Tag Archives: statistics project

Mmm, fresh data!

Hey ladies and gents. NEW BLOG LAYOUT! Do you like it? Please say yes.

Anyway.

So this is some data I collected in my junior year of high school. I asked 100 high schoolers a series of questions out of Keirsey’s Please Understand Me, a book about the 16 temperaments (you know, like the ISFPs or the ENTJs, etc.). When I “analyzed” this for my psych class back then, I didn’t really know any stats at all aside from “I can graph this stuff in Excel!” (which doesn’t even count), so I decided to explore it a little more. I wanted to see if there were any correlations between gender and any of the four preference scales.
The phi coefficient was computed between all pairs (this coefficient is the most appropriate correlation to compute between two dichotomous variables). Here is the correlation matrix:

First, it’s important to note how things were coded.
Males = 1, Females = 0
Extraversion = 1, Introversion = 0
Sensing = 1, Intuiting = 0
Feeling = 1, Thinking = 0
Perceiving = 1, Judging = 0

So what does all this mean? Well, pretty much nothing, statistically-speaking. The only two significant correlations were between gender and Perceiving/Judging and Sensing/Intuiting and Perceiving/Judging. From the coding, the first significant correlation means that in the sample, there’s a tendency for males to score higher on Perceiving than Judging, and for females to score higher on Judging. The second significant correlation  means that in the sample, there’s a tendency for those who score high on Feeling tend to score high on Perceiving, and a tendency for those to score high on Thinking to score high on Judging.

The rest of the correlations were non-significant, but they’re still interesting to look at. There’s a positive correlation between being female and scoring high on Extraversion, There’s a correlation between being male and scoring high on Feeling, and there’s a very, very weak correlation between Feeling/Thinking and Extraversion/Introversion.

Woo stats! Take the test, too, it’s pretty cool.

 

 

Today’s song: Beautiful Life by Ace of Base

Every Time You Misinterpret a Confidence Interval, God Kills a Statistician

HI AGAIN!

So I’m liking WordPress. I’m liking it enough that I’ve started an entirely new blog dedicated to R. It’s right here. It, unlike this one, will actually kind of look professional and will not consist of my daily ramblings. Rather, it will contain info on using R as well as little tricks, ’cause we all know R can be a pain sometimes.

OH YEAH, I also found an old high school project of mine that revolved around quite a nice little dataset. My “statistics” back then were horrendous, so I’m thinking I might re-do the whole thing and post it up here. Plus my graphs were made in Excel and look terrible. That has to be remedied.

WOO SHORT BLOGS! School starts tomorrow, I’m stressed, deal with it.

The things you can learn from I/O research

HI PEOPLE!

So I finally finished my Stat 514 project.

Setup: suppose you’re a prospective employee being interviewed by an individual who will determine what your starting salary for the job is. What would you do to increase your odds of getting a higher salary offer?
We (Dr. Thorsteinson, Tanya and I) designed a study that involved participants reading a script between an employer and a prospective employee and were asked, after reading the script, what they as the employer would offer the employee as a salary. We looked at three different types of anchoring methods that could lead to a higher offer than if there was no anchor offered. An “anchor” is an irrelevant or random number offered in some situation off of which other people tend to reference. For example, if I gave you a jar full of pennies and asked you how many pennies were in it, you might give me any number of answers. But if I said, “I think there are about 400 pennies in here, what about you?” your guess would be somewhere around 400.

Here were our three scenarios used to compare to a control scenario in which no anchoring number was offered:
1) Irrelevant number: the prospective employee mentions some unrelated number, like the number of employees at his last job.
2) Relevant number: the prospective employee mentions a dollar figure, like a previous salary.
3) Joking comment: the prospective employee jokes that he’d like a very large salary, like $1 million.

So what was the conclusion?

The joking comment significantly increased the offered salary over that offered in the control. The relevant number also significantly increased the offered salary, but not as much as the joking comment.

So when you’re getting interviewed and are asked what you’d like your salary to be, be sure to jokingly ask for $1 million.

It’s a good thing I enjoy this.

So it’s like 3 in the morning and I’m finally done with that damn Stat 422 project. So I shall now show you the basic results, minus all the fancy math and such.

Go!

The Number of Classes Offered by Each College
Agricultural and Life Sciences: 522
Art and Architecture: 209
Business and Economics: 188
Education: 606
Engineering: 647
Letters, Arts, and Social Sciences: 1,419
Natural Resources: 344
Science: 591
TOTAL: 4,526 classes offered
Percentage of Classes that Require Prerequisites, by College (estimated and actual, respectively)
Agricultural and Life Sciences: 25%, 21.6%
Art and Architecture: 25%, 24.9% (holy crap, I was so close!)
Business and Economics: 57.1%, 53.2%
Education: 21.7%, 15%
Engineering: 33%, 32.8% (pretty close here)
Letters, Arts, and Social Sciences: 16.7%, 11.7%
Natural Resources: 15.4%, 11.9%
Science: 36%, 36.5%
TOTAL: 25.1%, 21.8%
Percentage of Classes that Require Prerequisites Outside of the Department, by College (estimated and actual, respectively)
Agricultural and Life Sciences: 5%, 9%
Art and Architecture: 12.5%, 1.9% (haha, wow, that’s way off)
Business and Economics: 0%, 17% (this is even worse!)
Education: 8.7%, 3%
Engineering: 20.8%, 14.8%
Letters, Arts, and Social Sciences: 1.9%, 1%
Natural Resources: 7.7%, 7%
Science: 13.6%, 11.2%
TOTAL: 10%, 6.7%
So here are the final results!

The proportion of University of Idaho courses that require prerequisites was estimated to be 25.14% with a variance of .000858935 and a bound of .0586, or 5.86%.

The proportion of University of Idaho courses that require prerequisites outside of the field of the course in question was estimated to be 10.04% with a variance of .000960987 and a bound of .0619 or 6.19%.

With the bounds in place, the results of these estimates basically tell us that a 95% confidence interval for the proportion of courses offered by the University of Idaho that require prerequisites is between 19.28% and 31%, and that a 95% confidence interval for the proportion of courses offered by the University of Idaho that require prerequisites outside of the field of the course in question is between 3.85% and 16.23%.

 

Yay!