What big ideas have you identified in this chapter?
CiCi's Sunday January 23rd. 2-4
Wednesday, January 11, 2006
Monday, December 19, 2005
First Semester Final Exam
We couldn't possibly cover all of the first semester's content in two days of review, so let's point out a few topics that no one has asked about:
Correlation coefficient r and coefficient of determination r-squared. What special insight does the value of r-squared give you about the relationship between x and y?
Why can't we just take the square root of r-squared to get the value of r?
Slope of a LSRL = the estimated increase (or decrease) in the response variable for every unit increase in the explanatory variable.
The easiest way to get it is r*sy/sx where sy is the sample standard deviation of y and sx is the sample standard deviation of x.
While we're talking about sample standard deviations. . . the formula is SQRT(variance of the variable), so the sample standard deviation of x would be
SQRT[(sum of the squares of (Xi - Xbar) for all values of X)/(n-1)]. N is the sample size.
Don't panic if you can't read that--just look up the formula in the text.
What does it mean to be resistant to outliers? Give examples of measures which are resistant. Give examples of some which are not.
What are the benefits of different types of graphs (box and whisker, stem and leaf, histogram)?
How do you know if a set of data is approximately normally distributed? Look it up.
Why do we block?
Why do we experiment?
What makes an experiment special?
What are the characteristics of a well-designed experiment?
Why do people sometimes need double-blind experiments?
What is the placebo effect?
How do you know if two characteristics are independent?
Correlation coefficient r and coefficient of determination r-squared. What special insight does the value of r-squared give you about the relationship between x and y?
Why can't we just take the square root of r-squared to get the value of r?
Slope of a LSRL = the estimated increase (or decrease) in the response variable for every unit increase in the explanatory variable.
The easiest way to get it is r*sy/sx where sy is the sample standard deviation of y and sx is the sample standard deviation of x.
While we're talking about sample standard deviations. . . the formula is SQRT(variance of the variable), so the sample standard deviation of x would be
SQRT[(sum of the squares of (Xi - Xbar) for all values of X)/(n-1)]. N is the sample size.
Don't panic if you can't read that--just look up the formula in the text.
What does it mean to be resistant to outliers? Give examples of measures which are resistant. Give examples of some which are not.
What are the benefits of different types of graphs (box and whisker, stem and leaf, histogram)?
How do you know if a set of data is approximately normally distributed? Look it up.
Why do we block?
Why do we experiment?
What makes an experiment special?
What are the characteristics of a well-designed experiment?
Why do people sometimes need double-blind experiments?
What is the placebo effect?
How do you know if two characteristics are independent?
Thursday, December 15, 2005
Things to think about when you should be studying
The icon used for the command SAVE in Microsoft's Office applications is a 3.5" diskette. Now that diskettes are nearly obsolete, when will they change the icon and what will they change it to?
Tuesday, December 13, 2005
Chapter 8 Binomial and Geometric Probabilities
What are the differences between **having two kids and counting x=the number of girls** and **having kids until you get a girl**? What is the random variable x in the second case? What are the means [expected values] of the random variable x for each of these scenarios? What is the standard deviation of x in the first case? How could you simulate each of these scenarios?
How are these distributions similar? How are they different?
How are these distributions similar? How are they different?
Tuesday, November 29, 2005
Chapter 7 - Random Variables
How do you distinguish between a discrete random variable and a continuous random variable?
Compare and contrast probability histograms and density curves.
If X is discretely distributed for the integers {1, 2, 3} and P(X=1) does not equal P(X=3), does the expected value of X have to be an integer? Why or why not? Does the mode have to be an integer? Why or why not? Does the expected value of a distribution have to be a value of x from your distribution (for instance, does the average number of pips on one die rolled have to be 1, 2, 3, 4, 5, or 6)? Does the mode have to be an observed value of x? Why or why not?
How does the Law of Large Numbers relate to the Kid-sino lab on November 18th?
The mean of the sum is the sum of the means.
The variance of the sum is the sum of the variances (if the variables are independent).
The variance of the difference is the SUM of the variances (if the variables are independent).
Why?
The variance of 2X is 4 times the variance of X.
The variance of (X + Y) is the variance of X plus the variance of Y (if the variables are independent).
Why are these different formulas? Or are they?
Have a super day.
Compare and contrast probability histograms and density curves.
If X is discretely distributed for the integers {1, 2, 3} and P(X=1) does not equal P(X=3), does the expected value of X have to be an integer? Why or why not? Does the mode have to be an integer? Why or why not? Does the expected value of a distribution have to be a value of x from your distribution (for instance, does the average number of pips on one die rolled have to be 1, 2, 3, 4, 5, or 6)? Does the mode have to be an observed value of x? Why or why not?
How does the Law of Large Numbers relate to the Kid-sino lab on November 18th?
The mean of the sum is the sum of the means.
The variance of the sum is the sum of the variances (if the variables are independent).
The variance of the difference is the SUM of the variances (if the variables are independent).
Why?
The variance of 2X is 4 times the variance of X.
The variance of (X + Y) is the variance of X plus the variance of Y (if the variables are independent).
Why are these different formulas? Or are they?
Have a super day.
Thursday, November 17, 2005
Monday, November 07, 2005
Chapter 6 - Probability
Alas, here's your chance to finally learn to like probability. We'll be covering the important stuff and giving you the opportunity to extend your understanding through an optional challenge. The test will be on Thursday, November 17. On Friday, November 18th we will have our annual casino day. We would appreciate adult help on this day, especially from parents who have some experience watching chips pass back to the "house." If you want to design a casino game of chance where you will be the "house" and the students will play against you, see Mrs. L this week.
Please be safe on Tuesday. Good luck to the GHP interviewees. See you all on Wednesday.
Please be safe on Tuesday. Good luck to the GHP interviewees. See you all on Wednesday.
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