December 1st 2014
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December 2nd 2014
I will be able to: Organize outcomes in a sample space using tree diagrams. Compute the number of ordered arrangements of outcomes using permutations. Compute number of (nonordered) groupings of outcomes using combinations. Explain how counting techniques relate to probability in everyday life. |
December 3rd 2014
Finish By The end of Class time!! If not assignment is for Homework. I will be able to: Organize outcomes in a sample space using tree diagrams. Compute the number of ordered arrangements of outcomes using permutations. Compute number of (nonordered) groupings of outcomes using combinations. Explain how counting techniques relate to probability in everyday life. |
December 4th 2014
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December 5th 2014
Continue working on the review/ Review with partner for Chapter 4 Test for MONDAY!!
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December 8th 2014
Warm-up: NONE
Chapter 4 Test
December 9th 2014
Warm-up: Read through pg. 168-169 with the class
Pre-Test on Chapter 5
Notes 5.1 (Introduction to Random Variables and Probability Distributions)
Students will be able to:
distinguish between discrete and continuous random variables.
Graph discrete probability distributions
compute mean for a discrete probability distribution'
compute mean for a linear function of a random variable x
compute mean for a linear combination of two independent random variables.
Success criteria:
clickers to respond to pre-test
Q&A
Guided Practice
Notes will be provided
December 10th 2014
Warm-up: Recap of section 5.1 ( Video)/ review notes and questions
In-Class: pg. 178-182 #'s 1-17 odds
(Introduction to Random Variables and Probability Distributions)
Students will be able to:
distinguish between discrete and continuous random variables.
Graph discrete probability distributions
compute mean for a discrete probability distribution'
compute mean for a linear function of a random variable x
compute mean for a linear combination of two independent random variables.
Success criteria:
Q&A
Guided Practice
Notes will be provided
Students pair up to work on problems
December 11th 2014
Warm-up: none
Notes 5.2 (Binomial Probabilities)
I will be able to:
List the defining features of a binomial experiment
Compute binomial probabilities using the formula P(r)= c(n,r)p^(r)q^(n-r)
Use the binomial table to find P(r)
Use the binomial probability distribution to solve real-world applications.
Success criteria:
Q&A
Guided Practice
Notes will be provided
December 12th 2014
Warm-up: Video recap on section 5.2/ hand-out review and questions
In-Class: pg. 191-196 1-24 odds
I will be able to:
List the defining features of a binomial experiment
Compute binomial probabilities using the formula P(r)= c(n,r)p^(r)q^(n-r)
Use the binomial table to find P(r)
Use the binomial probability distribution to solve real-world applications.
Success criteria:
Q&A
Guided Practice
Notes will be provided
video
Warm-up: NONE
Chapter 4 Test
December 9th 2014
Warm-up: Read through pg. 168-169 with the class
Pre-Test on Chapter 5
Notes 5.1 (Introduction to Random Variables and Probability Distributions)
Students will be able to:
distinguish between discrete and continuous random variables.
Graph discrete probability distributions
compute mean for a discrete probability distribution'
compute mean for a linear function of a random variable x
compute mean for a linear combination of two independent random variables.
Success criteria:
clickers to respond to pre-test
Q&A
Guided Practice
Notes will be provided
December 10th 2014
Warm-up: Recap of section 5.1 ( Video)/ review notes and questions
In-Class: pg. 178-182 #'s 1-17 odds
(Introduction to Random Variables and Probability Distributions)
Students will be able to:
distinguish between discrete and continuous random variables.
Graph discrete probability distributions
compute mean for a discrete probability distribution'
compute mean for a linear function of a random variable x
compute mean for a linear combination of two independent random variables.
Success criteria:
Q&A
Guided Practice
Notes will be provided
Students pair up to work on problems
December 11th 2014
Warm-up: none
Notes 5.2 (Binomial Probabilities)
I will be able to:
List the defining features of a binomial experiment
Compute binomial probabilities using the formula P(r)= c(n,r)p^(r)q^(n-r)
Use the binomial table to find P(r)
Use the binomial probability distribution to solve real-world applications.
Success criteria:
Q&A
Guided Practice
Notes will be provided
December 12th 2014
Warm-up: Video recap on section 5.2/ hand-out review and questions
In-Class: pg. 191-196 1-24 odds
I will be able to:
List the defining features of a binomial experiment
Compute binomial probabilities using the formula P(r)= c(n,r)p^(r)q^(n-r)
Use the binomial table to find P(r)
Use the binomial probability distribution to solve real-world applications.
