1. Lecture PowerPoint Slides Basic Practice of Statistics 7 th Edition

2. Tips and expectations Although it’s a 100-level class, this is a hard class. The failure/dropout rate is high. BUT, you can succeed. Have a plan in place to study and get help when you need it. Relevant examples Actual practice with spreadsheets More focus on concepts and applications than on memorization of formulas

3. What you won’t have to do Copy down PowerPoint content (although you might want to take notes on what I say) Memorize lots of formulas

5. How to study for this class Actively use the textbook before class. When you sit down to read it, have a pencil and paper in hand and try to work through each example BEFORE looking at the book’s solution. If you don’t understand parts of the solution, seek help (me, tutor, classmate, internet search, etc.). Review your exit slips, quizzes, and exams; make sure you understand your mistakes. When I leave comments on your spreadsheet, let me know if you don’t understand them. Growth mindset vs. set mindset

6. Tips from former students – Dorothy Hershey I read the book- I didn't even read sections word for word. I mostly read the definitions and copied down the bolded words and their meaning. Even If I wasn't able to understand the text, it at least gave me a good heads up for what we would talk about in class and really helped me get those easy points from the entry/exit slips. A friend and I did the weekly quizzes together. It helped being able to talk through the questions out loud. We also rarely waited until the night of or morning of to do them. We typically did them one or two days before it was due which gave us plenty of time to look in the book, or ask questions in class. I found that going through each problem on the quizzes was helpful. I made sure that I understood why I missed the problems I got wrong- I even made sure to understand the problems I had gotten correct on the quizzes (looking back on the quizzes, I would sometimes not understand how I got certain problems right, so it was beneficial for me to go through all problems whether I got them right or wrong.) Also, I always did a few practice problems from the book- and I always did odd ones, so I could check if I got it right. Lastly, I asked questions!

7. Tips from former students – Eyan Roth One thing that was extremely helpful for me in preparing for exams were the study guides that you had posted. I found that if I was familiar with all of the concepts on the study guide I was well prepared for the exams. Another thing I did to prepare for exams was to work through some of the problems at the end of each chapter that involved calculations to make sure that I had a grasp on each of the different calculations that I would need to know for the test. Reviewing the online quiz questions was also helpful in preparing for exams since you sometimes pulled questions directly from those or used questions that were very similar.

8. Tips from former students – Dylan Polley and Miranda Lyle One thing I always did was read the book before class. Actually working through the problems in there was very beneficial. Studying together with others. Reviewing quiz solutions after I took them.

9. Statistics How do we know which TV shows, songs, etc. are popular? How can I improve my athletic performance? How can I find true love? How does faith affect quality of life? What are the extent, causes, and possible solutions to global climate change? Statistics is the science of learning from data.

10. Data are numbers, but … 3 5 23 37 6 8 20 22 1 3

11. Where the data come from matters Observational studies versus experiments Early hormone replacement studies: observational. Challenge: lurking variables, such as education and affluence of patients Later, definitive studies: experiments, decided no reduction in heart attack risk due to therapy Just one example of complex relationship between variables being oversimplified

12. Always look at the data Example graph: A few carefully chosen graphs are often more instructive than great piles of numbers.

13. Figure 0.1, The Basic Practice of Statistics, © 2015 W. H. Freeman

14. Unnumbered art Page 5, The Basic Practice of Statistics, © 2015 W. H. Freeman, Bruce Weaver/AFP/Getty Images

15. Announcements Exams cumulative? Any problems with textbook listing? Tribe? Google Sheets (received?); take a look at SA1 and Quiz1

16. Entry slip question According to our book, variation can be found everywhere. What is one example that the book used of variation that occurs in our world?

17. Variation is everywhere Gas prices, June 1990 to August 2013:

18. Variation is everywhere… and it makes life interesting Sports Psychology Business Sounds Faith Language

19. Because variation is everywhere, Conclusions are uncertain. Truth? It’s in there … or not … We can use statistics in many places where math alone is not that helpful. MathStats

20. Key statistical questions What is the typical (average) value of a variable? Is something varying from the average in a significant way? How much is it varying? Why is it varying?

21. Doing statistics is … More than manipulating numbers! One approach: State a problem in its real-world context, Plan your specific statistical work in detail, “Solve” the problem by making the necessary graphs and calculations, and Conclude by explaining what your findings say about the real-world setting.

22. A final word on “failure” Go to this linkthis link http://www.nytimes.com/interactive/2015/07/03/upshot /a-quick-puzzle-to-test-your-problem-solving.html

23. Random Name Generator

24. Our class survey results CommuteAthleteIn shapeTimeExcelText 0023610 0034510 0033220 1036740 1038230 1037320 103.56310 1045030 104 40 0024511 002.5802.51 002.755221 0034041 0033731 003 31 0032131 003.56441 0052531 0053531 1031541 1036521 1034531 0135040 0131840 013.56020 1144530 0132411 0132921 014202.51 0143041 133520 Statistics is the science of learning from data.

25. What lies ahead Purpose: give a working knowledge of the ideas and tools of practical statistics. Three main areas: Data analysis (Chapters 1–6) Data production (Chapters 8 and 9) Statistical inference (Chapters 13–18, 20–22)