Monday, November 15, 2010

Chapter 7 Q19


Chapter 7 Q19

a. The population standard deviation for this problem is 4000 (see page 278)

We can use Excel to find first the probability that the sample is in the area UP TO 51800 + 500 = 52300 and then UP TO 51800 – 500 = 51300. First find the standard error for a sample size of 60. This is 516.4. Then use normdist

=normdist(52300,51800,516.4,true) = 0.83

=normdist(51300,51800,516.4,true) = 0.17

then subtract to get 0.66

b. For (b), recalculate the standard error with the sample size of 120.

Sunday, November 14, 2010

SE Notation


The symbol for a population standard deviation is the Greek letter σ (‘sigma’).
For a sample standard deviation the symbol is s.

The notation for a standard error is given on the right of this post.

We don’t change the symbol on the left. Just switch the sigma in the numerator on the right side to s if you happen to know that it is from a sample (for example you just calculated it from data that is a sample)

Chapter 8.26


Chapter 8.26

Recall that the Margin of Error for 95% is
ME = 1.96*SE. Now, make sure you can follow the math here:

Chapter 8.22

Chapter 8.22

Note we’re told that the distribution has a normal distribution. This is important because the sample size is only 25, less than 30. Use Excel to get the mean

a. 3.348
b. Use =confidence(0.05,2.287,25) to get the margin of error. This is 0.896 (rounded). So the Confidence Interval is 3.348 – 0.896 to 3.348 + 0.896 (finish it off yourself which in the formal answer in a test you MUST do).

Now, the question doesn’t specify whether you need to calculate the Margin of Error ‘by hand’. Let’s do that just for practice. Recall that the M of E for 95% is 1.96 * SE. Here the Standard Error --- SE --- is 2.287/5 = 0.457 (rounded). So the Margin of Error is 1.96*0.457 = 0.896. The same as when we used =confidence. Then you can get the Confidence Interval in the same way....

Chapter 8.13

Chapter 8.13

Note that the sample size is only 8 and you don’t know whether the population from which the sample is drawn is normally distributed or not. In this situation use the t distribution. Put the numbers into Excel and then find the margin of error using the t distribution.....go to data analysis> descriptive stats and it is the number at the bottom of the table.

My results are:

Column1

Mean 10
Standard Error 1.224745
Median 10.5
Mode #N/A
Standard Deviation 3.464102
Sample Variance 12
Kurtosis -1.04286
Skewness -0.16496
Range 10
Minimum 5
Maximum 15
Sum 80
Count 8
Confidence Level(95.0%) 2.896061

So for 13 (a) xbar is 10; for (b) 3.464, and (c) is 2.896. The last question is the Confidence Interval. That’s easy: 10 – 2.896 and 10 + 2.896....note you MUST actually finish off this little calculation.

Difference between cluster sampling and stratified: think of cluster being used for spatial problems, such as the example about trees with beetles in BC. Stratified is used when we can easily divide the population into homogeneous groups, for example golf-club membership

Friday, August 8, 2008

It really is a Small World

Back in the late 1960s, the American sociologists Stanley Milgram and Jeffrey Travers conducted their well-known experiments of sending letters to a stockbroker in Boston---but via unknown intermediaries. They found that the letters took on average just over six hops to reach the stockbroker, a result that has achieved an almost iconic status. In 1990, John Guare wrote a play called Six Degrees of Separation, and a film followed. But is there any truth to the six degrees? After all, Milgram and Jeffrey sent out 296 letters, and only 64 reached the Boston stockbroker.

To test the proposition, Jure Leskovec and Eric Horvitz recently analysed 30 billion instant messages exchanged by 30 billion people using Microsoft Messenger in various countries. They found an average length of 6.6 hops, vindicating Milgram and Travers. Think what this means: every person reading this article is, on average, only half-a-dozen steps away from the Pope, or if you prefer, Madonna.
This close linkage holds some ideological challenges for those of us brought up to have an individualistic outlook, taught that effort and ability are enough for success. In fact, life isn’t like that, and network connections really do count. Some recent research by Lauren Cohen, Andrea Frazzini and Christopher Malloy on the network connections of mutual fund managers provides a good example. Over the period 1990 to 2006, they matched the educational backgrounds of fund managers with board members and senior officials at publicly traded companies. They were looking at the timing of stock buying and selling, and profits. The results were surprising at many levels, but particularly when a manager was at the same school at the same time and studying the same subject as a senior official. Having such a connection allowed the fund manager to make more than double profit on the stocks with the connection, compared to the unconnected. The authors conclude that ‘connected holdings outperform non-connected holdings in a statistically and economically significant way....’. What adds some human interest is that the timing of fund managers for bad news didn’t equal their exemplary timing when the news was good. The implication is that the old classmates at the corporation enjoyed passing on exciting tips, but kept quiet when there was trouble afoot.