Success criteria:
Q&A
Guided Practice
Notes will be provided
video
Dec. 15th 2014
Warm-up: Pass Back Quiz from Friday (5.1) Introduction to Random Variables and Probability Distributions
Notes 5.2 (Binomial Probabilities) I will be able to: List the defining features of a binomial experiment . Compute binomial probabilities using the formula p(r)=C(n,r)p^rq^(n-r). Use the binomial table to find P(r). Use the binomial probability distribution to solve real-world applications. Success Criteria: Q&A Guided Practice Notes are given |
Dec. 16th 2014
Warm-up: Hand-Out review over section 5.2 (Binomial Probabilities) In-Class: pg. 191-196 #'s 1-23 odds Students will work together to work on the problems with teachers assistance. I will be able to: List the defining features of a binomial experiment . Compute binomial probabilities using the formula p(r)=C(n,r)p^rq^(n-r). Use the binomial table to find P(r). Use the binomial probability distribution to solve real-world applications. Success Criteria: Q&A Guided Practice Notes are given |
Dec. 17th 2014
Warm-up: take out book to continue working on yesterdays assignment with teachers help. Go through the |
Dec. 18th 2014
Warm-up: NONE
VIDEO over Section 5.3 (Additional Properties of the Binomial Distribution ) Students will take notes over the video. Hand-Out will be given to students after video with exercise problems on it. I will be able to: make histograms for binomial distributions, compute mean and standard deviation for a binomial distribution , compute the minimum number of trials n needed to achieve a given probability of success P(r). |
Dec. 19th 2014 Warm-up: Check Homework from yesterday! In-Class: pg. 203-208 #'s 1-23 odds Quiz 5.1-5.2 |
January 5th 2015
Warm-up: video clips over Section 5.3 (recap from before break)
Work on Problems from section 5.3 with students. pg. 203-208 #'s 3-23 odds. Guiding students on how to work out the problems from section 5.3 I will be able to: Make histograms for binomial distributions. Compute the mean and standard deviation for a binomial distribution, computer the minimum number of trials n needed to achieve a given probability of success P(r). Success Criteria: Q&A Guided Practice |
January 6th 2015
Warm-up: NONE Video over Section 5.4 Students will take notes over the video work on problems with students on pg. 217-222 I will be able to: solve the geometric and Poisson probability distributions Success Criteria: Q&A Guided Practice Video 5.4 |
Jan. 7th 2015
Warm-up: NONe Chapter Review over Chapter 5 pg. 226-229 1-20 all I will be able to solve questions from chapter 5 |
Jan. 8th 2015
Warm-up:
Continue working on chapter 5 Review TEST CHAPTER 5 TOMORROW!! |
Jan. 9th 2015 Chapter 5 TEST Pass out review of MIDTERM!! |
January 12th 2015
Warm-up: None
Review for MIDTERM Chapters 1-5 all I will be able to solve problems that relate to chapters 1-5. |
January 13th 2015
Warm-up: None Review for MIDTERM Chapters 1-5 all I will be able to solve problems that relate to chapters 1-5. |
January 14th 2015
MIDTERM |
January 15 2015
|
Jan. 16th 2015 |
January 19th 2015
No School
January 20th 2015
warm-up: Pre-Test over the second semester
Students will read over section 6.1 ( Graphs of Normal Probability Distributions)
Notes over Section 6.1 ( Graphs of Normal Probability Distributions)
I will be able to:
Graph a normal curve and summarize its important properties
Apply the empirical rule to solve real-world problems
Use control limits to construct control charts. Examine the chart for three possible out-of-control signals.
Success Criteria:
Q&A
Guided Practice
January 21st 2015
warm-up: Read through pg. 234-236 (Normal Distributions) Preview Questions and Focus Problems
Notes over Section 6.1 ( Graphs of Normal Probability Distributions)
I will be able to:
Graph a normal curve and summarize its important properties
Apply the empirical rule to solve real-world problems
Use control limits to construct control charts. Examine the chart for three possible out-of-control signals.
Success Criteria:
Q&A
Guided Practice
January 22nd 2015
warm-up: None
Students will receive a hand-out over section 6.1 ( Graphs of Normal probability Distributions)
Read together as a class. Students will pair up to work on the hand-out. Due at the end of the hour.
I will be able to:
Graph a normal curve and summarize its important properties
Apply the empirical rule to solve real-world problems
Use control limits to construct control charts. Examine the chart for three possible out-of-control signals.
Success Criteria:
Q&A
Guided Practice
January 23rd 2015
warm-up: None
Quiz Monday over section 6.1 ( Graphs of Normal Probability Distributions)
In-class Review over section 6.1
pg. 244-248 #'s 1-15 odds
I will be able to:
Graph a normal curve and summarize its important properties
Apply the empirical rule to solve real-world problems
Use control limits to construct control charts. Examine the chart for three possible out-of-control signals.
