2.  Don’t speak, consult, share with anyone  Be truthful, fun, turn into me when you are done.

3.  Let’s set expectations, norms, behaviors that we expect from our five-week 140 family.  We have all agreed to a very challenging goal.  What do you as a student expect from me, the instructor?  What do you as a student expect from other students?  Think, write for one minute; then discuss with the person next to you; then write on board.

4.  Write our norms, behaviors, expectations on large paper; everyone sign it agreeing to our norms.  How will we make each other accountable to hold true to what we have agreed?

5.  I’m excited!... Just saying...

6.  Everyone gets a card; each group gets a corner (hearts, diamonds, spades, clubs)  Below are two sets of data. Discuss in your group what we can say about each data? What could each represent? What couldn’t each represent? Data Set #1: 97, 98, 94, 92, 31, 98, 93, 95, 97, 98, 98 Data Set #2: 4.0, 6.8, 7.1, 7.1, 7.2, 7.4, 7.7, 7.8, 12.1 Each group share out

7.  Observations that you or someone else records  But data is/should be more than just numbers; it is numbers in context... the story behind the numbers...  That’s where statistics come in... According to Dr. Gould, our textbook author, “The goal of statistics is finding meaning in data.”

8.  Think for 1 minute.  Share with the person next to you for 1 minute.  Share out to entire class (random selection; number off).

9.  Listen to the radio or watch TV news. Listen/look for a recent study & findings newscasters are reporting  Summarize the study/report  Why do you think they did the study/What was the purpose? How do you think they got their data?  Typed; printed out (do not submit via email); at least 5-6 complete sentences.  Due: tomorrow at beginning of class.

10. ...13 million adults, or 46 percent of the state’s population, are believed to be living with the precursor of type 2 diabetes or undiagnosed diabetes.  Researchers predict that 33 percent of young adults, aged 18 to 39, are pre-diabetic, which is rare since diabetes is generally more common among older adults.  According to researchers, up to 30 percent of residents living with pre-diabetes will develop type 2 diabetes within five years. As many as 70 percent of adults will develop the disease within their lifetime.  Why do you think ULCA is interested in this information? How do you think UCLA get these numbers, percents, predictions?

11.  Let’s think of a topic...  Now, what are some questions about that topic?  Write on board... and/or share out  Dr. Gould, UCLA

13. Then comes parenthood — and with it, an average weight gain of roughly 3-5 pounds for a man who lives in the same home as his child. Over the roughly 6 years following the birth of their first child, live-in dads picked up an average of about.6 BMI units. For a 5’10” man weighing 175 pounds, that is a weight gain of about 4 pounds. During the same period, the average BMI of men who had not fathered a child tended to decline slightly. The study used data collected on 10,623 young men over a 20- year period as part of the National Longitudinal Study of Adolescent Health. Many of those tracked were enrolled when they were 12, and some were followed to the age of 34.

15.  sample, statistic, variation  data set  population, parameter  experiment, observational study  causality, association, confounding variables  random selection, random assignment  numeric variable, categorical variable

16. SampleStatistic (changes from sample to sample; can vary) PopulationParameter (is fixed; does not change) Examples include µ, p, σ

17.  Go to Math 140 data on my website. Select and copy into Stat Crunch the ‘how much do you weigh?’ data  How many students are in this data? Is every student that attends COC included in this data?  Calculate the summary statistics for this distribution; compare & contrast sample statistics vs. population parameter

18.  The Department of Fish & Game are concerned about the health of fish in Lake Tahoe. They want some information about the weight of fish living in the lake.  They go out on the lake on several randomly- selected days and catch (then release) about 20 fish each day at random locations in the lake and weigh each fish.   They then calculate the mean and the standard deviation of the weights of the fish they caught

19.  Let’s say that you are my population  Raise you hand if you wear prescription glasses/contact lenses. The proportion of my population, us, who wear prescription glasses/contact lenses is p = _____  Now let’s randomly take a sample of 10 of us (let’s number off), 10 from our population… same question… let’s do this again... and again... Let’s make a dot plot on the board...

20.  Always, always comment, answer, compare, contrast… whatever the case.. in context! How can we find meaning if we don’t have context?  Remember what Dr. Gould said, “Goal of statistics is finding meaning in data.”  What are the objects? What was measured? What are the units of measure? Think about how we started our class tonight with the 2 data sets I gave you...

22.  1. Who carried out the survey?  2. How was the sample selected?  3. How large was the sample?  4. What was the response rate?  5. How were the subjects contacted?  6. When was the survey conducted?  7. What was the exact question asked?

