MATH 1774 Statistics Syllabus 010 Spring 2026

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Credit Hours 4.00 Lecture Hours 4 Clinical/Lab Hours 0
Type of Credit
Baccalaureate/Transfer
CIP Code
27.0501
Course Meeting Time

M/W 1:00 – 2:40 PM – L366

Course Description

This course focuses on statistical reasoning and on solving problems using real-world data rather than on computational skills. Use of technology-based computations (such as graphing calculators with a statistical package, spreadsheets, or statistical computing software) is required with emphasis on interpretation and evaluation of statistical results. Topics include data collection processes (observational studies, experimental design, sampling techniques, bias), descriptive methods using quantitative and qualitative data, bivariate data, correlation, and least-squares regression, basic probability theory, probability distributions (normal distributions and normal curve, binomial distribution), chi-square tests, one-way analysis of variance, confidence intervals and hypothesis tests using p-values. Students cannot receive credit for both MATH 1774 and BSNS 2514. IAI: M1 902 Mathematics. IAI: BUS 901 Business.

Prerequisites

MATH 1424 or MATH 0985 with a grade of C or better, appropriate assessment score, or High School Transitional Math: quantitative literacy (QL) or STEM pathway - Must be completed prior to taking this course.

Course Alignment

IAI Number
M1-902
MATH-01
IAI Title
General Education Statistics
Statistics
General Education Outcomes

General Education Outcomes are the knowledge, skills, abilities, attitudes, and behaviors that students are expected to develop as a result of their overall experiences with any aspect of the college, including courses, programs, and student services, both inside and outside of the classroom. The General Education Outcomes specifically learned in this course are:

  1. Communication
  2. Critical Thinking
Explanation of Course Alignment

Course information

Subject Code: BSNS/MATH

Course Number: 2514/1774

Course Name: Business Statistics/Statistics

Course Description:

This course focuses on statistical reasoning and solving problems using real-world data. Students will use technology-based computations using a graphing calculator with a statistical package, spreadsheets, or statistical computing software. Emphasis is on interpretation and evaluation of statistical results. Topics include data collection processes (observational studies, experimental design, sampling techniques, bias), descriptive methods using quantitative and qualitative data, bivariate data, correlation, least-squares regression, basic probability theory, probability distributions (normal distributions and normal curve, binomial distribution), chi-square tests, one-way analysis of variance, and confidence intervals and hypothesis tests using p-values. Students cannot receive credit for both MATH 1774 and BSNS 2514. IAI: BUS 901 Business. IAI: M1 902 Mathematics.

Prerequisite Narrative:

Appropriate assessment score; MATH 1424 with a grade of C or better; MATH 0985 with a grade of C or better; or High School transitional math: STEM pathway - Must be completed prior to taking this course.

Lecture Hours: 4

Lab Hours: 0

Credit Hours Narrative: 4 Credits

IAI number: M1-902; BUS901

IAI title: General Education Statistics; Business Statistics

General Education Outcomes: Communication, Critical Thinking

Outcomes

Organize data using frequency distributions and graphs

Distinguish between the different types of studies and different types of random sampling

Describe (summarize) data using measures of central tendency and dispersion

Perform calculations associated with fundamental distributions and solve related application problems

Construct confidence intervals for means and proportions

Perform hypothesis tests for means and proportions using p-values.

Calculate correlation coefficient and check its significance

Estimate a regression equation and interpret its coefficients

Use technology to complete basic statistical analyses

Faculty Contact Information

Faculty Name
Wesley Davenport
Faculty Email
wdavenport@kcc.edu
Faculty Phone
#: 815.802.8769
Faculty Office Number
D329
Faculty Student Support Hours

Office Hours: M/W – 3:00 – 5:00pm

Faculty Information

Outside of these office hours, you can always reach me through email. I will be happy to schedule virtual meetings with you outside of these hours.

