logistics. (use of ıt).
 design and conduct experiments, gather data, analyze and interpret results for investigating complex aviation logistics research questions. (analytical skill).
 work efficiently in interdisciplinary and multidisciplinary teams by collaborating effectively, in addition to an individual effective working ability.(teamwork)
 enhance critical thinking skill by integrating relevant information, decisionmaking techniques, and concepts through the interdisciplinary aviation logistics area. (critical thinking).
 communicate effectively, using information technology and oral and written skills to enhance decision making process through better communication.
 be aware of the importance of lifelong learning.(LLL)
 make ethical and legal business decisions by considering cultural differences.(ethics)
 recognize the importance of entrepreneurship, innovation and sustainable development.(sustainability)
 have knowledge of the global and social effects of aviation logistics on health environment, and safety, and contemporary issues of the century reflected into the field of aviation logistics.
At least one undergraduate or graduate level statistics course or equivalent.
 James, G., Witten, D., Hastie, T. and Tibshirani, R. (2013). An Introduction to Statistical Learning: with Applications in R. Springer Verlag. (An oldest version will be fine as well.)
 Ming Hui, E.G. (2019). Learning R for Applied Statistics: With Data Visualizations, Regressions, and Statistics. Apress Publishing.
 Newbold, P., Carlson W.L. and Thorne,B.M. (2013), Statistics for Business and Economics, 8. Edition, Pearson. (You can use your old statistics book instead of that book.)
 Some supplementary readings from online sources will be added when needed.
 You will need access to R. The installation of the R will be explained in the first week of the class. Anyone, who would like to download them before the class, can use the following webpages: This page provides the R installation: https://www.rproject.org/. After installing R, you need also to install R Studio from this page: https://www.rstudio.com/.
 Academic publications might be assigned and discussed throughout the semester.
Grading will be based upon 10 short exams, 1 midterm exam, 1 termproject and 1 final exam.
Midterm and Final Exam 
The midterm and final exams are 20% and 40% final average, respectively: This assignments may be comprehensive in material and consist of a mixture of multiple choice questions and short essays or problems and lots of data analysis. Questions in the exams are designed to make sure that students comprehend the topics lectured and the analyses made with using R. These exams will cover lecture notes, lab exercises and any assigned readings. The types of questions on the exams will be similar to those asked in the study questions, lab exercises and the lecture materials covered during the semester. 


Term Project: 
This is 20% of the final average:
This assignment is aimed to improve the ability of students in using R and interpret results of the research properly.


Short exams: 
This is 20% of the final average: Students are required to complete 5 short exams throughout the course by means of the portal. The short exams includes 5 short work questions covering a specific chapters. These assignments cannot be made up after the deadline of each particular short exam. There will be no makeup exam for short exams. 

Policy on Make ups: Makeup examinations will only be administered to students with excused absences. Excused absences include death in the immediate family, University sponsored trips or critical illness. Verification is required and permission to miss an examination must be secured PRIOR TO the scheduled examination time. If this condition is not met, a zero will be given for the missed exam. 
Grading Scale  

Grade  Quality Points 
A = Excellent  90 – 100% 
B = Good  80 – 89% 
C = Satisfactory  70 – 79% 
D = Passing  60 – 69% 
F = Failing  below 60% 
Incompletes (I) demonstrate that a student was doing sufficient work at the end of a semester period but, for reasons beyond the control of the student, was unable to complete all requirements of the course in the related semester. The grade I obliges student to complete all course requirements within a time period that is specified by the instructor. This time period can’t exceed one academic calendar year from the end of the semester in that the grade I is assigned. The students has to arrange with the course instructor in order to complete all course requirements in a specified time period. If all course requirements are not completed by the students in a specified time period, I is changed to the grade F, unless the instructor has assigned a different grade.
WithdrawalsWStudents may withdraw from courses following the drop and add period until midterm by completing the withdrawal process on the portal. A grade of "W" will appear in the student's official records if the student has withdrawn according to the SFU’s Withdrawal Policy. (Please see the SFU’s Withdrawal Policy for details.)
Participation and consistent attendance is essential for acceptable performance in the course. The students are expected to be present each class period except when special hardships occur, e.g. death in the immediate family, University
sponsored trips or critical illness.
Regulations for attendance of Suje Florida University will be applied for this class.
Academic integrity is the responsibility of all Suje Florida University faculty and students. Cheating and plagiarism are not tolerated and will result in a failing grade, if the student is found guilty of cheating. All students are expected to do the work on their own and to accept standards of academic ethics.
 As a portal course, it requires extensive work be done by students using the Internet. You must familiarize yourself with your portal account.
 Supplemental materials, including lecture notes will be posted on portal. You are responsible to check your account regularly for materials posted.
 You should expect to practice a lot with data. And you should practice enough so you can repeat tasks quickly during the exam.
 Please ask questions. If you do not get an opportunity to ask your question during lecture or the lab time, feel free to contact me via email.
 Check your email often.
 Communications with the instructor should be via portal or email. Email is preferred.
 Changes may be necessary in the syllabus. Students will be informed about the changes in the syllabus.
 Students are responsible for any new material or announcements missed due to the absence.
Week  Topic  Recommended Reading(s) 

1 
Lab: Installation of R and R Studio 

2 
Descriptive and Inferential Statistics, Measurement Scales, Visualizing data, frequency histograms Lab: What is R?, RStudio: The IDE for R; Writing Scripts in R 

3 
Descriptive Statistics: Measures for central tendency: mean, median, mode. Lab: Basic Synthax: Writing in R Console, Using the Code Editor, Vectors, Lists, Matrix, Dataframe, Loops, Functions; Lab: Reading and writing data files from and to SPSS, Excel and Csv; Calculating the mean, median and mode 

4 
Descriptive Statistics: Measures of variability: range, quartiles, standard deviation, skewness and variance. Lab: Reading and writing data files from and to SPSS, Excel and Csv; Basic Data Processing; Type of Data; Calculating the range, quartiles, standard deviation, skewness. 

