General Info Professor: Dr. Mine C etinkaya-Rundel - - - PowerPoint PPT Presentation

Lecture 0: Introduction

Statistics 101

Mine C ¸ etinkaya-Rundel

January 10, 2013

Syllabus & policies Logistics

General Info

Professor:

• Dr. Mine C

¸ etinkaya-Rundel - mine@stat.duke.edu Old Chemistry 213 Teaching Gary Larson - gary.larson@stat.duke.edu Assistants: Yingbo Li - yl118@duke.edu Shaan Qamar - shaan.qamar@duke.edu Anthony Weishampel - anthony.weishampel@duke.edu Lecture: Tuesdays and Thursdays, 1:25 - 2:40 at Soc Sci 136 Lab: Mondays at Old Chem 101

• 08:30am - 09:45am - Anthony
• 10:05am - 11:20am - Gary
• 11:45am - 01:00pm - Anthony
• 01:25pm - 02:40pm - Gary
• 03:05pm - 04:20pm - Gary

Statistics 101 (Mine C ¸ etinkaya-Rundel) Lecture 0: Introduction January 10, 2013 1 / 39 Syllabus & policies Logistics

Required materials

Textbook OpenIntro Statistics Diez, Barr, C ¸ etinkaya-Rundel CreateSpace, 2nd Edition, 2012 ISBN: 978-1478217206 Clicker i>clicker2. ISBN: 1429280476, available at the Duke textbook store, i>clicker website, or Ama- zon, used clickers from former students (see Google doc). Calculator (Optional) You might need a four function calcu- lator that can do square roots for this class. No limitation on the type of calculator you can use.

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Clicker registration

http://iclicker.com/support/registeryourclicker

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Syllabus & policies Logistics

Webpage

http://stat.duke.edu/courses/Spring13/sta101.001 All announcements and assignments will be posted on this website under the schedule tab.

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Grading

• Clicker questions: 5%
• Problem sets: 7.5%
• Labs: 7.5%
• Readiness assessments: 15%

(2/3 individual, 1/3 team)

• Project 1: 10%
• Project 2: 10%
• Midterm: 15%
• Final: 25%
• Peer evaluations: 5%

Grades curved at the end of the course after overall averages have been calculated.

Average of 90-100 guaranteed A-. Average of 80-90 guaranteed B-. Average of 70-80 guaranteed C-.

The more evidence there is that the class has mastered the material, the more generous the curve will be.

Statistics 101 (Mine C ¸ etinkaya-Rundel) Lecture 0: Introduction January 10, 2013 5 / 39 Syllabus & policies Details

Course goals & objectives

1

Recognize the importance of data collection, identify limitations in data collection methods, and determine how they affect the scope of inference.

2

Use statistical software to summarize data numerically and visually, and to perform data analysis.

3

Have a conceptual understanding of the unified nature of statistical inference.

4

Apply estimation and testing methods to analyze single variables

• r the relationship between two variables in order to understand

natural phenomena and make data-based decisions.

5

Model numerical response variables using a single explanatory variable or multiple explanatory variables in order to investigate relationships between variables.

6

Interpret results correctly, effectively, and in context without relying on statistical jargon.

7

Critique data-based claims and evaluate data-based decisions.

8

Complete two research projects: one that employs simple statistical inference and another that employs more advanced modeling techniques.

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Units and major topics

Unit 1 Introduction to data: Observational studies and non-causal inference, principles of experimental design and causal inference, exploratory data analysis: description, summary and visualization, introduction to statistical inference. Unit 2 Probability and distributions: The basics of probability and chance processes, Bayesian perspective in statistical inference, the normal distribution. Unit 3 Framework for inference: Central Limit Theorem and sampling distributions Unit 4 Statistical inference for numerical variables Unit 5 Statistical inference for categorical variables Unit 6 Simple linear regression: Bivariate correlation and causality, introduction to modeling Unit 7 Multiple linear regression: More advanced modeling

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Syllabus & policies Details

Course structure

Seven learning units. Set of learning objectives and required and suggested readings, videos, etc. for each unit. Prior to beginning the unit, complete the readings and familiarize yourselves with the learning objectives. Begin a new unit with a readiness assessment: individual, then team. Tuesdays and Thursdays: Split rest of the class time between lecture (supplemented with active participation and peer instruction via clickers) and team application exercises. Mondays: Complete lab assignments in teams.