In the mutual fund manager case, the links were probably one-to-one. What if the links are more tenuous, as for example between someone reading this article and Madonna? Actually, the more tenuous the link the better, at least when it comes to getting information, perhaps about a possible job. In 1973, in what is perhaps the best-known research in sociology, Mark Granovetter showed that ‘weak’ ties work best. The rationale is that people with whom you have strong links will have pretty much the same knowledge base as yourself, but people to whom you are only weakly linked will have quite different, and more useful, areas of access. Professor Granovetter, now at Stanford University, is continuing his network research on the phenomenon of Silicon Valley.

Describing ties as ‘weak’ or ‘strong’ implies that they can be quantified, and some recent research by Nicholas Christakis and James Fowler, on the spread of obesity in the United States, uses this fact. They show that an individual’s probability of becoming obese increases dramatically the closer his or her relationship with someone who is already obese. When the tie is strong, or the number of degrees of separation is few, for example between good friends, a person’s chances of becoming obese increased by 57 per cent if she or he had a friend who became obese in a given interval. For siblings, the probability reduces slightly, to 40%. The most interesting finding is that even three degrees of separation increased the probability. The results are statistically very robust, and based on impeccable data. It is not clear why network effects should carry over such long social distances, especially as geographical distance did not affect the results. Social norms no doubt play a role, as perhaps does physiological imitation through the ‘mirror neurons’ in the brain’s frontal lobes. As the two researchers note, ‘even infectious causes of obesity are conceivable’.

Sources
Travers, Jeffrey and Stanley Milgram, 1969 “An Experimental Study of the Small World Problem” Sociometry, Vol 32, No 4, pp 425-443.
Microsoft Report
published in the journal Physics in March 2008, and also as a Microsoft Technical Report in June 2007.
Mutual Fund Managers
Cohen, Frazzini and Malloy paper: published as a National Bureau of Economic Research paper
Weak Ties and Jobs
Mark Granovetter: “The Strength of Weak Ties” American Journal of Sociology,
Obesity
Christakis, Nicholas A, and James H Fowler, 2007 “The Spread of Obesity in a Large Social Network Over 32 Years”. New England Journal of Medicine 357: July 26, 2007

Tuesday, May 13, 2008

How big's your social network?

Recently, there have been three ideas entirely new to me that have affected me profoundly. By profoundly I mean that until I happened to read the scholarly papers containing the ideas, I had no conception that such ideas even existed. It was such a shock that I had to stop reading and allow myself a little recovery time before reading on. And each time it happened I thought of John Keats, writing about his first experience of reading Chapman’s Homer:

Then felt I like some watcher of the skies
When a new planet swims into his ken



The most recent ‘planet’ was a 2003 paper by RA Hill and RIM Dunbar published in the journal Human Nature. They discuss the relative volume of the neocortex of the brain compared to the rest of the brain. Yes, I know that doesn’t sound very promising, but wait! All primates, such as humans, have a neocortex, the brain’s most recent addition. The neocortex is responsible for cognitive skills, such as identifying and recognising friends and foes. Now, what Hill and Dunbar did was to measure the share of the volume of the total brain taken up by the neocortex, primate species by primate species. They then looked at the size of the social group maintained by members of each species. The remarkable thing is that there is a direct, linear relationship between neocortex share and size of social group. Less neocortex means fewer friends, or at least people to socialise with.

The linear relationship predicts that the average human would have a social group size of about 150 people. This sounds like a lot of people to me, but then I’m a bit odd and not many people want to be friends with me. Anyway, the researchers then surveyed people in the UK, finding out the size of their social group through their Christmas-card lists. The result: the average Christmas-card list contained 154 names. Isn’t the fact that the predictions matched reality so closely fascinating? Now, one might want to run this particular survey again, and in other cultures perhaps, using something other than a Christmas-card list, perhaps e-mail recipients. That would be interesting in itself.