Success Criteria:
Q&A
Guided Practice
No School
January 20th 2015
warm-up: Pre-Test over the second semester
Students will read over section 6.1 ( Graphs of Normal Probability Distributions)
Notes over Section 6.1 ( Graphs of Normal Probability Distributions)
I will be able to:
Graph a normal curve and summarize its important properties
Apply the empirical rule to solve real-world problems
Use control limits to construct control charts. Examine the chart for three possible out-of-control signals.
Success Criteria:
Q&A
Guided Practice
January 21st 2015
warm-up: Read through pg. 234-236 (Normal Distributions) Preview Questions and Focus Problems
Notes over Section 6.1 ( Graphs of Normal Probability Distributions)
I will be able to:
Graph a normal curve and summarize its important properties
Apply the empirical rule to solve real-world problems
Use control limits to construct control charts. Examine the chart for three possible out-of-control signals.
Success Criteria:
Q&A
Guided Practice
January 22nd 2015
warm-up: None
Students will receive a hand-out over section 6.1 ( Graphs of Normal probability Distributions)
Read together as a class. Students will pair up to work on the hand-out. Due at the end of the hour.
I will be able to:
Graph a normal curve and summarize its important properties
Apply the empirical rule to solve real-world problems
Use control limits to construct control charts. Examine the chart for three possible out-of-control signals.
Success Criteria:
Q&A
Guided Practice
January 23rd 2015
warm-up: None
Quiz Monday over section 6.1 ( Graphs of Normal Probability Distributions)
In-class Review over section 6.1
pg. 244-248 #'s 1-15 odds
I will be able to:
Graph a normal curve and summarize its important properties
Apply the empirical rule to solve real-world problems
Use control limits to construct control charts. Examine the chart for three possible out-of-control signals.
Success Criteria:
Q&A
Guided Practice
January 26 2015
Warm-up: NONE
Notes 6.2 ( Standard Units and Areas Under the Standard Normal Distribution) I will be able to: Convert raw data to z scores given the mean and Standard Deviation. Success Criteria: Q&A Guided Practice Notes |
January 27th 2015
Warm-up: None Students will work on Problems from Section 6.2(Standard Units and Areas under the Standard Normal Distribution) pg. 256-258 #'s 1-47 odds I will be able to: Convert raw data to z scores given the mean and Standard Deviation. Success Criteria: Q&A Guided Practice Notes |
January 28th 2015
Warm-up: NONE Notes 6.3 (Areas Under Any Normal curve) I will be able to: Compute the probability of standardized events, Find a z score from a given normal probability (inverse normal) and use the inverse normal to solve guarantee problems. Success Criteria: Q&A Guided Practice Notes |
January 29th 2015
Students will work on Problems from Section 6.3 ( Areas Under Any Normal Curve ) In-Class: pg. 267-273 1-39 Odds I will be able to: Compute the probability of standardized events, Find a z score from a given normal probability (inverse normal) and use the inverse normal to solve guarantee problems. Success Criteria: Q&A Guided Practice Notes |
January 30th 2015 QUIZ Over Sections 6.1-6.3 |
FEB. 2nd 2015
Warm-up: What you learned from reading Section 6.3 (Areas Under Normal Curve)
Notes 6.3 (Areas Under Any Normal curve)
Notes will be provided to the students
In-class: pg. 267-272 1-4 all, 5-39 odds
I will be able to: Compute the probability of standardized events, Find a z score from a given normal probability (inverse normal) and use the inverse normal to solve guarantee problems.
Success Criteria:
Q&A
Guided Practice
Notes
FEB. 3rd 2015
warm-Up: none
Students will work on the in-class assignment was assigned to them yesterday. Pg. 267-272 1-4 all, 5-39 odds
I will be able to: Compute the probability of standardized events, Find a z score from a given normal probability (inverse normal) and use the inverse normal to solve guarantee problems.
Success Criteria:
Q&A
Guided Practice
Notes
FEB. 4th 2015
Warm-up: NONE
Notes 6.4 ( Normal Approximation to the Binomial Distribution)
notes will be provided
I will be able to:
State the assumptions needed to use the normal approximation to the binomial distribution. Compute Mean and Standard deviation for the normal approximation. Use the continuity correction to convert a rang of r values to a corresponding range of normal x values. Convert the x values to a range of standardized z scores and find desired probabilities.
Success Criteria:
Q&A
Guided Practice
Notes
FEB 5th 2014
Warm-up: NoNE
in-class assignment over the reading/ notes yesterday (6.4) Normal Approximation to the Binomial Distribution.
pg. 278-280 #'s 5-13 odds
With students and teachers assistance.
