Link to course syllabus as a word document - K300 Sorge 25824 FINAL.docx

COURSE

Semester and year:  Fall 2026

Number/Title:           SPEA-K 300, Statistical Techniques

Class number:         25824

Day and Time:         M/W, 1:30 p.m. – 2:45 p.m.

(In person)

Location:                 BS 3014

 

INSTRUCTOR

 

Instructor:                Brandon Sorge, Ph.D.

Office hours:            M/W, 12:30 p.m.–1:15 p.m. and by appointment

Location:                 BS 4150

Telephone:               (317) 274-7306

E-mail address:        bsorge@iu.edu

 

COURSE DESCRIPTION

Official course description

Prerequisite: MATH 11000/11100 or equivalent. Nature of statistical data. Ordering and manipulation of data. Measures of central tendency and dispersion. Elementary probability. Concepts of statistical inference decision: estimation and hypothesis testing. Special topics discussed may include regression and correlation, analysis of variance, nonparametric methods. Credit not given for both K300 and either ECON-E 270, PSY-B 305, SOC-R 359, STAT 30100

 

Instructor description

K300 is an introductory survey of statistical reasoning and analysis. The course has been redesigned to be more conceptual than computational, but ordered computation is still common.  However, primary emphasis will be placed on interpreting and understanding results as opposed to accuracy of answers. 

Topics will include ordering and summarizing data, measures of central tendency and dispersion, elementary probability, and concepts of statistical inference decision (estimation and hypothesis testing). Special topics discussed will include regression and correlation.    

This course is designed to ensure your success by providing several readily available resources.  First, your instructor places an emphasis on in-class examples and building conceptual understanding. The textbook uses online homework and other online assistance tools. 

Second, PowerPoint lectures, summary slides, topic-specific video clips, recorded lectures, and online testing material are available on the ALEKS website.  Students must be registered in ALEKS to avail themselves of these resources.  Students are required to complete chapter by chapter homework in this system. 

Additional classroom assistance is available from the MAC-Stat lab.  The Mac-Stat lab has on campus tutors specifically dedicated to K300 students.  You can visit the MAC-Stat lab as often as you like, and there is no extra charge for this service.

REQUIRED COURSE MATERIALS

• Elementary Statistics by William Navidi and Barry Monk (Evergreen Release, McGraw Hill, 2025), bundled with Aleks software.
  • Note- The textbook and software are available in Canvas. No additional purchase necessary.
• Journal articles linked through the class Canvas site

 

COURSE LEARNING OUTCOMES

Upon successful completion of this course, a student should be able to:

• demonstrate a firm understanding of fundamental statistical terms,
• be able to derive univariate measures of central tendency and variation,
• display and interpret data graphically,
• understand the standard normal curve and its basic applications
• have a basic understanding of inferential statistics
• generate results both manually and with the aid of a computer.

PROFILES OF LEARNING FOR UNDERGRADUATE SUCCESS

IU Indianapolis prepares all students to communicate, innovate, and engage local and global communities to solve the problems of the 21st century. The skills needed to do this work are grouped into the four learning profiles set out below. IU Indianapolis students have multiple opportunities to learn and enhance their abilities in each profile.

Communicator

An IU Indianapolis communicator can evaluate information, listen actively, build relationships and convey ideas effectively. IU Indianapolis students learn to convey ideas effectively and ethically in oral, written, and visual forms across public, private, interpersonal, and team settings, using face-to-face and mediated channels. Communicators are mindful of themselves and others, listen, observe, and read thoughtfully, ask questions, evaluate information critically, create messages that demonstrate awareness of diverse audiences, and collaborate with others and across cultures to build relationships. 

Problem solver 

An IU Indianapolis problem solver thinks critically, collaborates, analyzes, synthesizes and evaluates information and perseveres in the face of challenge. IU Indianapolis students work individually and with others to collect, analyze, evaluate, and synthesize information to implement innovative solutions to challenging local and global problems.

Innovator 

An IU Indianapolis innovator investigates, creates and designs, confronts challenges and makes decisions. IU Indianapolis students learn to build on experiences and disciplinary expertise to approach new situations and circumstances in original ways, are willing to take risks with ideas, and pose solutions. They learn to create a plan to achieve their goals and carry out that plan to its completion. Innovators use their knowledge and skills to address complex problems. 

Community contributor 

An IU Indianapolis community contributor builds community, respectfully engages cultures, behaves ethically and anticipates consequences. IU Indianapolis students learn to be responsible, self-aware, civically engaged and to look outward to understand the needs of society and the environment.

 IU INDIANAPOLIS POLICIES AND CODES

Students are expected to adhere to all campus-wide policies governing the conduct of courses at IU Indianapolis, including maintaining academic and professional honesty and integrity, being responsible for their behavior, respecting the rights and dignity of others both within and outside of the university community, and others. These policies can be found at http://studentcode.iu.edu/about/index.html.

