Syllabus

MATH 260 – Introductory to Statistics

4 credit hours

JTerm 2025

Course Overview

This course, grounded in algebra, introduces fundamental statistical concepts. Utilizing the statistical software RStudio, we will gather and scrutinize diverse datasets. You will master the interpretation of RStudio outputs to formulate insightful conclusions.

Catalog Description

Using statistical software, this course covers probability, descriptive statistics, sampling distributions and the Central Limit Theorem, hypothesis testing and confidence intervals, distributions of random variables and/or test statistics (normal, Z, t, F, binomial, and chi-square), t-tests (one- and two-sample, paired), analysis of categorical data (one proportion: binomial test, normal approximation; two or more proportions: chi-square tests, odds ratios), correlation, and simple linear regression. Credit will not be granted for both MATH 260 and BUS ADM 216.

Course Learning Outcomes

Upon successful completion of MATH 260 - Introductory Statistics, students will be able to: 

· Understand the fundamental role of statistics in daily life.   

· Define and distinguish the basic terminology of statistics, including sample vs. population, parameters vs. statistics, and descriptive vs. inferential statistics.   

· Use the simple random sampling method to collect samples for experiments.    

· Categorize variables as either qualitative or quantitative, and further as discrete or continuous.   

· Summarize and interpret datasets using graphical displays and numerical measures, depending on data type.  

· Use key concepts in probability theory and the rules that apply to calculating probabilities, focusing on both equally likely and unequally likely outcomes.    

· Construct discrete probability distributions.   

· Recognize the features of a binomial experiment and apply the binomial probability mass function to find probabilities associated with a binomial random variable.   

· Recognize the features of a normal distribution, and compute probabilities associated with normal distributions. 

· Understand the role sampling distributions play in statistical inference: Determine the sampling distribution of the sample mean for a normally distributed random variable and explain how the Central Limit Theorem is applied to large samples when the distribution of the random variable is unknown or non-normal.     

· Apply the statistical inference techniques for parameter estimation of point estimation and confidence interval estimation: Calculate and/or interpret point estimates and confidence intervals for one population mean, one and two population proportions, and two population means for both independent and dependent samples.   

· Comprehend the reasoning behind hypothesis testing and apply techniques for testing various statistical hypotheses concerning population parameters, including one-sample tests for a mean and for a proportion, two-sample and paired sample tests for two means (for independent and dependent samples, respectively), and the two-sample proportion test. 

· Explore the possible relationship between two quantitative variables using a scatter plot and linear correlation coefficient.   

· Develop and interpret a simple linear regression model between two quantitative variables, which allows for prediction.   

· Analyze categorical data via a goodness-of-fit test, and tests of independence and homogeneity.    

· Use a dedicated statistical package to conduct basic statistical analyses.

How to be successful in this course

Statistics problems are expected to be challenging and build on previous knowledge and understanding. Consequently, you should set aside at least 9 1/2 - 12 hours per week for study. (Note: The amount of time required for study will vary by individual.) It would be beneficial to work on extra problems, in addition to the ones assigned in the homework and assignments. If at any time you feel that you are falling behind, you should contact the instructor immediately.

Student’s Responsibility

· Be prepared for all classes.

· Be respectful of others.

· Actively contribute to the learning activities in class

· Make sure they have a strong internet connection.

Instructor’s Responsibility

· Be prepared for all classes.

· Evaluate all fairly and equally.

· Be respectful of all students.

· Create and facilitate meaningful learning activities.

· Behave according to university codes of conduct

· Give timely feedback.

Grading Policies

20% - Practice Homework:  The homework is graded. You have unlimited attempts on the homework and until the end of the semester to complete it.  I highly suggest attempting all homework before an exam as all exam problems come from the homework.  

40% - Chapter Exams: 2 exams will be given each worth 20% of your grade.  Make sure to schedule time in your calendar to take the exam. Make sure to have a strong internet connection. There are no makeup exams given.

30% - Final Exam: This will be a cumulative final exam.  You can choose Monday, Tuesday, or Wednesday to take the final during finals week.  Make sure to schedule time in your calendar to take the final. Make sure to have a strong internet connection There are no makeup finals given.

Letter-grade scale

Learning environment

This is an online course that requires you to have a strong internet connection.  This course is accessible through Canvas.   There is no live lecture for this course, students will be required to use the course software Access Pearson Statistics, Access Pearson Statistics videos, Lecture Videos, and lecture notes to learn the material.  If you email me by Friday, January 3^rd with the amount of time you should set aside per week to study for this course you will earn 1 extra credit point.

All students are required to purchase Access Pearson Statistics software through Pearson or the bookstore. You will use this software to complete practice homework, quizzes, and exams. 

You should regularly schedule time outside the class to complete homework and study.  Do not procrastinate or get behind in this class, the difficulty of the class will make it hard to catch up.  If you are struggling with homework or understanding concepts Access Pearson Statistics software has resources to help you.  These resources will be discussed in the introduction video on Navigating Access Pearson, be sure to ask about these resources if you have any questions.

The Exams will be taken through Access Pearson Statistics.  You must submit all work for your exams through Canvas to have an opportunity to earn partial credit.  However, you must submit all your work, not just specific problems within 10 minutes of completing your exam. There are no exceptions to the 10-minute rule even if you have internet issues. Making sure you have a strong internet connection is your responsibility. Your work must be submitted through Canvas and not emailed to me.  DO NOT review your exam until after submitting your work.  If your work shows you reviewed the exam before submitting it, I will not accept it.  Exam problems for which work is not submitted will not be graded for partial credit.  All work must be numbered (including a, b, c, etc..), neatly organized, and easy to follow or you will not receive partial credit. DO NOT scribble your work out as you should be using a pencil. There should not be error messages in your RStudio work. Your work must be a pdf otherwise it will not be accepted, and you will not have an opportunity to receive partial credit.  Review your pdf document before submitting it if it is not readable, I will not accept it, and you will not receive credit for it.  All calculations must be done in RStudio, not Excel.