I will be able to:
State the assumptions needed to use the normal approximation to the binomial distribution. Compute Mean and Standard deviation for the normal approximation. Use the continuity correction to convert a rang of r values to a corresponding range of normal x values. Convert the x values to a range of standardized z scores and find desired probabilities.
Success Criteria:
Q&A
Guided Practice
Notes
FEB 6th 2015
QUIZ 6.3-6.4
Feb. 9th 2015
Warm-up: Take out books
INDEPENDENT LEARNING DAY
Students will read through section 6.4 ( Normal Approximation to the Binomial Distribution) pg. 273
Students will take notes over the section 6.4
Teacher will start on even problems to show students how to do problems from this section.
In-Class: pg. 278 #'s 5-13 odds
I will be able to:
State the assumptions needed to use the normal approximation t the binomial distribution. Compute the mean and standard deviation for the normal approximation. Use the continuity corrections to convert a range of r values to a corresponding range of normal x values. Convert the x values to a range of standardized z scores and find desired probabilities.
Success Criteria:
Q&A
Guided Practice
February 10th 2015
Warm-up: take Out assignment from yesterday pg. 278-280 #'s 5-13 odds
CHAPTER 6 TEST Thursday!!
After students finish problems from section 6.4, they are to start on the chapter 6 review
pg. 282-284 #'s 1-25 all
I will be able to answer questions on :
Graphing normal probability distributions, Standards Units and areas under the standard normal distribution, Areas under any normal cure and Normal approximation to the binomial distribution.
February 11th 2015
Finish the Chapter 6 Review from yesterday
February 12th 2015
CHAPTER 6 TEST (NORMAL DISTRIBUTIONS)
February 13th 2015
INDEPENDENT LEARNING DAY
students will read independently section 7.1 (sampling distributions)
pg. 292-298. Students will take notes over the section.
I will be able to:
Review such commonly used terms as random sample, relative frequency, parameter, statistic, and sampling distribution. From raw data, construct a relative frequency distribution for mean values and compare the result to a theoretical sampling distribution.
Success Criteria:
Q&A
Guided Practice.
February 23rd 2015
Warm-up: Take out book
INDEPENDENT LEARNING DAY (7.1) (Sampling Distributions)
pg. 292-298
students will read through section 7.1 and take notes
I will be able to:
Review such commonly used terms as random sample, relative frequency, parameter, statistic, and sampling distribution. From raw data, construct a relative frequency distribution for mean values and compare the result to a theoretical sampling distribution.
Success Criteria:
Q&A
Guided Practice.
February 24th 2015
Warm-up: open book to pg. 298
Students will work on problems from section 7.1 ( Sampling Distributions)
pg. 298-299 #'s 1-9 all
I will be able to:
Review such commonly used terms as random sample, relative frequency, parameter, statistic, and sampling distribution. From raw data, construct a relative frequency distribution for mean values and compare the result to a theoretical sampling distribution.
Success Criteria:
Q&A
Guided Practice.
Partners
February 25th 2015
Warm-up: take out book
INDEPENDENT LEARNING DAY - (section 7.2 ) The Central Limit Theorem
Students will read through section 7.2 and take notes
I will be able to:
For a normal distribution use mean and standard deviation to construct the theoretical sampling distribution for the statistic mean. for large samples, use sample estimates to construct a good approximate sampling distribution for the statistic mean. Learn the statement and underlying meaning of the central limit theorem well enough to explain it to a friend who is intelligent, but (unfortunately) doesn't know much about statistics.
Success Criteria:
Q&A
Guided Practice
February 26th 2015
Warm-up: turn to page 306 in the text book
Teacher will go through problems from yesterdays section 7.2
In-Class: pg. 306-307 #'s 3-17 odds
I will be able to:
For a normal distribution use mean and standard deviation to construct the theoretical sampling distribution for the statistic mean. for large samples, use sample estimates to construct a good approximate sampling distribution for the statistic mean. Learn the statement and underlying meaning of the central limit theorem well enough to explain it to a friend who is intelligent, but (unfortunately) doesn't know much about statistics.
Success Criteria:
Q&A
Guided Practice
February 27th 2015
QUIZ SECTIONS 7.1-7.2
March 2nd 2015
Warm-up: Hand-Out Review over section 7.2
INDEPENDENT READING DAY over section 7.3 ( Sampling Distributions for proportions )
Students are to take notes over section 7.2 and problems pg. 317 #'s 1-4 all
I will be able to:
Compute the mean and standard deviation for the sample proportion p hat = r/n
Use the normal approximation to compute probabilities for proportions p hat=r/n
Construct p-charts and interpret their meaning.
