00:09:57	Ekua-Yaaba Monkah:	Long week!
00:10:04	Anai Ramos:	Good! A lot of hw
00:10:06	Silvana Larrea:	Hi, good :)
00:10:15	Julia Hankin:	Doing okay! Lots going on, Epi midterm on Monday
00:10:22	Nikki Marucut:	How are you doing?
00:10:55	Nikki Marucut:	Is that phn250A?
00:11:04	Cristal Escamilla:	yes
00:11:06	Nikki Marucut:	That y’all are talking about
00:11:09	Nikki Marucut:	GOOD LUCK
00:11:21	Cristal Escamilla:	Thank you!
00:11:27	Nikki Marucut:	I took it in the summer, it’s hard
00:11:31	Nikki Marucut:	And I feel for yall
00:11:35	Nikki Marucut:	You got this!
00:14:21	Nikki Marucut:	Are we going to get access to these slides?
00:18:03	Nikki Marucut:	Thank you!
00:20:03	Ijeoma Uche:	The last 10 .. z-score and on
00:20:09	Laura Sanavio:	Question 2
00:20:39	Annalisa Watson (she/her):	#10 & 11, then zscore and on
00:20:51	Arianna Libenson:	3
00:21:10	Ijeoma Uche:	#9
00:21:16	Michele Ko (she/her):	Agree with z score and on
00:21:24	Ijeoma Uche:	#11
00:21:27	Nikki Marucut:	Question 8 and 9
00:21:37	Ijeoma Uche:	#14
00:21:43	Julia Hankin:	Just to make sure I understand, for Part 1 we aren’t using any R functions to calculate, correct? Just using R functions for Part 2?
00:22:29	Ijeoma Uche:	#2, #9, #11, #14, and the last 10 (z-score and on)
00:23:52	Kelsey MacCuish:	@Julia right! there’s some math for the first part, but the code really comes in in the last part
00:24:02	Kelsey MacCuish:	(and also with uploading your tree diagram)
00:24:06	Kelsey MacCuish:	(in the screening part)
00:25:14	Aliza Adler:	I’d vote for going over probability and then z score s(differences between pnorm, qnorm, and rnorm)
00:25:42	Ekua-Yaaba Monkah:	#6, #9, Normal Distribution, and Z score questions
00:26:18	Lian Hsiao:	Can we go over a bit of every part so we get an idea of how everything looks
00:26:46	Silvana Larrea:	If possible I would like to review 5,6 and 7 :)
00:26:54	Kelsey MacCuish:	yes we definitely will go over at least a little of every part! Just wanted to know where to start bc we didn’t get to spend as much time on normal dist in my other lab
00:27:38	Ijeoma Uche:	Is it possible to access the recordings to the other lab?
00:27:59	Annalisa Watson (she/her):	I think only this lab section is recorded^
00:28:51	Laura Sanavio:	When is the recording usually posted? I will not be able to stay the full time because I have another class after
00:31:16	Mariah Jiles (she/her):	above the mean? right side of graph
00:31:17	Hiruni Jayasekera (she/her):	between 0 and 1
00:31:19	Taylor Yoo:	to the right of the mean
00:31:25	Nikki Marucut:	It’s about 2-2.5 away from the mean?
00:33:57	Aliza Adler:	Doesn’t it say it differently in the lab though - it says qnorm you input a probability?