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Teams

Assigned to teams of 4-5 students based on data from the survey and the pre-test. Teams are heterogeneous with respect to stats exposure and homogenous with respect to majors and/or interests - to the extent that it’s possible. Once team assignments have been made there is no option for changing teams, other than under extraordinary circumstances. Six peer evaluations throughout the semester as well as other measures to ensure the functionality of the teams and to make sure all team members contribute to the team work.

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Lectures

Lecture slides will be posted on the course webpage (under schedule) by noon the day of the course. In order to be able to keep up with the pace of the course and not fall behind you must attend the lectures. Introduction of concepts as well as hands on activities and exercises to complement them.

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Clicker questions

Objective: Make you an active participant and help me pace the class. On new material introduced in class that day. Credit for clicking in, regardless of whether you have the correct answer (must answer at least 75% of the questions that day). Up to two unexcused late arrivals or absences will not affect your clicker grade. If one person is simultaneously using two or more clickers, all students involved will receive a 0 for an overall clicker grade. Grading will start on January 22.

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Syllabus & policies Details

Problem sets

Objective: Help you develop a more in-depth understanding of the material and help you prepare for exams and projects. Questions from the textbook. Due at the beginning of class on the due date. Show all your work to receive credit. Welcomed and encouraged to work with others, but turn in your

• wn work.

Lowest score will be dropped. No make-ups. Excused absences do not excuse homework.

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Labs

Objective: Give you hands on experience with data analysis using a statistical software and provide you with tools for the projects. http://beta.rstudio.org Add your gmail address to Google doc by 5pm today to create an RStudio account. Complete in teams. Lowest lab score will be dropped. If you do not attend a lab section, you are not eligible for credit

• n that lab.

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Projects

Objective: Give you independent applied research experience using real data and statistical methods. Project 1:

individual statistical inference exploring the distributional characteristics of

• ne variable or relationship between two variables

choose a research question, find data, analyze it, write up your results

Project 2: in teams, presentation, multiple linear regression, more info later

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Readiness assessments

Objective: Encourage you to complete the reading assignment prior to coming to class and evaluate your conceptual understanding of the learning objectives. 10 multiple choice questions, at the beginning of a unit. Conceptual questions addressing the learning objectives of the new unit, assessing familiarity and reasoning, not mastery. Take the individual readiness assessment using your clickers, and then re-take the same assessment in teams. Your performance on both assessments factors into your final grade: score for each assessment is a weighted average of the individual (2/3) and team (1/3) scores. First readiness assessment next Tuesday, for practice, not graded. 6 graded readiness assessments, lowest score will be dropped.

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Syllabus & policies Details

Exams

Midterm: Thursday, February 21 Final: Saturday, May 4, 2-5pm (Cumulative) Exam dates cannot be changed. No make-up exams will be

• given. If you cannot take the exams on these dates you should

drop this class. You must bring a calculator to the exams (no cell phones, iPods, etc.) and you are also allowed to bring one sheet of notes (“cheat sheet”). This sheet must be no larger than 8 1

2” × 11” and

must be prepared by you (no photocopies). You may use both sides of the sheet.

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Work load

You are expected to put in 4-6 hours of work outside of class. Some

• f you will do well with less time than this, and some of you will need

more.

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Email

I will regularly send announcements by email, so make sure to check your email daily. While email is the quickest way to reach me outside of class, it is much more efficient to answer most statistical questions in person.