Success Criteria:
Q&A
Partners
March 3rd 2015
NO CLASS
March 4th 2015
NO CLASS
March 5th 2015
Warm-up: Take out notebook
Students will watch a video over section 7.3 ( Sampling Distribution of proportions)
Students will take notes over video
I will be able to:
Compute the mean and standard deviation for the sample proportion p hat = r/n
Use the normal approximation to compute probabilities for proportions p hat=r/n
Construct p-charts and interpret their meaning.
Success Criteria:
Q&A
Partners
March 6th 2015
Warn-up: NONE
Students will work on practice problems from section 7.3
pg. 317-320 #'s 5-13 odds
I will be able to:
Compute the mean and standard deviation for the sample proportion p hat = r/n
Use the normal approximation to compute probabilities for proportions p hat=r/n
Construct p-charts and interpret their meaning.
Success Criteria:
Q&A
Partners
Warm-up: What you learned from reading Section 6.3 (Areas Under Normal Curve)
Notes 6.3 (Areas Under Any Normal curve)
Notes will be provided to the students
In-class: pg. 267-272 1-4 all, 5-39 odds
I will be able to: Compute the probability of standardized events, Find a z score from a given normal probability (inverse normal) and use the inverse normal to solve guarantee problems.
Success Criteria:
Q&A
Guided Practice
Notes
FEB. 3rd 2015
warm-Up: none
Students will work on the in-class assignment was assigned to them yesterday. Pg. 267-272 1-4 all, 5-39 odds
I will be able to: Compute the probability of standardized events, Find a z score from a given normal probability (inverse normal) and use the inverse normal to solve guarantee problems.
Success Criteria:
Q&A
Guided Practice
Notes
FEB. 4th 2015
Warm-up: NONE
Notes 6.4 ( Normal Approximation to the Binomial Distribution)
notes will be provided
I will be able to:
State the assumptions needed to use the normal approximation to the binomial distribution. Compute Mean and Standard deviation for the normal approximation. Use the continuity correction to convert a rang of r values to a corresponding range of normal x values. Convert the x values to a range of standardized z scores and find desired probabilities.
Success Criteria:
Q&A
Guided Practice
Notes
FEB 5th 2014
Warm-up: NoNE
in-class assignment over the reading/ notes yesterday (6.4) Normal Approximation to the Binomial Distribution.
pg. 278-280 #'s 5-13 odds
With students and teachers assistance.
I will be able to:
State the assumptions needed to use the normal approximation to the binomial distribution. Compute Mean and Standard deviation for the normal approximation. Use the continuity correction to convert a rang of r values to a corresponding range of normal x values. Convert the x values to a range of standardized z scores and find desired probabilities.
Success Criteria:
Q&A
Guided Practice
Notes
FEB 6th 2015
QUIZ 6.3-6.4
Feb. 9th 2015
Warm-up: Take out books
INDEPENDENT LEARNING DAY
Students will read through section 6.4 ( Normal Approximation to the Binomial Distribution) pg. 273
Students will take notes over the section 6.4
Teacher will start on even problems to show students how to do problems from this section.
In-Class: pg. 278 #'s 5-13 odds
I will be able to:
State the assumptions needed to use the normal approximation t the binomial distribution. Compute the mean and standard deviation for the normal approximation. Use the continuity corrections to convert a range of r values to a corresponding range of normal x values. Convert the x values to a range of standardized z scores and find desired probabilities.
Success Criteria:
Q&A
Guided Practice
February 10th 2015
Warm-up: take Out assignment from yesterday pg. 278-280 #'s 5-13 odds
CHAPTER 6 TEST Thursday!!
After students finish problems from section 6.4, they are to start on the chapter 6 review
pg. 282-284 #'s 1-25 all
I will be able to answer questions on :
Graphing normal probability distributions, Standards Units and areas under the standard normal distribution, Areas under any normal cure and Normal approximation to the binomial distribution.
February 11th 2015
Finish the Chapter 6 Review from yesterday
February 12th 2015
CHAPTER 6 TEST (NORMAL DISTRIBUTIONS)
February 13th 2015
INDEPENDENT LEARNING DAY
students will read independently section 7.1 (sampling distributions)
pg. 292-298. Students will take notes over the section.
I will be able to:
Review such commonly used terms as random sample, relative frequency, parameter, statistic, and sampling distribution. From raw data, construct a relative frequency distribution for mean values and compare the result to a theoretical sampling distribution.
Success Criteria:
Q&A
Guided Practice.
February 23rd 2015
Warm-up: Take out book
INDEPENDENT LEARNING DAY (7.1) (Sampling Distributions)
pg. 292-298
students will read through section 7.1 and take notes
I will be able to:
Review such commonly used terms as random sample, relative frequency, parameter, statistic, and sampling distribution. From raw data, construct a relative frequency distribution for mean values and compare the result to a theoretical sampling distribution.