INSTRUCTOR’S CLASSROOM POLICIES

Students have traditionally developed an increased level of understanding from real-time problem-solving, discussion and interpretation in the classroom setting.  As much as possible, the instructor attempts to use this method of instruction.  Class instruction time will primarily be used to demonstrate problem-solving and provide practical application examples.  For this instruction method to be effective, students must prepare in advance by studying the relevant material and developing a reasonable understanding.  Without adequate preparation, students commonly find problem-solving and practical application examples are not as useful.  To prepare students for in-person problem solving, the instructor requests students review material ahead of time.  

Because the K300 instructional material is progressive, new material is built upon an understanding of previous material, and thus, understanding each chapter is essential (generally) before proceeding to the next chapter.  To this end, the completion of homework is essential.  Unless you have extenuating circumstances, homework due dates will not be changed.  Homework due dates are provided below under “Course Schedule.”  You will only receive credit for the portion of homework you have completed by the due date.

 Extenuating circumstances include a doctor-verified illness, military service, family loss, family emergencies, or any number of life situations that reasonably interfere with or prevent you from completing your homework. You may be required to provide proof of illness or family emergency, etc.  Extensions in these cases will be decided on a case-by-case basis and in advance of the deadline whenever possible.

Student Awareness of Grades

Students should take individual responsibility for their grades.  Please note, ALEKS homework averages may not address missed homework.  ALEKS may ignore your missed homework and use it when averaging.  At the end of the semester, your instructor will average in any missed homework as a “0”.  

Within the prescribed due dates, you have unlimited attempts to take homework, but only one attempt at each exam.  Your instructor does not care if you use notes, or technology (Excel) during homework or the exams.  Your exams will be in the classroom, and you must be in attendance to take the exam (unless other accommodations have been made before hand).    

You should be prepared for your exam.  Your instructor will perform a “review” before each exam which will include information on the general content of the exam and solutions to problems like those in the exam.  The final is not comprehensive and will address only material covered in class after the second exam.      

Behavior in the classroom

In an ideal world, it would be great if every student came to class with enthusiasm, curiosity, and a willingness to challenge themselves.  I will do what I can to help inspire this attitude. A positive attitude from everyone goes a long way in helping nurture a learning environment where you succeed.  You will get out of this class what you put into it. If you are actively engaged, paying attention, and participating, you will set yourself up for success in this course. Always be respectful of others and create a welcoming space for your peers. Your instructor will do the same.

Class attendance & participation

Your instructor understands if an unusual event such as illness or family emergencies occurs. Excusable absences are addressed under “Instructor Classroom Policies” above.  You must contact your instructor in advance.  Communication is extremely important!

Please attend each class meeting and come on time, having prepared any required assignments and ready to participate in discussions with your instructor and peers.  Your instructor will do the same.      

Due dates and late work

Please be aware of online opening and closing dates and times for your homework.   You must complete your homework to receive credit, and homework is counted as 41% of your grade.  Opening homework after it closes will only be considered in cases involving extenuating circumstances as addressed above under “Instructor Classroom Policies.”

Communication

Please use the Canvas Inbox messages tool as your primary communication method for questions related to this course. I check my email throughout my regular work hours (M-F 7 a.m. – 4 p.m.) and on most Sunday evenings prior to assignment deadlines. My office is in the Business/SPEA (BS) building (4150). My regular office hours are Mondays and Wednesdays before class but I am also available online or in-person by appointment. I will reply to all email messages within 48 hours or sooner. If you have not heard from me in that timeframe, please resend the email.

Technology use

Our meetings are highly interactive. If possible, please bring a computer to class with you. This will allow you to be actively engaged.

 ASSESSMENT AND GRADING

Grading scale

 

Grade	Letter Grade
97-100	A+
93-96.9	A
90-92.9	A-
87-89.9	B+
83-86.9	B
80-82.9	B-
77-79.9	C+
73-76.9	C
70-72.9	C-
67-69.9	D+
63-66.9	D
60-62.9	D-
0-59.9	F

 

Summary of course grading

Course Components	Points (percent)
Attendance (3 x 30)	90 (9%)
Homework assignments (10 x 41 points)	410 (41 %)
Exams (4 x 125 points)	500 (50 %)
Total	1000

 

Course components

 Attendance

Attendance is mandatory in this class. Attendance will be taken via roll call during non-examination class. Each class is worth 3 points for a total of 90 points (9%).

 Quizzes/Exams

There will be four non-cumulative examinations, each worth 125 points.  Collectively they represent 50% of the final grade.   

Assignments

All homework assignments are due before class (at 11:59 pm the night before). The assignments should be submitted electronically, via Canvas, on or before the due date. Each assignment is worth 41 points. Completing these assignments is extremely important to you overall success in the course. You can re-do homework assignments to receive full credit within week of the class you received your graded homework! You may use the class AI agent to support you in completing your assignments. AI should not be used to do the work!!!

