MAT 161  Introduction to Statistics (3 Credits)
Topic outline

Introduction to Statistics
By Aric LaBarr
Enroll me Introduction to Statistics is not just about math. The course's central theme is to help you learn to understand the world from patterns in data. "Beyond the formula" skills are emphasized. This course will require you to: think critically, be skeptical, think about spread and variation in data (rather than just about the center of data), move beyond a "memorize the answer" approach, and think about how to make inferences from data. Some mathematical skill is required to work with elementary statistics, but mathematical manipulations will be replaced by relying on technology for the calculations and graphics; this will allow more emphasis to be placed on the "beyond the formula" skills mentioned above. This course requires more intellectual effort than the low mathematical level suggests! It is related to every other course you may study. The course is elementary in mathematical level but conceptually rich in statistical ideas and in its aim to improve your dataanalytic skills and your ability to apply statistical methods with understanding.
Course Objectives
This course will enable you to: Incorporate statistical thinking into your everyday lives;
 Acquire the necessary datagathering, dataanalysis, and interpretation/communication expertise to meet the challenges of a more demanding global environment;
 See and analyze the hidden patterns God has placed in the world through data.
Resources
All content for this course is found in the video lectures, slides, and reading materials.Assignments
View all online videos and read the assigned materials. After viewing everything for the unit, take the quiz based on the lectures (100% of grade).Grading Scale
A 95100% A 9094% B+ 8789% B 8386% B 8082% C+ 7779% C 7376% C 7072% D+ 6769% D 6366% D 6062% F 059%
Your average for the course must be at least 60%. Otherwise, you will fail the class and will receive no credit.Deadline
You have 180 days to finish the course. Complete all assignments before the final deadline, or you will be automatically unenrolled, and all coursework will be removed. You will have to start over and take the class again to receive credit.About Professor LaBarrMy name is Dr Aric LaBarr. Previously, I have been a Director and Senior Data Scientist at a data science consulting firm called Elder Research. Currently, I am an Associate Professor of Analytics at the Institute for Advanced Analytics at North Carolina State University and the cofounder of a small, boutique consulting firm called analyticAL. I am the husband to a loving wife and the father to two amazing children. My wife and I partner with another couple to lead a small group at our church. We also volunteer with the audiovideo team and help partner with the youth ministry when we can. I never grew up around faith, but God called me to a relationship with Jesus Christ when I was in my early 20's helping volunteer at my wife's (then fiancee) church. Since then I have tried to incorporate my faith into everything I do. This led me to create Data4God (www.data4god.com) where you can find out more about my story! 
 Definition around data
 Exploring relationships with data
 Concepts of associations and correlation
 Data in the world around us

 Randomness
 Samples and populations
 Sampling methods
 Experiments
 Data ethics
 Intuition around collected data

 Qualitative and quantitative variables
 Describing center

 Interpreting scatterplots
 Correlation
 Correlation and causation
 Regression idea

 Probability (and risk)
 Probability models
 Probability rules
 Simulation
 Law of large numbers
 Expectations

 Random variables
 Probability distribution
 Discrete probability
 Expected value and variance
 Binomial distribution

 Probabilities on intervals
 Types of continuous distributions
 689599.7 rule
 Standard scores
 Zscores

 Point estimates
 Sampling errors
 Central Limit Theorem
 Proportions

 Margin of error
 Confidence intervals
 Empirical rule
 Standard error
 t distribution

 Hypothesis testing
 Null and alternative hypothesis
 Test statistic
 Pvalue and significance levels
 Ethics around inferences

 Analysis of variance
 Multiple comparisons
 Linear Regression