Success Criteria:
Q&A
Guided Practice.
February 24th 2015
Warm-up: open book to pg. 298
Students will work on problems from section 7.1 ( Sampling Distributions)
pg. 298-299 #'s 1-9 all
I will be able to:
Review such commonly used terms as random sample, relative frequency, parameter, statistic, and sampling distribution. From raw data, construct a relative frequency distribution for mean values and compare the result to a theoretical sampling distribution.
Success Criteria:
Q&A
Guided Practice.
Partners
February 25th 2015
Warm-up: take out book
INDEPENDENT LEARNING DAY - (section 7.2 ) The Central Limit Theorem
Students will read through section 7.2 and take notes
I will be able to:
For a normal distribution use mean and standard deviation to construct the theoretical sampling distribution for the statistic mean. for large samples, use sample estimates to construct a good approximate sampling distribution for the statistic mean. Learn the statement and underlying meaning of the central limit theorem well enough to explain it to a friend who is intelligent, but (unfortunately) doesn't know much about statistics.
Success Criteria:
Q&A
Guided Practice
February 26th 2015
Warm-up: turn to page 306 in the text book
Teacher will go through problems from yesterdays section 7.2
In-Class: pg. 306-307 #'s 3-17 odds
I will be able to:
For a normal distribution use mean and standard deviation to construct the theoretical sampling distribution for the statistic mean. for large samples, use sample estimates to construct a good approximate sampling distribution for the statistic mean. Learn the statement and underlying meaning of the central limit theorem well enough to explain it to a friend who is intelligent, but (unfortunately) doesn't know much about statistics.
Success Criteria:
Q&A
Guided Practice
February 27th 2015
QUIZ SECTIONS 7.1-7.2
March 2nd 2015
Warm-up: Hand-Out Review over section 7.2
INDEPENDENT READING DAY over section 7.3 ( Sampling Distributions for proportions )
Students are to take notes over section 7.2 and problems pg. 317 #'s 1-4 all
I will be able to:
Compute the mean and standard deviation for the sample proportion p hat = r/n
Use the normal approximation to compute probabilities for proportions p hat=r/n
Construct p-charts and interpret their meaning.
Success Criteria:
Q&A
Partners
March 3rd 2015
NO CLASS
March 4th 2015
NO CLASS
March 5th 2015
Warm-up: Take out notebook
Students will watch a video over section 7.3 ( Sampling Distribution of proportions)
Students will take notes over video
I will be able to:
Compute the mean and standard deviation for the sample proportion p hat = r/n
Use the normal approximation to compute probabilities for proportions p hat=r/n
Construct p-charts and interpret their meaning.
Success Criteria:
Q&A
Partners
March 6th 2015
Warn-up: NONE
Students will work on practice problems from section 7.3
pg. 317-320 #'s 5-13 odds
I will be able to:
Compute the mean and standard deviation for the sample proportion p hat = r/n
Use the normal approximation to compute probabilities for proportions p hat=r/n
Construct p-charts and interpret their meaning.
Success Criteria:
Q&A
Partners
March 9th 2015
Warm-up: Take out your book turn to page 321
Students will review over chapter 7 (Introduction to Sampling Distributions)
students will work in groups to work on problems pg. 321*-322 #'s 1-9 all
I will be able to :
solve problems dealing with sampling distributions, the central limit theorem and sampling distributions for proportions.
Success Criteria:
Q&A
Partners
March 10th 2015
students will continue working on the chapter 7 review from yesterday
Students will review over chapter 7 (Introduction to Sampling Distributions)
students will work in groups to work on problems pg. 321*-322 #'s 1-9 all
I will be able to :
solve problems dealing with sampling distributions, the central limit theorem and sampling distributions for proportions.
Success Criteria:
Q&A
Partners
March 11th 2015
CHAPTER 7 TEST (Introduction to sampling distributions)
March 12th 2015
Warm-up: turn to page 328
INDEPENDENT LEARNING DAY section 8.1 (Estimation mean when standard deviation is known) reading pg's 330-338 . Students will take notes over section 8.1
I will be able to:
Explain the meaning of confidence level, error of estimate, and critical value. Find the critical value corresponding to a given confidence level. Compute confidence intervals for mean and standard is known. interpret the results.
Compute the sample size to be used for estimating a mean.
Success Criteria:
Guided Practice
Partners
Q&A
March 13th 2015
March 16th 2015
Warm-up: Turn to page 338 and take out a sheet of paper.
Teacher will go through some examples from section 8.1 (
(Estimation mean when standard deviation is known) students are to write down the examples on their sheet of paper.