Course Information

Course Outcomes

At the end of this course, students will be able to:

  1. Organize data using frequency distributions and graphs
  2. Distinguish between the different types of studies and different types of random sampling
  3. Describe (summarize) data using measures of central tendency and dispersion
  4. Perform calculations associated with fundamental distributions and solve related application problems
  5. Construct confidence intervals for means and proportions
  6. Perform hypothesis tests for means and proportions using p-values.
  7. Calculate correlation coefficient and check its significance
  8. Estimate a regression equation and interpret its coefficients
  9. Use technology to complete basic statistical analyses
Topical Outline
  1. Data Collection
    1. Introduction to the Practice of Statistics
    2. Observational Studies vs Designed Experiments
    3. Simple Random Sampling
    4. Other Effective Sampling Methods
    5. Bias in Sampling
    6. The Design of Experiments
  2. Descriptive Statistics
    1. Organizing Qualitative Data
    2. Organizing Quantitative Data: The Popular Displays
    3. Additional Displays of Quantitative Data
    4. Graphical Misrepresentations of Data
  3. Numerically Summarizing Data
    1. Measures of Central Tendency
    2. Measures of Dispersion
    3. Measures of Central Tendency and Dispersion from Grouped Data
    4. Measures of Position and Outliers
    5. The Five-Number Summary and Boxplots
  4. Describing the Relation Between Two Variables
    1. Scatter Diagrams and Correlation
    2. Least Squares Regression
    3. Diagnostics on the Least-Square Regression
    4. Contingency Tables and Association
  5. Probability and Probability Distributions
    1. Probability Rules
    2. The Addition Rule and Complements
  6. Discrete Probability Distributions
    1. Discrete Random Variables
    2. The Binomial Distribution
  7. The Normal Probability Distribution
    1. Properties of the Normal Distribution
    2. Applications of the Normal Distribution
    3. Assessing Normality
  8. Sampling Distributions
    1. Distribution of the Sample Mean
    2. Distribution of the Sample Proportion
  9. Estimating the Value of a Parameter
    1. Estimation a Population Proportion
    2. Estimating a Population Mean
    3. Estimating a Population Standard Deviation
    4. Putting It Together: Which Procedure Do I Use?
  10. Hypothesis Tests Regarding a Parameter
    1. The Language of Hypothesis Testing
    2. Hypothesis Tests for a Population Proportion
    3. Hypothesis Tests for a Population Mean
    4. Hypothesis Tests for a Population Standard Deviation
  11. Inferences on Two Samples
    1. Inference about Two Population Proportions
    2. Inference about Two Means: Dependent Samples
    3. Inference about Two Means: Independent Samples
  12. Inference on Categorical Data
    1. Goodness-of-Fit Test
    2. Tests for Independence and the homogeneity of Proportions
  13. Comparing Three or More Means
    1. One-Way Analysis of Variance
Textbook/s and Course Materials

Course Materials:

You do not have to purchase the book. The textbook is not required for this course – you will need access to Canvas and the website, StatCrunch.com (Pearson).

Navigate to the website, select “Sign in or Register,” on the login page, select “Register.” A 6-month access code will cover the duration of this course.

Methods of Evaluation

Description of Assignments:

Assignments: Assignments are to be submitted weekly with few exceptions. These will consist of practice problems within the website StatCrunch and, early in the semester, simply in a Word document as we begin to understand the jargon.

Research Project: You are expected to complete a semester-long research project covering the Phillips Curve. This project will feature data gathering, cleaning, and various treatments through the StatCrunch website. Your deliverable is to be modelled after academic research papers in the econometrics field. A rubric will be provided.

Exams: Exams will consist of terminology and interpretation of various statistical outputs. These will be open-ended questions and will contain visual elements.

Evaluation:

ItemPoints
Assignments35
Attendance15
Research Project50
Midterm Exam25
Final Exam75
Grading ScaleGrade
90%-100%A
80%-89%B
70%-79%C
60%-69%D
Below 60%F
Academic Division

Liberal Arts & Sciences

Dean, Jennifer Huggins; 815-802-8484; R310; jhuggins@kcc.edu; Division Office- W102; 815-802-8700

Course Policies

ATTENDANCE POLICY & MAKE-UP POLICY:

You are expected to attend classes and complete all assignments in-class and at home. It is your responsibility to get missed information/materials from other students if you are late or absent.