00:34:21	Nani Conklin (she/her):	same question - I have in my notes that we use pnorm to find the probability of a point on the curve
00:35:03	Hiruni Jayasekera (she/her):	the notes say pnorm() uses the value of x and calculates the probability below that point
00:36:45	Phoenix Ding:	I think qnorm is given probability area, while pnorm is to calculate probability area
00:37:08	Aliza Adler:	^that tracks with my notes on pnorm from class too
00:39:16	Nani Conklin (she/her):	we haven't actually learned qnorm in class yet :/ here's the explanation of pnorm from the lecture yesterday:
00:39:37	Nani Conklin (she/her):	pnorm(q = 1.2, mean = 0, sd = 2), to calculate the cumulative probability below a given value
00:42:11	Nani Conklin (she/her):	yes! thank you :)
00:42:13	Taylor Yoo:	yes :)
00:43:13	Hiruni Jayasekera (she/her):	i’m getting an error after inputting this code: p15 <- qnorm(q=0.8, mean = 0, sd = 1)
00:43:16	Hiruni Jayasekera (she/her):	Error in qnorm(q = 0.8, mean = 0, sd = 1) : unused argument (q = 0.8)
00:43:25	Hiruni Jayasekera (she/her):	anyone else having this problem?
00:43:50	Alexis O'Connor:	you have to add lower.tail=TRUE, log.p=FALSE
00:43:51	Hiruni Jayasekera (she/her):	whoops i figured it out
00:44:04	Aliza Adler:	^yeah, I had to remove q = and just enter .8
00:44:15	Hiruni Jayasekera (she/her):	thanks aliza!
00:44:27	Phoenix Ding:	Can you try qnorm(p=0.8)?
00:46:33	Hiruni Jayasekera (she/her):	i figured it out! i did p = 0.8
00:47:11	Annalisa Watson (she/her):	qnorm
00:47:14	Hiruni Jayasekera (she/her):	qnorm
00:47:44	Mariah Jiles (she/her):	lower tail= FALSE
00:47:45	Ala Koreitem:	lower.tail = F
00:49:47	Hiruni Jayasekera (she/her):	pnorm?
00:49:53	Aliza Adler:	Qnorm
00:49:54	Mariah Jiles (she/her):	qnorm
00:49:58	Ekua-Yaaba Monkah:	qnorm
00:50:32	Michele Ko (she/her):	3350
00:50:37	Nani Conklin (she/her):	use qnorm to find the z score with a probability of 0.25 and use the mean of 3350 and sd 440
00:51:40	Annalisa Watson (she/her):	qnorm(.90, mean=3350, sd=440)
00:51:41	Hiruni Jayasekera (she/her):	qnorm
00:51:43	Ekua-Yaaba Monkah:	qnorm
00:52:51	Julia Hankin:	Norm?
00:52:51	Lupita Ambriz:	pnorm
00:52:52	Aliza Adler:	Qnorm
00:53:19	Annalisa Watson (she/her):	75th-25th
00:55:17	Chitra Nambiar:	lB <- qnorm(0.25,mean = 3350, sd = 440 )
uB <- qnorm(0.75,mean = 3350, sd = 440 )
p19 <- c(lB, uB)
00:55:21	Silvana Larrea:	c(qnorm (0.25, mean = 3350, sd = 440), qnorm (0.75, mean = 3350, sd = 440))
00:57:18	Aliza Adler:	Pnorm since we want probability
00:58:17	Phoenix Ding:	55,mean=50,sd=5,lower.tail = FALSE
01:00:02	Nikki Marucut:	i
01:00:07	Nikki Marucut:	I’m glad we’re going over this
01:00:09	Nikki Marucut:	LOL
01:00:17	Nikki Marucut:	It’s so confusing
01:00:26	Mariah Jiles (she/her):	^^^
01:00:30	Ekua-Yaaba Monkah:	im confused lol
01:00:48	Mariah Jiles (she/her):	I personally would like to start from the start
01:00:53	Ekua-Yaaba Monkah:	yea I agree
01:01:19	Nani Conklin (she/her):	could you please post the round function in the chat? do we need to know how to use that?
01:02:22	Aliza Adler:	I’d also like to go over #21 at some point if possible- struggling with the examples where we take two given bounds/probabilities
01:02:23	Cristal Escamilla:	I got 100 for p20...round(pnorm(0.55, mean=50, sd=5, lower.tail = FALSE)*100, 2)
01:02:53	Cristal Escamilla:	got it thank you!