I will be able to:
Explain the meaning of confidence level, error of estimate, and critical value. Find the critical value corresponding to a given confidence level. Compute confidence intervals for mean and standard is known. interpret the results.
Compute the sample size to be used for estimating a mean.
Success Criteria:
Guided Practice
Partners
Q&A
March 17th 2015
Warm-up: Take Out Assignment from yesterday Section 8.1.
Students are to continue working on the assignment from yesterday
Students will be working in pairs to complete the assignment
I will be able to:
Explain the meaning of confidence level, error of estimate, and critical value. Find the critical value corresponding to a given confidence level. Compute confidence intervals for mean and standard is known. interpret the results.
Compute the sample size to be used for estimating a mean.
Success Criteria:
Guided Practice
Partners
Q&A
March 18th 2015
Warm-up: turn to page 342 Section 8.2 ( Estimating mean when standard deviation is unknown)
INDEPENDENT LEARNING DAY section 8.2 ( Estimating mean when standard deviation is unknown) . Students will take notes over section 8.2
I will be able to:
Learn about degrees of freedom and students t distributions. Find critical values using degrees of freedom and confidence levels. Compute confidence intervals for mean when standard deviation is unknown. What does this information tell you?
Critical thinking:
Q&A
Guided Practice
Partners
March 19th 2015
Warm-up: Turn to page 349 and take out a sheet of paper.
Teacher will go through some examples from section 8.2 (
(Estimation mean when standard deviation is unknown) students are to write down the examples on their sheet of paper.
I will be able to:
Learn about degrees of freedom and students t distributions. Find critical values using degrees of freedom and confidence levels. Compute confidence intervals for mean when standard deviation is unknown. What does this information tell you?
Critical thinking:
Q&A
Guided Practice
Partners
March 20th 2015
Warm-up: Take Out Assignment from yesterday Section 8.2.
Students are to continue working on the assignment from yesterday
Students will be working in pairs to complete the assignment
QUIZ MONDAY OVER SECTIONS 8.1-8.2
I will be able to:
Learn about degrees of freedom and students t distributions. Find critical values using degrees of freedom and confidence levels. Compute confidence intervals for mean when standard deviation is unknown. What does this information tell you?
Critical thinking:
Q&A
Guided Practice
Partners
Warm-up: Take out your book turn to page 321
Students will review over chapter 7 (Introduction to Sampling Distributions)
students will work in groups to work on problems pg. 321*-322 #'s 1-9 all
I will be able to :
solve problems dealing with sampling distributions, the central limit theorem and sampling distributions for proportions.
Success Criteria:
Q&A
Partners
March 10th 2015
students will continue working on the chapter 7 review from yesterday
Students will review over chapter 7 (Introduction to Sampling Distributions)
students will work in groups to work on problems pg. 321*-322 #'s 1-9 all
I will be able to :
solve problems dealing with sampling distributions, the central limit theorem and sampling distributions for proportions.
Success Criteria:
Q&A
Partners
March 11th 2015
CHAPTER 7 TEST (Introduction to sampling distributions)
March 12th 2015
Warm-up: turn to page 328
INDEPENDENT LEARNING DAY section 8.1 (Estimation mean when standard deviation is known) reading pg's 330-338 . Students will take notes over section 8.1
I will be able to:
Explain the meaning of confidence level, error of estimate, and critical value. Find the critical value corresponding to a given confidence level. Compute confidence intervals for mean and standard is known. interpret the results.
Compute the sample size to be used for estimating a mean.
Success Criteria:
Guided Practice
Partners
Q&A
March 13th 2015
March 16th 2015
Warm-up: Turn to page 338 and take out a sheet of paper.
Teacher will go through some examples from section 8.1 (
(Estimation mean when standard deviation is known) students are to write down the examples on their sheet of paper.
I will be able to:
Explain the meaning of confidence level, error of estimate, and critical value. Find the critical value corresponding to a given confidence level. Compute confidence intervals for mean and standard is known. interpret the results.
Compute the sample size to be used for estimating a mean.
Success Criteria:
Guided Practice
Partners
Q&A
March 17th 2015
Warm-up: Take Out Assignment from yesterday Section 8.1.
Students are to continue working on the assignment from yesterday
Students will be working in pairs to complete the assignment
I will be able to:
Explain the meaning of confidence level, error of estimate, and critical value. Find the critical value corresponding to a given confidence level. Compute confidence intervals for mean and standard is known. interpret the results.
Compute the sample size to be used for estimating a mean.
