## Topic outline

### Course Overview

Introduction to Statistics

By Aric LaBarr

Enroll me Introduction to Statistics is not a math course. 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 data-analytic 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 data-gathering, data-analysis, 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 95-100% A- 90-94% B+ 87-89% B 83-86% B- 80-82% C+ 77-79% C 73-76% C- 70-72% D+ 67-69% D 63-66% D- 60-62% F 0-59%

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.My 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 co-founder of a small, boutique consulting firm called analytic-AL. 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 audio-video 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!**About Professor LaBarr**### Day 1-10

- Definition around data
- Exploring relationships with data
- Concepts of associations and correlation
- Data in the world around us

### Day 11-20

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

### Day 21-30

- Qualitative and quantitative variables
- Describing center

### Day 31-40

- Interpreting scatterplots
- Correlation
- Correlation and causation
- Regression idea

### Day 41-50

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

### Day 51-60

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

### Day 61-70

- Probabilities on intervals
- Types of continuous distributions
- 68-95-99.7 rule
- Standard scores
- Z-scores

### Topic 8

- Point estimates
- Sampling errors
- Central Limit Theorem
- Proportions

### Topic 9

- Margin of error
- Confidence intervals
- Empirical rule
- Standard error
- t distribution

### Topic 10

- Hypothesis testing
- Null and alternative hypothesis
- Test statistic
- P-value and significance levels
- Ethics around inferences

### Topic 11

- Summary of main course concepts

### Topic 12

- Analysis of variance
- Multiple comparisons
- Linear Regression