01:03:26	Nani Conklin (she/her):	Mariah, did you mean start at the beginning of the normal dist work or the beginning of the lab?
01:03:48	Mariah Jiles (she/her):	whoops I meant start at the beginning of the lab
01:03:52	Nani Conklin (she/her):	I assumed you meant the latter but that might've been me projecting, since I'd like to start from the beginning of the lab!
01:03:59	Mariah Jiles (she/her):	thanks for clarifying Nani
01:04:17	Max Billat (any pronouns):	can we go over #15 please
01:04:22	Annalisa Watson (she/her):	Can we go over #11
01:04:49	Aliza Adler:	I’d still like to go over #21 please
01:04:51	Michele Ko (she/her):	Can we finish normal distribution though
01:04:59	Ala Koreitem:	Can we go over #21
01:05:09	Nikki Marucut:	^ala
01:07:32	Christopher Patterson:	pnorm(55, mean = 50, sd = 5)
01:08:04	Aliza Adler:	pnorm(q = 40, mean = 50, sd = 5)
01:09:13	Ijeoma Uche:	Im getting errors
01:09:17	Cristal Escamilla:	me too
01:09:17	Mariah Jiles (she/her):	same
01:09:18	Yulan Xie:	^
01:10:07	Kelsey MacCuish:	round((pnorm(55, mean = 50, sd = 5) - pnorm(40, mean = 50, sd = 5))*100, 2)
01:10:09	Nani Conklin (she/her):	Thanks, Kelsey! I have to head to another class now - do you know when the recording will be posted?
01:10:57	Nani Conklin (she/her):	super :) i'll check it out tonight, thanks!
01:11:01	Jessica Fields (she/her/hers):	Yes I want to go over #3!!
01:11:05	Nikki Marucut:	I do too
01:11:05	Arianna Libenson:	I said #3! haha
01:11:06	Olufunke Fasawe:	Let
01:11:12	Hiruni Jayasekera (she/her):	also #2…haha
01:11:14	Olufunke Fasawe:	Let's start from the begining
01:12:29	Olufunke Fasawe:	0.35 + 0.5?
01:12:30	Annalisa Watson (she/her):	.35*.45
01:12:30	Hiruni Jayasekera (she/her):	multiply the probabilities of each having o?
01:12:31	Silvana Larrea:	By multiplying those probabilites
01:12:32	Ijeoma Uche:	.35 *.45
01:12:33	Julia Hankin:	0.45*0.35
01:12:37	Olufunke Fasawe:	.35 + 0.45
01:14:33	Ala Koreitem:	B
01:14:40	Ala Koreitem:	And AB
01:14:41	Silvana Larrea:	AB
01:15:53	Olufunke Fasawe:	(0.45*0.35)+(0.4*0.27)+(0.11*0.26)+(0.04*0.12)
01:17:00	Hiruni Jayasekera (she/her):	we’re thumbs upping!
01:17:02	Jessica Fields (she/her/hers):	Im good!
01:17:04	Yulan Xie:	^^
01:17:04	Michele Ko (she/her):	#3! Haha
01:17:10	Jessica Fields (she/her/hers):	Yeah let’s do #3!
01:17:12	Lupita Ambriz:	Lets keep going:)
01:17:19	Olufunke Fasawe:	I have put up two thumbs
01:19:19	Hiruni Jayasekera (she/her):	the sample space is equal to 1
01:21:10	Ijeoma Uche:	1
01:21:19	Annalisa Watson (she/her):	1 - P(0 having O- blood)
01:21:24	Olufunke Fasawe:	1 - P(0, have O-blood)
01:22:15	Ijeoma Uche:	7.2
01:22:16	Olufunke Fasawe:	0.072?
01:22:16	Silvana Larrea:	0.072?