Success Criteria:
Guided Practice
Partners
Q&A
March 18th 2015
Warm-up: turn to page 342 Section 8.2 ( Estimating mean when standard deviation is unknown)
INDEPENDENT LEARNING DAY section 8.2 ( Estimating mean when standard deviation is unknown) . Students will take notes over section 8.2
I will be able to:
Learn about degrees of freedom and students t distributions. Find critical values using degrees of freedom and confidence levels. Compute confidence intervals for mean when standard deviation is unknown. What does this information tell you?
Critical thinking:
Q&A
Guided Practice
Partners
March 19th 2015
Warm-up: Turn to page 349 and take out a sheet of paper.
Teacher will go through some examples from section 8.2 (
(Estimation mean when standard deviation is unknown) students are to write down the examples on their sheet of paper.
I will be able to:
Learn about degrees of freedom and students t distributions. Find critical values using degrees of freedom and confidence levels. Compute confidence intervals for mean when standard deviation is unknown. What does this information tell you?
Critical thinking:
Q&A
Guided Practice
Partners
March 20th 2015
Warm-up: Take Out Assignment from yesterday Section 8.2.
Students are to continue working on the assignment from yesterday
Students will be working in pairs to complete the assignment
QUIZ MONDAY OVER SECTIONS 8.1-8.2
I will be able to:
Learn about degrees of freedom and students t distributions. Find critical values using degrees of freedom and confidence levels. Compute confidence intervals for mean when standard deviation is unknown. What does this information tell you?
Critical thinking:
Q&A
Guided Practice
Partners
March 23rd 2015
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March 24th 2015
Test over Sections 8.1-8.2 |
March 25th 2015
Students will work on practice problems from section 8.3 ( Estimating p in the Binomial Distribution) pg. 362-364 #'s 5-21 odds
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March 26th 2015
Warm-up: turn to pg. 362 and take out your notebook Teacher will go through questions 5-18 evens for review over section 8.3(Estimating p in the Binomial Distribution) INDEPENDENT READING DAY section 8.4 (Estimating mu 1 - mu 2 and p1-p2) I will be able to: Distinguish between independent and dependent samples. Compute confidence intervals for mu 1- mu2 when sigma 1 and sigma 2 are known. Compute confidence intervals for mu1-mu2 when sigma 1 and sigma 2 are unknown. Compute confidence intervals for p1-p2 using the normal approximation. Interpret the meaning and implications of an all-positive, all-neg. or mixed confidence interval. |
March 27th 2015
Warm-up: Turn to page 366 and take out your notebook. Teacher will go through problems form section 8.4 pg.377 1-7 odds. Students will do #'s 9-23 odds I will be able to: Distinguish between independent and dependent samples. Compute confidence intervals for mu 1- mu2 when sigma 1 and sigma 2 are known. Compute confidence intervals for mu1-mu2 when sigma 1 and sigma 2 are unknown. Compute confidence intervals for p1-p2 using the normal approximation. Interpret the meaning and implications of an all-positive, all-neg. or mixed confidence interval. |
March 30th 2015
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March 31st 2015
Students will be reviewing for their chapter 8 test Students will work on problems from pg. 387-392 #'s 1-19 all |
April 1st 2015
Students will be reviewing for their chapter 8 test Students will work on problems from pg. 387-392 #'s 1-19 all |
April 2nd 2015
CHAPTER 8 TEST |
April 3rd
No SCHOOL |
April 20th 2015
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April 21st 2015
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April 22nd 2015
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April 23rd 2015
Warm-up: Take out your notebook and turn to page 431. Teacher will go through practice problems from section 9.3 pg. 437-441 Students will then receive practice problems to work on within their groups. with teachers assistance.
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April 24th 2105
continue with the exercise problems from yesterday with in your group. with teachers assistance section 9.3
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April 13th 2015
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April 14th 2015
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April 15th 2015
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April 16th 2015
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April 17th 2105
quiz 9.1 and 9.2 |
April 13th 2015
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April 14th 2015
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April 15th 2015
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April 16th 2015
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April 17th 2105
quiz 9.1 and 9.2 |
April 27th 2015
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April 28th 2015
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April 29th 2015
Continue working on Assignment from yesterday and review for quiz tomorrow.
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April 30th 2015
Quiz over sections 9.3-9.4 |
May 1st 2105
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May 4th 2015
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May 5th 2015
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May 6th 2015
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May 7th 2015
Review for Chapter 9 Test Students will be working on Notes-Cards for chapter 9 test |
May8th 2105
CHAPTER 9 TEST |
May 11th 2015
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May 12th 2015
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May 6th 2015
I-READY TESTING
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May 7th 2015
Continue on board game design. |
May8th 2105
Continue on board-game design |
May 18th 2015
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May 12th 2015
Review for FINALS !!
Students will work on their review for their final |
May 6th 2015
Review day - FINALS |
May 7th 2015
Review for finals |
May8th 2105
NO School |