http://blogging4biology.edublogs.org/2010/08/28/college-board-lab-files/
BLAST Homepage:
http://blast.ncbi.nlm.nih.gov/Blast.cgi
Entrez Gene Website
http://www.ncbi.nlm.nih.gov/gene
Friday, May 30, 2014
Blast
Gene files:
http://blogging4biology.edublogs.org/2010/08/28/college-board-lab-files/
BLAST Homepage:
http://blast.ncbi.nlm.nih.gov/Blast.cgi
Entrez Gene Website
http://www.ncbi.nlm.nih.gov/gene
http://blogging4biology.edublogs.org/2010/08/28/college-board-lab-files/
BLAST Homepage:
http://blast.ncbi.nlm.nih.gov/Blast.cgi
Entrez Gene Website
http://www.ncbi.nlm.nih.gov/gene
Thursday, May 29, 2014
Trees in Paradise
Listen to this. It's awesome.
http://www.kqed.org/a/forum/R201405260930#disqus_thread
http://www.kqed.org/a/forum/R201405260930#disqus_thread
Wednesday, May 14, 2014
Monday, May 12, 2014
Sunday, May 11, 2014
Q 10
So, this equation is on the given equations for the test, yet it was not part of any of our labs. Watch this video and familiarize your self with Q 10. Feel free to post questions.
http://www.bozemanscience.com/q10-the-temperature-coefficient
http://www.bozemanscience.com/q10-the-temperature-coefficient
Video to Watch this AM
Happy Mother's Day!
https://www.youtube.com/watch?v=tFNJLs-Ql0o
Signal Transduction
http://www.bozemanscience.com/038-signal-transduction-pathways
https://www.youtube.com/watch?v=tFNJLs-Ql0o
Signal Transduction
http://www.bozemanscience.com/038-signal-transduction-pathways
Saturday, May 10, 2014
Bozeman Science Homepage
This is the entire Bozeman collection, organized by the three "Big Ideas" in AP Bio.
http://www.bozemanscience.com/ap-biology/
Watch a few of the Science Practices today
Watch this one today:
http://www.bozemanscience.com/032-signal-transmission-and-gene-expression
http://www.bozemanscience.com/ap-biology/
Watch a few of the Science Practices today
Watch this one today:
http://www.bozemanscience.com/032-signal-transmission-and-gene-expression
Friday, May 9, 2014
Practice Problems and Other Resources (draft)
Hardy Weinberg
http://www.indiana.edu/~l111/handouts/hardy.htm
(the bottom links you to the solutions)
Population Growth
http://netclass.landmark-cs.org/ClassDocuments/5036/loggrowth_answers.pdf
http://www.westonk12-ct.org/file.cfm?resourceid=16123&filename=ExamView_-_pop_growth_practice.pdf
http://www.indiana.edu/~l111/handouts/hardy.htm
(the bottom links you to the solutions)
Population Growth
http://netclass.landmark-cs.org/ClassDocuments/5036/loggrowth_answers.pdf
http://www.westonk12-ct.org/file.cfm?resourceid=16123&filename=ExamView_-_pop_growth_practice.pdf
Thursday, May 8, 2014
Okay, I made a song.
I will sing it for you tomorrow.
It goes to the Adam's Family theme song https://www.youtube.com/watch?v=_YFk4b6yeX4
Here are the lyrics
Verse One:
The data fit (clap clap)
The data fit (clap clap)
When data we collected
Match the unexpected
Our hypothesis is rejected
We don't reject the null.
A chi square that is smaller
Shouldn't make you holler
The null is still the baller
We don't reject the null.
Critical value (clap clap)
Critical value (clap clap)
When data we collected
Match the unexpected
Our hypothesis is rejected
We don't reject the null.
If you can learn this song, then you know, that if the data doesn't fit, (high chi) we reject the null.
It goes to the Adam's Family theme song https://www.youtube.com/watch?v=_YFk4b6yeX4
Here are the lyrics
Verse One:
The data fit (clap clap)
The data fit (clap clap)
When data we collected
Match the unexpected
Our hypothesis is rejected
We don't reject the null.
A chi square that is smaller
Shouldn't make you holler
The null is still the baller
We don't reject the null.
Critical value (clap clap)
Critical value (clap clap)
When data we collected
Match the unexpected
Our hypothesis is rejected
We don't reject the null.
If you can learn this song, then you know, that if the data doesn't fit, (high chi) we reject the null.
Wednesday, May 7, 2014
Chi Square
Sorry for the confusion (it's my fault):
Chi square is a way to measure "goodness of fit" between data based on expected values and data obtained through experiment or calculations ("observed").