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Discussion Forum on Sakai

Any non-personal questions related to the material covered in class, problem sets, labs, projects, etc. should be posted on the Discussion Forum on Sakai. Before posting a new question please make sure to check if your question has already been answered. The TAs and myself will be answering questions on the forum daily and all students are expected to answer questions as well.

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Syllabus & policies Support

Office hours

Professor Mondays and Wednesdays 2pm - 4pm TAs at the SECC Sunday - Thursday 4pm - 9pm (Old Chemistry 211A)

The statistics education center has upper level statis- tics students available to help you. For more informa- tion and a schedule see http://stat.duke.edu/courses/ resources-students .

You are highly encouraged to stop by with any questions or comments about the class, or just to say hi and introduce yourself. Most problem sets due on Thursday. Recommend attempting all problems by Wednesday to make the most of OH. Specific TA office hours TBA.

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Other learning resources

Aside from the TAs and the professor’s office hours, you can also make use of the Academic Resource Center. For more information, see http://web.duke.edu/arc .

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Students with disabilities

Students with disabilities who believe they may need accommodations in this class are encouraged to contact the Student Disability Access Office at (919) 668-1267 as soon as possible to better ensure that such accommodations can be made.

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Policies I

Late work policy for problem sets and labs reports:

late but during class: lose 10% of points after class on due date: lose 20% of points next day: lose 30% of points later than next day: lose all points

Late work policy for projects: 10% off for each day late. There will be no make-up for clicker questions, individual and team readiness assessments, labs, problem sets, projects, or exams. If a readiness assessment or the midterm exam must be missed, absence must be officially excused in advance, in which case the missing exam score will be imputed using the final exam score. Missed assessments not excused in advance will receive a grade of 0. The final exam must be taken at the stated time.

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Syllabus & policies Policies

Policies II

You must take the final exam and turn in the two projects in order to pass this course. Regrade requests must be made within one week of when the assignment is returned, and must be submitted in writing.

These will be honored if points were tallied incorrectly, or if you feel your answer is correct but it was marked wrong. No regrade will be made to alter the number of points deducted for a mistake. There will be no grade changes after the final exam.

Clickers may not be shared, and the clicker registered to a person may only be used by that person. Failure to abide by this will result in a 0 clicker grade for everyone involved.

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Academic Dishonesty

Any form of academic dishonesty will result in an immediate 0 on the given assignment and will be reported to the Office of Student

• Conduct. Additional penalties may also be assessed if deemed
• appropriate. If you have any questions about whether something is or

is not allowed, ask me beforehand. Some examples: Use of disallowed materials (including any form of communication with classmates or looking at a classmate ˜ Os work) during exams. Plagiarism of any kind. Use of outside answer keys or solution manuals for the homework.

Statistics 101 (Mine C ¸ etinkaya-Rundel) Lecture 0: Introduction January 10, 2013 25 / 39 Syllabus & policies Tips

Tips for success

1

Complete the reading before a new unit begins, and then review again after the unit is over.

2

Be an active participant during lectures and labs.

3

Ask questions - during class or office hours, or by email. Ask me, the TAs, and your classmates.

4

Do the problem sets - start early and make sure you attempt and understand all questions.

5

Start your projects early and and allow adequate time to complete them.

6

Give yourself plenty of time time to prepare a good cheat sheet for exams. This requires going through the material and taking the time to review the concepts that you’re not comfortable with.

7

Do not procrastinate - don’t let a week go by with unanswered questions as it will just make the following week’s material even more difficult to follow.

Statistics 101 (Mine C ¸ etinkaya-Rundel) Lecture 0: Introduction January 10, 2013 26 / 39 To do

To do

Get an i>clicker2 and register it. If you have previously bought an i>clicker1 for this course and cannot return it, see me after class

• r at OH tomorrow.