You are responsible for completing assignments on time. If you choose not to complete an assignment on time, you will receive a 0 for that assignment. There are no do-overs.*

  • If you require additional time to complete an assignment for a personal emergency, email me to accommodate your situation.
    • Make-up Exams are given with a valid reason. Please email me prior to the exam if you are not able to make it. No tests are given prior to their scheduled time.

Grading Timeline:

Graded work will be returned in a timely manner. Reflections will most often be returned the following week, discussion grades will most often be posted promptly, and exams will most often be returned within two weeks.

Expectations for Classroom and Online Behavior

Tips for Success:

A strong grade in this course will not be attainable through lectures exclusively. You will need to be actively involved in your learning in order to succeed. Complete your assignments, participate in class, and ask questions to further your understanding. If you do these things, there will be no surprises in the midterm or final course examinations.

Course Calendar

Important Dates:

January 12th - first day of class

January 19th – MLK Day, no class

March 9th – 13th – Spring Break

Final Exam: Wednesday, May 13th @ Noon

Topical Outline:

A. Data Collection

1. Introduction to the Practice of Statistics

2. Observational Studies vs Designed Experiments

3. Simple Random Sampling

4. Other Effective Sampling Methods

5. Bias in Sampling

6. The Design of Experiments

B. Descriptive Statistics

1. Organizing Qualitative Data

2. Organizing Quantitative Data: The Popular Displays

3. Additional Displays of Quantitative Data

4. Graphical Misrepresentations of Data

C. Numerically Summarizing Data

1. Measures of Central Tendency

2. Measures of Dispersion

3. Measures of Central Tendency and Dispersion from Grouped Data

4. Measures of Position and Outliers

5. The Five-Number Summary and Boxplots

D. Describing the Relation Between Two Variables

1. Scatter Diagrams and Correlation

2. Least Squares Regression

3. Diagnostics on the Least-Square Regression

4. Contingency Tables and Association

E. Probability and Probability Distributions

1. Probability Rules

2. The Addition Rule and Complements

F. Discrete Probability Distributions

1. Discrete Random Variables

2. The Binomial Distribution

G. The Normal Probability Distribution

1. Properties of the Normal Distribution

2. Applications of the Normal Distribution

3. Assessing Normality

H. Sampling Distributions

1. Distribution of the Sample Mean

2. Distribution of the Sample Proportion

I. Estimating the Value of a Parameter

1. Estimation a Population Proportion

2. Estimating a Population Mean

3. Estimating a Population Standard Deviation

4. Putting It Together: Which Procedure Do I Use?

J. Hypothesis Tests Regarding a Parameter

1. The Language of Hypothesis Testing

2. Hypothesis Tests for a Population Proportion

3. Hypothesis Tests for a Population Mean

4. Hypothesis Tests for a Population Standard Deviation

K. Inferences on Two Samples

1. Inference about Two Population Proportions

2. Inference about Two Means: Dependent Samples

3. Inference about Two Means: Independent Samples

L. Inference on Categorical Data

1. Goodness-of-Fit Test

2. Tests for Independence and the homogeneity of Proportions

M. Comparing Three or More Means

1. One-Way Analysis of Variance

College Policies, Resources and Supports

College Policies

For information related to the Student Code of Conduct Policy, Withdrawal Policy, Email Policy, and Non- Attendance/Non-Participation Policy, please review the college’s Code of Campus Affairs and Regulations webpage, which can be found at catalog.kcc.edu under the Academic Regulations & Conduct Guide. 

Resources

KCC offers various academic and personal resources for all students. Many services are offered virtually, as well as in person. Please visit Student Resources - Kankakee Community College to access student resources services such as:

  • Clubs and organizations
  • Counseling and referral services
  • Office of disability services
  • Student complaint policy
  • Transfer services
  • Tutoring services, etc.
Copyright & Syllabus Disclaimer

The materials on this course are only for the use of students enrolled in this course for purposes associated with this course. Further information regarding KCC's copyright policy is available at https://kcc.libguides.com/copyright. 
|Course syllabus/calendar is subject to change.