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Math 10 Probability
CHAPTER 3 NOTES: Introduction to Probability
http://nebula.deanza.edu/~bloom/Math10/M10_Ch3ProbabilityNotes2009Smr.pdf
CHAPTER 4 NOTES: Discrete Probability Distributions
http://nebula.deanza.edu/~bloom/Math10/M10DiscreteDistrNotes2008F.pdf
Solutions to Ch 4 Homework problems #38, 39, 40
Discrete: http://nebula.deanza.edu/~bloom/Math10/DiscreteProbPractAns.doc
Solutions to Ch 4 Homework problems #43
Geometric: http://nebula.deanza.edu/~bloom/Math10/GeometPractAns.doc
Calculator: Binomial Distribution on the TI 83,83+,84+,86,89:
TI-83 and 84, press 2nd DISTR
TI-86 press 2nd MATH MORE and then press F key corresponding to STAT menu on screen; then press F2:DISTR
TI-89 press APPS; pPress 1: FlashApps; highlight Stats/List Editor press
ENTER F5: Distr
pdf stands for probability distribution function; gives probability P(x = r)
cdf stands for cumulative distribution function; gives probability P(x < r)
Specific instructions are in the table below:
| | TI 83, 84 | TI 86 |
| P(x = r) | binompdf(n,p,r) | bipdf(n,p,r) |
| P(x < r) | binomcdf(n,p,r) | bicdf(n,p,r) |
| P(x < r) | binomcdf(n,p,r - 1) | bicdf(n,p,r - 1) |
| P(x > r) | 1 - binomcdf(n,p,r) | 1 - bicdf(n,p,r) | |
| P(x > r) | 1 - binomcdf(n,p,r - 1) | 1 - bicdf(n,p,r - 1) | |
OPTIONAL REVIEW: ADDITIONAL MIXED PRACTICE PROBLEMS
for Probability Rules, Discrete, Binomial,Geometric Distributions:
Probability Practice Problems (Carol's Sweet Shop)
http://facultyfiles.deanza.edu/gems/bloomroberta/Carolsweetshop.doc
Problems 1 thru 8 are for using probability rules (Ch 3)
Problem 28 is a tree (Ch 3)
Problems 9 thru 16 are contingency tables(Ch 3 Illowsky/Dean)
Problems 22 thru 24 are discrete probability distributions (Ch 4)
Problems 19 thru 21 & 25 thru 27 are binomial distribution (Ch 4)
Problems 17 thru 18 are geometric distribution
(There are no Poisson practice problems.)
Solutions:
http://nebula.deanza.edu/~bloom/Math10/M10sweetshopans1.GIF
http://nebula.deanza.edu/~bloom/Math10/M10sweetshopans2.GIF
http://nebula.deanza.edu/~bloom/Math10/M10sweetshopans3.GIF
http://nebula.deanza.edu/~bloom/Math10/M10sweetshopans4.GIF
Ch. 5 UNIFORM Distribution
There are no webnotes; take notes in class & read the textbook.
Additional example: http://nebula.deanza.edu/~bloom/Math10/M10UniformDistrExample.pdf
Ch. 5 EXPONENTIAL Distribution
There are no webnotes; take notes in class & read the textbook.
Additional example: http://sofia.fhda.edu/gallery/statistics/lessons/lesson05-3.html
CH. 6 NORMAL Distribution Worksheet
http://nebula.deanza.edu/~bloom/Math10/M10NormalDistWorksheet2008F.pdf
CH. 7 CENTRAL LIMIT THEOREM
Ch. 7 Notes http://nebula.deanza.edu/~bloom/Math10/M10CLTNotesF2008.pdf
Videos of Central Limit Theorem problems: Probability for sample means (averages)
Original Distribution Unknown, mu, sigma given
http://www.deanza.edu/bmc/ppts/clt71/#
Original Distribution Uniform, you need to calculate mu and sigma:
http://www.deanza.edu/bmc/ppts/clt72/#
Graphical Example of the Central Limit Theorem
See how the shape of the distribution of sample means becomes becomes more normally
distributed and more concentrated about the mean as the sample size increases
http://www.statisticalengineering.com/central_limit_theorem.htm
UNIFORM, EXPONENTIAL, NORMAL, NORMAL with CLT
Continuous Distributions Summary
Table summarizing key points about problems involving the Uniform, Exponential,
Normal, and Normal with CLT distributions
Return to Resources Page: http://faculty.deanza.edu/bloomroberta/stories/storyReader$101 |
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