Math 219 Syllabus-MW 1015 Spring 2018.docx 

Meet Your Instructor:

 Hi, my name is Dr. George Sweeney. I am a full-time professor here at Santa Ana College.  I have a PhD in Math Education from the joint doctoral program at San Diego State and UC San Diego.  I also have a master's degree in Math from Cal Poly Pomona and a bachelor's from UCLA.  My fields of specialty are in research methodologies, Statistics, Linear Algebra, and Data Science.  I am Bronze, Silver, and Gold certified in Canvas in-person and online teaching. My dissertation was on teaching and learning in Linear Algebra, and I have done research and been published in research in trigonometry, differential equations, physical chemistry and calculus.  I love to teach and do mathematics.  I am currently learning some new computer programming languages.  I also enjoy meditating, making music, reading, playing with my kids, and hanging out with my wife.  I have been married for 13 years and I have two children, 2 1/2 and 5.

Course Description: 

This is an introductory course in statistical reasoning.  I use the term statistical reasoning because the focus of the course is how do we make decisions based upon data.  Statistical reasoning is a process of making decisions and as such is much more than simply mathematics.  The mathematics is important certainly, but the process that is used to make these decisions requires logic and a set of practices that extend beyond the use of numbers.  In this class you will learn to make decisions based upon comparing probabilities, developing descriptive statistics, examining data, making inferences, and comparing data from different groups using statistical methods.  The goal of this class is to make you better purveyors of information and expand your reasoning skills to include data-driven activities.

I fully understand that many students have had difficulties with mathematics in the past.  But I would say that statistics is a totally different discipline than anything you have done in the past.  So, maybe we can start fresh.  You forget for a bit that this is a math class and I will work on teaching you statistics from a fresh place.  You can do this and together we can learn statistics. With that said, there is some math that will be handy for you to know:

Textbook Information:  We will be using Open Educational Resources, OpenStax textbook and LumenOhm learning management system.  Access to Lumenohm is provided to you with entering the course.  The remaining resources are free and available in this course.

Additional Course Information: 

A calculator that can do distributions is required.  I recommend either a graphing calculator or the TI-36X Pro.  In addition, several assignments will require Stat Crunch.  Stat Crunch is available online and costs 16.20 for 6 months worth of access. It might be helpful for your work in class to bring in a laptop or tablet to do some of the data crunching for the course.

Course Learning Objectives: 

1. Distinguish between and discuss the implications of scales of measurement and types of data.
2. Interpret data displayed in tables and graphs.
3. Identify and critique (advantages and disadvantages) sampling techniques and study designs.
4. Calculate measures of central tendency (mean, median, mode, and midrange) and measures of spread (standard deviation, variance, interquartile range, and range) for data sets and distributions.
5. Apply the concepts of sample space and the principles of probability.
6. Calculate expected value (mean) and standard deviation (or variance) for discrete probability distributions.
7. Calculate probabilities, mean, and standard deviation for binomial random variables.
8. Use the Standard-Normal distribution to find probabilities or values for normal random variables.
9. Distinguish between population and sampling distributions noting their similarities, differences, and the role of the Central Limit Theorem.
10. Construct and interpret confidence intervals.
11. Determine and interpret levels of statistical significance using p-values.
12. Use the Standard-Normal and Student-t distributions to find critical values for confidence intervals and to calculate probabilities (p-values).
13. Identify the fundamental components of hypothesis testing, including Type I and II errors, and interpret technology-based output for statistical analyses.
14. Conduct and interpret the results from hypothesis tests for one and two populations (using z, t, and Chi-Square distributions), linear regression, and ANOVA.
15. Select the appropriate type of hypothesis test for a given situation.
16. Select the appropriate statistical technique to answer statistical questions involving estimation or inference using data from disciplines including business, social sciences, psychology, life science, health science, and education.

Student Learning Objectives:  

The Student Learning Outcomes for the course are as follows:

1. By the end of the semester students will correctly interpret a graphical display of data.
2. By the end of the semester students will take a statistical claim about a data set, perform an appropriate procedure and write a conclusion that addresses the claim.