23.  Assign each person a question number  Find your question number group members and discuss why we should ask ourselves your question before we believe a poll/survey (or even an experiment) is valid/trustworthy; what could possibly happen that would make the results misleading?  Be prepared to share out to the class; provide examples.

24.  Randomly choose eight students  Come up to board and write where you last went out to eat and how much you spent there (approximately).  What are our variables?  Two types of variables:  What else can we observe about our data? Trends? General statements? Discuss for 1 minute.

25.  Can you think of a categorical data that looks like numerical data… but it isn’t. It’s really categorical. Discuss for a minute…

26.  Always ask yourself, does finding the mean (average) of this data make sense?

27.  Data collection: your gender and if you have pierced ears or not.  What trends can we see?

28. Ears PiercedEars NOT Pierced Male Female

29.  Side by side bar graphs, grouped bar chart..  Raw data  Looking at data in different ways helps us understand data better  Go to Stat Crunch, explore, data, search domestic violence, click on data set to bring up

30.  Experienced domestic violence or not; experienced headaches or not (included staff of shelter as a comparison)  Graph, bar plots, with data  Select abused, group by slxhead, type relative frequency, compute  Can also do a two-way stat, tables, contingency, with data

31.  Percentages, proportions can sometimes be more helpful in seeing trends... Especially if we have more than one group and the total raw numbers are very different from group to group

32. SportInjuriesParticipants Baseball178,66815,600,000 Basketball615,54628,900,000 Bowling21,13343,900,000 Football387,94817,700,000 Ice Hockey16,4352,100,000 Soccer178,51914,500,000 Softball125,87513,600,000 Tennis19,63311,000,000 Volleyball59,22511,500,000

33.  There are always firefighters at fires. Therefore, firefighters cause fires  x: Person regularly attends religious services  y: How long a person lives  x: HS seniors’ SAT scores  y: Students’ first year GPA

34.  x causes y, there must be a controlled, randomized, well-designed experiment  Treatment (explanatory, factor) variable; Response (outcome) variable  Sample size ‘large’  Treatment group (receiving treatment(s); control group (receiving placebo)  Random assignment of subjects (or experimental units)… using an acceptable randomization process  Double-blinding is best

35.  Reduce bias  Bias: Systemically ‘off’  Examples: scale, clock

36. Gastric freezing is a proposed treatment for ulcer pain in the upper intestine. In this treatment the patient swallows a deflated balloon with tubes attached. Then a cold liquid is pumped through the balloon for an hour. The rationale is that the cooling will reduce production of acid and relieve ulcer pain. A study reported in the Journal of the American Medical Association provided gastric freezing to a large number of patients and reported that gastric freezing reduced acid production and relieved stomach pain. Based on this the (safe and easy) treatment was used for several years.

37. A later study divided ulcer patients randomly into two groups. The first group was treated by gastric freezing. The second group received a placebo treatment in which the liquid was at body temperature rather than cooled In the first group 34% of 82 patients improved. In the second group 38% of 78 patients improved. Based on this second study gastric freezing was discontinued as a therapy for ulcers.

38.  Is it well designed? Why or why not?  Marshmallow video  https://www.youtube.com/watch?v=QX_oy9614HQ https://www.youtube.com/watch?v=QX_oy9614HQ  Random selection/assignment?  Replication?  Confounding variables?  Control/Comparison group?  Double blinded?  Placebo?

39.  https://www.youtube.com/watch?v=RK- oQfFToVg https://www.youtube.com/watch?v=RK- oQfFToVg  Random selection/assignment?  Replication?  Confounding variables?  Control/Comparison group?  Double blinded?  Placebo?

40.  Is this an observational study or a controlled experiment?  Experiment? Was there a large sample size? Was randomization used to assign participants to treatment groups? Was the study double- blinded? Was there a placebo?  Was the paper published in a peer-reviewed journal or just posted on internet?  Did the study follow the subjects for a long period of time?

42.  What is the difference between a sample statistic and a population parameter? What are the symbols used?  Why is context so important?  What is the difference between an experiment and an observational study?  Give an example of a confounding variable.  Why is random selection or random assignment so essential? What is the difference between them?

43.  Give an example of categorical data then give an example of numerical data. How are they different?  Explain double blinding and why it is important. Would double blinding be used in a survey or observational study or an experiment?  Why is replication so important?

44.  Page 26  1.1, 1.2, 1.15, 1.21, 1.23, 1.25, 1.27, 1.29, 1.31, 1.33, 1.35