Download or purchase the textbook. If you missed lab yesterday:

Complete the getting to know you survey on Sakai. Complete the pretest. Add your Gmail address to the Google Doc

Read the syllabus and let me know if you have any questions. Start reviewing the resources for Unit 1 - readiness assessment next Tuesday.

Statistics 101 (Mine C ¸ etinkaya-Rundel) Lecture 0: Introduction January 10, 2013 27 / 39

Statistics and the Scientific Method

Statistics and the Scientific Method

From Universe Today - http://www.universetoday.com/74036/what-are-the-steps-of-the-scientific-method/ Statistics 101 (Mine C ¸ etinkaya-Rundel) Lecture 0: Introduction January 10, 2013 28 / 39 Examples Baby names

Baby names in the US

Each year the Social Security Administration collects and releases data on the how many babies are given a certain name. They released these data for years 1880 to 2011 for each gender. For privacy reasons they restrict the list of names to those with at least 5 occurrences.

Statistics 101 (Mine C ¸ etinkaya-Rundel) Lecture 0: Introduction January 10, 2013 29 / 39 Examples Baby names

Top 10 Baby Names For 2011

http://www.ssa.gov/oact/babynames Statistics 101 (Mine C ¸ etinkaya-Rundel) Lecture 0: Introduction January 10, 2013 30 / 39 Examples Baby names

Jac...

Statistics 101 (Mine C ¸ etinkaya-Rundel) Lecture 0: Introduction January 10, 2013 31 / 39

Examples Baby names

Clicker question Which of the below is the most common name in this class? (a) Andrew (b) William (c) Kevin (d) Rachel (e) Grace

Statistics 101 (Mine C ¸ etinkaya-Rundel) Lecture 0: Introduction January 10, 2013 32 / 39 Examples Geotagged data

Clicker question Do you geotag your posts on social networking sites, like Facebook, Twitter, Instagram, etc.? (a) yes (b) no

Statistics 101 (Mine C ¸ etinkaya-Rundel) Lecture 0: Introduction January 10, 2013 33 / 39 Examples Geotagged data

Map based on Flickr tags

http://aaronstraupcope.com Statistics 101 (Mine C ¸ etinkaya-Rundel) Lecture 0: Introduction January 10, 2013 34 / 39 Examples 538

The most famous statistician in the world

Source: http://fivethirtyeight.blogs.nytimes.com Statistics 101 (Mine C ¸ etinkaya-Rundel) Lecture 0: Introduction January 10, 2013 35 / 39

Examples Links to blogs

Links to blogs

http://stat.duke.edu/courses/Spring13/sta101.001/links.html

Statistics 101 (Mine C ¸ etinkaya-Rundel) Lecture 0: Introduction January 10, 2013 36 / 39 Why study statistics

[...], the study also warns there is a significant shortage

• f qualified workers to analyze

these data sets adequately. According to the report, a shortfall of about 140,000 to 190,000 individuals with analytical expertise is projected by 2018. The study also predicts a need for an additional 1.5 million managers and analysts by that same date to fully engage the true potential of the currently available data.

http://jobs.aol.com/articles/2011/08/10/data-scientist-the-hottest-job-you-havent-heard-of Statistics 101 (Mine C ¸ etinkaya-Rundel) Lecture 0: Introduction January 10, 2013 37 / 39 Why study statistics http://www.dailymail.co.uk/news/article-2023514 Statistics 101 (Mine C ¸ etinkaya-Rundel) Lecture 0: Introduction January 10, 2013 38 / 39 Data collection

What do you want to know?

We’ll do a class survey, collecting data you are interested in. What do you want to know about your peers?

Is this a question about one variable or two variables? What are the variables? Are they categorical or numerical?

Work in groups to write a question to measure variable(s) of

• interest. Write questions so the resulting data will be accurate

and easy to analyze.

Numerical variable? Give units. Categorical variable? Give the possible categories (at most 5). Be clear and specific.

I will email with instructions to fill out an anonymous online survey.

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