In class we differentiated between a sample distribution and a sampling distribution. Afterwards, we took a look at a few examples.
The homework is listed below and the notes are attached.
Notes - Sampling Distributions for Sample Proportions
HW - pgs. 432-434 (1 - 19 odd)
read pgs. 419-430 (Sampling Distributions for Sample Means)
Friday, January 30, 2015
Wednesday, January 28, 2015
Quiz - Probability
In class students took a quiz on probability.
The homework is listed below.
HW -Read pgs. 412-419
The homework is listed below.
HW -Read pgs. 412-419
Monday, January 26, 2015
Probability Review
In class students reviewed advance probability focusing on Chapters 16 and 17 in the textbook. Afterwards, students took a practice quiz.
The practice quiz and answer key are attached below. The homework is listed.
Practice Quiz
Answer Key - Practice Quiz
HW - Study for Quiz
Read Chapter 18
The practice quiz and answer key are attached below. The homework is listed.
Practice Quiz
Answer Key - Practice Quiz
HW - Study for Quiz
Read Chapter 18
Thursday, January 22, 2015
The Normal model to the Rescue (again)
In class we learned the we can use the normal model to determine probabilities for samples that are significantly large by checking the 10% condition and success/failure condition.
The homework is listed below and the notes are attached.
Notes - From the Binomial Distribution to the Normal Model
HW - pgs. 401 - 403 (1, 2, 18, 20, 22, 26, 35)
*Probability Quiz on Wednesday 1/28*
The homework is listed below and the notes are attached.
Notes - From the Binomial Distribution to the Normal Model
HW - pgs. 401 - 403 (1, 2, 18, 20, 22, 26, 35)
*Probability Quiz on Wednesday 1/28*
Tuesday, January 20, 2015
Bernoulli Trials
In class students were introduced to Bernoulli trials which are defined as follows:
1) Two possible outcomes (p = success; q = failure)
2) Probability of Success is constant
3) Trails are independent
In class we looked at geometric probability models (defined by 1 parameter - p) and binomial probability models (defined by 2 parameters - p and n).
The notes and formula sheet is attached below. The homework is listed.
Notes - Bernoulli Trials (1)
HW - pgs. 401 - 403 (7, 9, 11, 15, 17, 19, 21)
Read pgs. 397-399
AP Statistics - Formula Sheet
1) Two possible outcomes (p = success; q = failure)
2) Probability of Success is constant
3) Trails are independent
In class we looked at geometric probability models (defined by 1 parameter - p) and binomial probability models (defined by 2 parameters - p and n).
The notes and formula sheet is attached below. The homework is listed.
Notes - Bernoulli Trials (1)
HW - pgs. 401 - 403 (7, 9, 11, 15, 17, 19, 21)
Read pgs. 397-399
AP Statistics - Formula Sheet
Monday, January 12, 2015
Notes - Continuous Random Variables
In class students learned how to find probabilities for continuous random variables.
The notes are attached and the homework is listed below.
Notes - Continuous Random Variables
HW - pgs. 384-386 (25, 26, 29, 30, 37, 41, 42, 46)
Read pgs. 388-396
**Quiz next Wednesday January 21st
The notes are attached and the homework is listed below.
Notes - Continuous Random Variables
HW - pgs. 384-386 (25, 26, 29, 30, 37, 41, 42, 46)
Read pgs. 388-396
**Quiz next Wednesday January 21st
Tuesday, January 6, 2015
Adding, Multiplying and adding independent variables.
In class we reviewed what happens to the expected value E(x) and standard deviation s(x) when we add/subtract a constant and multiply/divide a constant.
add/subtract a constant...E(x ± c) = E(x) ± c
s(x ± c) = s(x)
multiply/divide a constant...E(ax) = a E(x)
s(ax) = a s(x)
Afterwards, we talked about what happens when we add two independent variables.
adding two (or more) independent variables...E(x1 ± x2) = E(x1) ± E(x2)
Notes - Discrete Random Variables (cont.)
HW - pgs. 384-385 (21, 23, 27, 33, 35, 36)
read pgs. 377-381
add/subtract a constant...E(x ± c) = E(x) ± c
s(x ± c) = s(x)
multiply/divide a constant...E(ax) = a E(x)
s(ax) = a s(x)
Afterwards, we talked about what happens when we add two independent variables.
adding two (or more) independent variables...E(x1 ± x2) = E(x1) ± E(x2)
Notes - Discrete Random Variables (cont.)
HW - pgs. 384-385 (21, 23, 27, 33, 35, 36)
read pgs. 377-381
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