COURSE SCHEDULE

 

Week	Class Topic	Assignments
Week 1 8/24 & 8/26	Introduction to the course
	Defining and Collecting Data
Week 2 8/31 & 9/2	Organizing and Visualizing Data
	Numerical Descriptive Measures
Week 3 9/7 & 9/9	Labor day – no class (9/7)	Assignment 1 Due 9/6
	Numerical Descriptive Measures
Week 4 9/14 & 9/16	9/14 – Exam 1	Assignment 2 Due 9/13
	Basic Probability
Week 5 9/21 & 9/23	Basic Probability	Assignment 3 Due 9/23
	Discrete Probability
Week 6 9/28 & 9/30	Continuous (Normal) Probability Distribution	Assignment 4 Due 9/30
Week 7 10/5 & 10/7	Sampling Distributions	Assignment 5 Due 10/7
Week 8 10/12 & 10/14	Sampling Distributions (online class materials)
Week 9 10/19 & 10/21	10/19 – No Class Fall Break 10/21 – Exam 2 Review	Assignment 6 Due 10/21
Week 10 10/26 & 10/28	10/26 – Exam 2
	Confidence Intervals
Week 11 11/2 & 11/4	Confidence Intervals
Week 12 11/9 & 11/11	Hypothesis Testing – One Sample	Assignment 7 Due 11/8
Week 13 11/16 & 11/18	Hypothesis Testing – Two Sample	Assignment 8 Due 11/15
Week 14 11/23	Exam 3 - Prep	Assignment 9 Due 11/22
	Thanksgiving
Week 15 11/30 & 12/2	Exam 3
	Correlation/Linear Regression
Week 16 12/7, 12/9, 12/14	Linear Regression Exam 4 - Prep	Assignment 10 Due 12/9
Finals	Exam 4- Wednesday, Dec. 16  1-2 p.m.

ADDITIONAL INFORMATION

 

Administrative withdrawal-

A basic requirement of this course is you will participate in class and conscientiously complete writing and reading assignments. If you miss more than half the class meetings within the first 25% of the semester without contacting me, you may be administratively withdrawn from this course. This class meets twice each week; thus if you miss more than four class meetings in the first four weeks, you may be withdrawn. Administrative withdrawal may have academic, financial, and financial aid implications. Administrative withdrawal will take place after the full refund period, and if you are administratively withdrawn from the course you will not be eligible for a tuition refund. If you have questions about the administrative withdrawal policy at any point during the semester, please contact me.

Copyright

Materials in this course, including lectures, notes, handouts, exams, and similar materials created by the instructor, are protected by U.S. copyright law. Materials are used in an educational context for personal use and should not be reproduced, distributed, or sold in print or digitally outside the course without permission. Students who are involved in unauthorized distribution of copyrighted material may be in violation of the Code of Student Rights, Responsibilities, & Conduct.

Accessible Educational Services (AES)

AES is the IU Indianapolis office dedicated to working with students with documented disabilities to ensure that these students receive the appropriate accommodations so they have an equal opportunity to be successful at higher education. The AES Office is located in Joseph T. Taylor Hall, Room 100, and can be contacted by phone at (317) 274-3241 or email at aes@iu.edu. For more information, visit the Accessible Educational Services website.

O’Neill’s make-up exam policy

Under extenuating circumstances noted in this syllabus, a make-up exam may be given with instructor approval. In this situation, a student would complete the make-up exam during the official proctoring time, which is Fridays from 9 a.m.–noon in room BS 4087. Students will need to avail their schedules to this timeframe in order to complete a make-up exam.

Artificial Intelligence/ChatGPT

Specified Use of Generative AI

You may use the Artificial Intelligence (AI) agent in Gemini provided by the instructor. Class assignments are designed to help you develop your research, analytics, and writing skills. Artificial Intelligence (AI) programs are powerful and useful tools for performing work and generating text.  Products created using such tools, however, do not represent your original work. Further, using other AI may undermine the learning objectives associated with the assignment and the course. The Generative AI GEM provided by the instructor may be used to complete any assignment or assessment for this class only if the use of such software is specifically and expressly permitted by the instructor in the associated assignment description. Unless expressly permitted, use of other AI to complete any portion of an assignment or assessment will be considered a violation of academic integrity policies.

Intellectual Diversity

This course includes assigned readings and other materials that address broadly accepted concepts, theories, principles, and methods central to the topics examined in this course.  These materials provide a foundation of knowledge that is necessary to both understanding and further study of the course topics.  With this foundational knowledge, students will be better prepared to evaluate new, complex, and emerging questions in the field.  As knowledge evolves, new ideas come forward for consideration.   A student who identifies materials that they believe are relevant and will enhance understanding of course topics is invited to email the work to the instructor for consideration of inclusion in the course discussion.