01:22:17	Aliza Adler:	7.2%
01:22:33	Chitra Nambiar:	- 0.072
01:22:34	Olufunke Fasawe:	So, not having O- blood will be 1-0.072
01:22:59	Chitra Nambiar:	1- 0.072
01:23:00	Annalisa Watson (she/her):	=.928
01:23:01	Phoenix Ding:	1-0.072
01:25:08	Annalisa Watson (she/her):	.928^10
01:25:13	Jessica Fields (she/her/hers):	(P(one person having O- blood))^10
01:27:41	Annalisa Watson (she/her):	No we need to subtract this from 1
01:27:42	Hiruni Jayasekera (she/her):	is it 1-0.928^10?
01:27:43	Silvana Larrea:	1 - 0.4736?
01:28:19	Jessica Fields (she/her/hers):	I know this is out of order, but could we review #12 after this?
01:28:25	Jessica Fields (she/her/hers):	(And if not, no worries!)
01:28:58	Taylor Yoo:	yes :)
01:29:26	Annalisa Watson (she/her):	Could we start with 11!!
01:29:57	Ijeoma Uche:	Yes can we start with 11
01:32:22	Nikki Marucut:	yes
01:32:22	Hiruni Jayasekera (she/her):	yes
01:34:40	Aliza Adler:	Yeah I can’t see the formula, just the bottom part of the tree
01:36:11	Ijeoma Uche:	.9985*.0060
01:36:15	Jessica Fields (she/her/hers):	0.015925
01:36:31	Ijeoma Uche:	I got 0.005991
01:36:42	Chitra Nambiar:	me too
01:36:52	Jessica Fields (she/her/hers):	For 10, didn’t we have to do 0.01*0.9985 + 0.99*0.0060
01:37:43	Jessica Fields (she/her/hers):	Great! Was just responding to a few folks in the chat who got something different
01:38:18	Ijeoma Uche:	It came out to be correct when I checked
01:39:17	Aliza Adler:	Can you just quickly explain that?
01:40:08	Yulan Xie:	Where can we access the recorded videos again?
01:40:44	Ijeoma Uche:	I tried this .9985/(.006+.9985) but its wrong lol
01:40:49	Arianna Libenson:	isn’t 10 just 0.009985 + 0.00594 = 0.015925
01:40:54	Annalisa Watson (she/her):	0.01*0.9985/(0.01*0.9985 + 0.99*0.0060)
01:41:34	Christopher Patterson:	P(HIV+ AND test+) / P(test +)
01:45:48	Ijeoma Uche:	P(T = positive|A= antibody present)
01:45:55	Annalisa Watson (she/her):	Person has the disease and tests positive for it
01:46:08	Ijeoma Uche:	P(T = negative|A= antibody absent)
01:46:35	Annalisa Watson (she/her):	Thanks!
01:46:57	Ijeoma Uche:	P(A=antibody present|T=positive)
01:47:00	Annalisa Watson (she/her):	Is it #11
01:49:16	Ijeoma Uche:	99.4%
01:49:17	Phoenix Ding:	0.9940
01:50:07	Ijeoma Uche:	Do we have to account prevalence ?
01:51:23	Jessica Fields (she/her/hers):	Do we have to account for prevalence for sensitivity and specificity? Or only for PPV?
01:52:58	Ijeoma Uche:	#14 pleaseee
01:53:52	Ijeoma Uche:	.6^10
01:56:29	Hiruni Jayasekera (she/her):	none?
01:59:39	Ijeoma Uche:	Out of the 10?
01:59:43	Silvana Larrea:	I have to go. Thanks Kelsey!
02:00:41	Ala Koreitem:	0.6^10
02:01:45	Cheyenne Pritchard:	complement?
02:02:29	Ijeoma Uche:	.006046618
02:02:36	Hiruni Jayasekera (she/her):	.6^10
02:04:46	Cheyenne Pritchard:	(not sedentary >= 1) = 1-p (not sedentary = 0)
#Use the complement rule. When we read "at least 1 -> 1-P (1- probability of not sedentary) 
# 1-.4^10