5 
Data Visualization: What are Data Visualization?; Bar Chart and Histogram, Line Chart and Pie Chart, Scatter Plot and Box Plot, Scatterplot Matrix Lab: Using ggplot2, Drawing graphs by using ggplot2 

6 
Elements of Chance: Probability Methods: Random Experiment, Outcomes, and Events, Probability Rules, Bivariate Probabilities, Bayes’ Theorem 

7 
Discrete Probability Distributions: Probability Distributions for Discrete Random Variables, Properties of Discrete Random Variables, Binomial Distribution, Poisson Distribution, Hypergeometric Distribution, Jointly Distributed Discrete Random Variables Lab: Exercises with R 

8 
MIDTERM EXAM 

9 
Continuous Probability Distributions: Continuous Random Variables, Expectations for Continuous Random Variables, The Normal Distribution, The Exponential Distribution, Jointly Distributed Continuous Random Variables Lab: Exercises with R 

10 
Distributions of Sample Statistics: Simple Random Sampling, Sampling Distributions of Sample Means, Central Limit Theorem, Sampling Distributions of Sample Proportions, Sampling Distributions of Sample Variances, Stratified Sampling, Cluster Sampling, Correlations and Covariance Lab: Lab exercise on sampling, using the sample() function, installing necessary packages like dplyr and conducting the related analysis. 

11 
Confidence Interval Estimation: Point Estimators, Confidence Interval Estimation for the Mean of a Normal Distribution: Population Variance Known and Population Variance Unknown, Confidence Interval Estimation for Population Proportion, Confidence Interval Estimation for the Variance of a Normal Distribution and Further Topics in Confidence Intervals Lab: Conducting confidence intervals by R and interpreting them. 

12 
Hypothesis Testing and pvalue: ttest, type of ttest, Assumptions of ttest, Type I and Type II error, One Sample ttest, two sample dependent ttest Lab: Hypothesis testing by using R and interpreting the results 

13 
Simple Linear Regression: OLS estimator, slope parameter, intercept parameter, interpretation of regression results. SST, SSE, SSR, goodness of fit measures, changes in measurement. Lab: Using R and its library in order to perform simple Linear Regression 

14 
Multiple Linear Regression: Hold all other variables constant, assumptions for OLS to be BLUE, goodness of fit measures, interpretation of point estimates, hypothesis test for overall significance, individual and joint significance tests, Chow test, predicted values, confidence interval around prediction. Lab: Model estimation and prediction with R. 

15 
Analysis of Variance: Comparison of Several Population Means, OneWay Analysis of Variance, TwoWay Analysis of Variance Lab: Hypothesis, Assumptions, Between Group Variability, Within Group Variability, One way ANOVA, Two way ANOVA, MANOVA with R 

16 
FINAL EXAM 
At the end of the term, all students will be expected to complete an online Student Opinion of Instruction survey that will be available on portal. Students will receive an email notification through their Palm Beach University email address when the SOI is available. SOI responses are anonymous to instructors/administrators. Instructors will be able to view only a summary of all responses two weeks after they have submitted final grades.
Suje Florida University is committed to creating a diverse and inclusive work and learning environment free from discrimination and harassment. Discrimination on the basis of race, color, ethnicity, national origin, sex (including pregnancy status, sexual harassment and sexual violence), sexual orientation, gender identity, religion, age, national origin, disability, genetic information, or veteran status, in the Suje Florida University's programs and activities is prohibited as required by applicable laws and regulations such as Title IX. The individual designated with responsibility for coordination of compliance efforts and receipt of inquiries concerning nondiscrimination policies is the University's Title IX Coordinator.
Students with disabilities who are experiencing barriers in this course may contact the Access Office for assistance in determining and implementing reasonable accommodations.
Term Project
 In order to complete this course with a passing grade you will have to submit a term project that includes a detailed data analysis.
 You will need to use R to present data, estimate models and make a prediction.
 Students will be organized into groups of maximum three students each (Some group may have less depending upon the enrollment).
 The term project topics will be assigned to the groups by the third week of semester or each group has the option to determine their own topic which must be approved by the instructor. The topics should be in the fields of aviaton logistics, business analytics or data science. (Due to the fact that this course is offered to all master programs in Suje Florida University, the topic can differ according to the master program that students are enrolled in the University.)
 There are various deadlines for the term paper that you need to meet. These are presented and discussed below.
 Timeline of your project will be as follows:
Task Deadline How Group Creation 2.week email Pick a topic (or get approval) 3.week email Data 5.week email Progress Report 1 10.week email Progress Report 2 12.week email First draft 14.week email Final term paper 16.week portal  You need to send me your final decision on the topic of your term paper and send me the data that you’ll be using by the 5. Week of the semester. (This has to be done in writing). Not meeting this deadline will result in lowering of the weight of the term paper by 1 % point for each day by which you are late
 You are also expected to give me an update on the progress of your term paper in 10.week and 12.week.
 A first draft may be submitted to me on the 14.week. This, however, is optional and its only purpose is to give you feedback about potential improvements (if needed). No feedback will be provided if the draft is submitted after this week.
 The term paper will be due on the 16. week. (Not meeting this deadline will result in a further lowering of the weight of the term paper by 2 percentage point for each day by which you are late (18% for one day, 16% for two days, etc.).