Monday, August 30, 2010

O-JIVE

Worksheet:  #30 and 1.20
Ch. 5 #27, 30, 41

WebWork#4


Read and summarize the following article.  Pay careful attention to HOW the data was collected.  Make sure to click on the "Read More" so that you can read the whole article.  Read to the end please.
Suffering Adds Up in a Hurry In Survey of Tsunami Survivors
By David Brown
Washington Post Staff Writer
Saturday, January 15, 2005; Page A01
CALANG, Indonesia -- For two days this week, an Australian doctor and his Acehnese assistant knocked on every 10th door in Calang, a seaside town on Sumatra island that was decimated by the Dec. 26 tsunami. At each house, they asked the same brief questions, thanked the residents and departed after about 10 minutes.
What they learned in their bare-bones, random statistical survey, conducted for the International Rescue Committee, was chilling.
Before the tsunami, 8,700 people lived in Calang; now, that number is 2,500, and a third of those are displaced from other towns. Sixty-five percent of households have had a death in the immediate family. Twenty-two percent have taken in orphans, usually more than one. Only 8 percent of the population is younger than 5, and 85 percent of those children have had diarrhea in the past two weeks.
The survey, conducted by Richard Brennan and his assistant, Kamaruddin, has provided the most precise look to date at the tsunami's effects on the people living in the worst-hit part of the worst-hit country.
The survey produced more than numbers, however. It will help the International Rescue Committee, a humanitarian relief agency based in New York, plan how best to spend the $7 million it has budgeted to assist Indonesian survivors of the tsunami.
Relief organizations often use such systematic assessments during man-made disasters involving war, famine and forced dislocation, in which people's needs may not be obvious.  

Thursday, August 26, 2010

3-day weekend of stats!

Ch. 5 #33, 34

Tuesday, August 24, 2010

Outlier?

Ch. 5 #23-25

Monday, August 23, 2010

Bored?


Want to procrastinate instead of do your schoolwork?

My son's videos are a great place to waste some time!  Bob's World of Clay

Boxplots!

Ch. 5 #32, 35, 36

WebWork #3

What Happy People Don’t Do


Published: November 19, 2008
Happy people spend a lot of time socializing, going to church and reading newspapers — but they don’t spend a lot of time watching television, a new study finds.
That’s what unhappy people do.
Although people who describe themselves as happy enjoy watching television, it turns out to be the single activity they engage in less often than unhappy people, said John Robinson, a professor of sociology at theUniversity of Maryland and the author of the study, which appeared in the journal Social Indicators Research.
While most large studies on happiness have focused on the demographic characteristics of happy people — factors like age and marital status — Dr. Robinson and his colleagues tried to identify what activities happy people engage in. The study relied primarily on the responses of 45,000 Americans collected over 35 years by the University of Chicago’s General Social Survey, and on published “time diary” studies recording the daily activities of participants.
“We looked at 8 to 10 activities that happy people engage in, and for each one, the people who did the activities more — visiting others, going to church, all those things — were more happy,” Dr. Robinson said. “TV was the one activity that showed a negative relationship. Unhappy people did it more, and happy people did it less.”
But the researchers could not tell whether unhappy people watch more television or whether being glued to the set is what makes people unhappy. “I don’t know that turning off the TV will make you more happy,” Dr. Robinson said.
Still, he said, the data show that people who spend the most time watching television are least happy in the long run.
Since the major predictor of how much time is spent watching television is whether someone works or not, Dr. Robinson added, it’s possible that rising unemployment will lead to more TV time.
1. This is a classic example of the difference between association and causation. Explain:
2. Discuss what other variables might be influencing this study.  That is, what might be also associated with TV watching the effects people's happiness?
3. How might a researcher study causation for this scenario? What would make causation difficult to determine?

Wednesday, August 18, 2010

Describe!

Ch. 4 #8, 9, 11

Monday, August 16, 2010

independent?

Ch. 2 #9
Ch. 3 #16 & 18

WebWork #2

Please read this:  Wired Article

Did you click on "Correlation is not causation" link in the article?  If not, please do so now and read some more.

Write a one paragraph response to this article.  You might use the ideas below to shape your response, but you don't need to.  I want you to tell me what YOU thought about the article on a personal level.

  • Are there things you have planned for your future that will require statistical literacy?
  • Do you see decisions made at this school that could be improved if we had better data?
  • Do you hear things in the news that make you wonder about statistics?
  • Do you agree with the author's premise?

Friday, August 13, 2010

Segmented Bar

We did Ch. 3 #15 in class.
No homework.

Thursday, August 12, 2010

Pie Charts!

Ch. 2 #8
Ch. 3 #6 & 8
Don't forget to scroll down to Monday to see the WebWork!

Wednesday, August 11, 2010

Day 2

Ch. 2 #4 & 5
Read chapter 1

Monday, August 09, 2010

First WebWork for the year

This article (click here) is very interesting.  Read it.

Now consider these questions (in your notes/notebook):

1)  Why is the average so high above the typical user?

2)  Cell phone companies have stopped offering unlimited data plans for smart phones.  Explain why the change between 2009 and 2010 makes this a sensible choice.

3)  With ATT, only users who use more than 1000 MB will have to pay more money.  What percentage of customers is this?

4)  Sticking with ATT, users who use less than 200 MB will have their bill cut in half.  What percentage of users is this?

5)  The x-axis is not scaled properly.  Do you think it should be?

6)  In the end, do you think this is good for consumers now?   Explain why you think this might be bad for consumers in the future.