How do we know our "expected values?" We use tools like Punnet squares and Hardy Weinberg to give us our expected values. Sometimes our expected values are simply an even distribution (like in the fruit fly behavior example)
What is the null hypothesis? The null hypothesis states that there is no significant difference between the expected and the experimentally derived ("observed") data. In other words, the data "fit."
For example, we think that the coin used in the NFL is unfairly weighted to land on "heads." Our "expected" data is 50/50 heads tails (even though we actually think the coin is rigged). Our null hypothesis is that there will be no difference between the NFL coin and any other coin.
The experimental, or "alternate" hypothesis should describe a situation when the data do not "fit."
(one of the major sources of confusion was the practice problem we did today in class had a hypothesis that predicted a "fit." Do you see why this would jumble up the null with the hypothesis?)
Our alternate hypothesis is that the NFL coin will disproportionately land on "heads."
If the data "fit" nothing significant is happening. The coin is not rigged.
If the data don't "fit" then we need to call the NFL commissioner because the coin is rigged.
Again, we use chi-square to determine the fit. We need to use the Critical Values Table
When the calculated chi-square value exceeds the critical value in the table for a 0.05 p value, then we can reject the null hypothesis. Which means, in the NFL case, the coin is rigged.
If the calculated chi-square is less than the critical value, then we do not reject the null hypothesis, because the data "fit." Which means the coin was not rigged and we are paranoid football fans.
Chi square is a way to measure "goodness of fit" between data based on expected values and data obtained through experiment or calculations ("observed").
How do we know our "expected values?" We use tools like Punnet squares and Hardy Weinberg to give us our expected values. Sometimes our expected values are simply an even distribution (like in the fruit fly behavior example)
What is the null hypothesis? The null hypothesis states that there is no significant difference between the expected and the experimentally derived ("observed") data. In other words, the data "fit."
For example, we think that the coin used in the NFL is unfairly weighted to land on "heads." Our "expected" data is 50/50 heads tails (even though we actually think the coin is rigged). Our null hypothesis is that there will be no difference between the NFL coin and any other coin.
The experimental, or "alternate" hypothesis should describe a situation when the data do not "fit."
(one of the major sources of confusion was the practice problem we did today in class had a hypothesis that predicted a "fit." Do you see why this would jumble up the null with the hypothesis?)
Our alternate hypothesis is that the NFL coin will disproportionately land on "heads."
If the data "fit" nothing significant is happening. The coin is not rigged.
If the data don't "fit" then we need to call the NFL commissioner because the coin is rigged.
Again, we use chi-square to determine the fit. We need to use the Critical Values Table
When the calculated chi-square value exceeds the critical value in the table for a 0.05 p value, then we can reject the null hypothesis. Which means, in the NFL case, the coin is rigged.
If the calculated chi-square is less than the critical value, then we do not reject the null hypothesis, because the data "fit." Which means the coin was not rigged and we are paranoid football fans.
Tuesday, May 6, 2014
Finals Schedule
SPRING
SEMESTER FINALS SCHEDULE
JUNE
3-6, 2014
Tuesday, June 3,
2014
|
Period
|
|
Time
|
|
4 minute passing
time before period 4
|
|
1
|
|
|
(120)
|
|
|
Lunch
|
|
|
|
Instructional
Minutes
|
|
4
|
|
|
(120)
|
244
|
Wednesday,
June 4, 2014
|
Period
|
|
Time
|
|
4 minute passing
time before period 6
|
|
7
|
|
|
(120)
|
|
|
Lunch
|
|
|
|
Instructional
Minutes
|
|
6
|
|
|
(120)
|
244
|
Thursday, June
5, 2014
|
Period
|
|
Time
|
|
4 minute passing
time before period 2
|
|
3
|
|
|
(120)
|
|
|
Lunch
|
|
|
|
Instructional
Minutes
|
|
2
|
|
|
(120)
|
244
|
Friday, June 6,
2014
|
Period
|
|
Time
|
|
4 minute passing
time before period
1, 2, 3, 4, 5, 6, and 7
|
|
5
|
|
|
(120)
|
|
|
Break
|
|
|
|
|
|
1
|
|
|
( 13)
|
|
|
2
|
|
|
( 13)
|
|
|
3
|
|
|
( 13)
|
|
|
4
|
|
|
( 13)
|
|
|
5
|
|
|
( 13)
|
|
|
6
|
|
|
( 13)
|
Total
Instructional Minutes
|
|
7
|
|
|
( 13)
|
239
|
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