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Stat 88 class - Shared screen with speaker view
Andrew
11:38
prof, you're echoing a lot on my side
lucy sandoe
11:43
^
YeJin Ahn
11:49
^
Marisol Trejos
11:49
^^^^ same
Jooee Karwande
11:51
^
isaacecheto
12:44
yep
Joshua
12:45
yup
Shannon
12:46
yes
Jooee Karwande
12:47
ys
YeJin Ahn
12:48
yes
Joshua
13:10
yes
chelsie
13:10
yes
Andrew
13:11
its fine now
Jooee Karwande
13:12
The echo is gone
Shannon
13:13
yes
YeJin Ahn
13:13
yes
isaacecheto
13:16
It’s not echoing anymore
lucy sandoe
14:19
5%?
Vasanth Kumar
14:27
- phi(.05)
celinewherritt
16:15
Do u use ppf for these examples bc it gave u a 90% percentile?
lucyzhang
17:09
Can u not use inverse for b? Is that an option?
lucyzhang
17:24
Wait nvm
lucyzhang
17:28
thanks
Dorkhan Chang
17:29
for a, wouldn't inverse of (0.95) also give you area to the left of "-z"?
Zachary Zhu
18:32
~68%
Marisol Trejos
18:36
68%
lucyzhang
19:49
Sorry when do u use inverse and when do u not?
Joshua
22:12
Is the empirical rule strictly for normal curves?
sophia
31:41
Can you explain z > 1.9 again plz
sophia
32:20
yes you said something like 1.9 SD away
sophia
32:28
From 0?
sophia
32:57
got it thank you!
lucy sandoe
34:09
sorry what does i.i.d. stand for?
lucy sandoe
35:58
thanks
Jooee Karwande
36:55
Are we supposed to know what phi(1.25) is
sophia
37:07
On the quiz can we leave our answers in phis
celinewherritt
37:38
Why do we multiply the SD by root 100 not 100
Alex Xi
37:45
^
lucyzhang
37:46
Why did u do sort root of 100 *2 again
lucyzhang
37:51
square
celinewherritt
38:08
Ohhh ok thank u
lucyzhang
38:12
Okay that answered my Q thanks
Stephen
38:15
Must start with transformation of variance
Stephen
38:25
Then square root
lauraherron
39:46
b
Jooee Karwande
39:47
b
ashley minooka
39:48
b
Marisol Trejos
39:48
a
Atmika Ashok Pai
39:48
b
lucy sandoe
39:48
b
Zehra Ali
39:49
b
jessicaornowski
39:49
b
chelsie
39:49
b
mayareuven
39:49
b
louisnorris
39:49
b
Noah Wadhwani
39:50
b
Zachary Zhu
39:50
B
celinewherritt
39:50
b
sophia
39:50
b
Kieran Cottrell
39:51
b
Lauren White
39:51
b
Vasanth Kumar
39:52
b
YeJin Ahn
39:53
b
Natalie
39:54
b
Ronan Figueroa
39:55
b
Laura Nguyen
39:56
b
Justin
39:57
b
Dayne Tran
40:01
c
Micah Grim
40:03
b
Michael Madden
40:05
b
sophia
40:51
Why can’t we use CLT?
sophia
41:49
got it
Brendan Co
48:59
no
Jingyu Han
50:27
where did you get -1.645 again?
Jingyu Han
51:14
Oh I see
Jingyu Han
51:31
yup
Jingyu Han
51:32
Thanks
Dayne Tran
54:39
hi for markovs isn't it greater than or equal to, so should the p be p(x >= 701) and we use 701 in 690/c?
lauraherron
54:46
how is the probability over 1?
Dayne Tran
55:59
yes thanks
Stephen
56:00
how do we know when a distribution is discrete or continuous?
parkersalomon
56:21
How large does n need to be to use clt
sophia
56:30
What’s considered a large n? Is there a cutoff value?
Stephen
56:58
yes 9ty1
Noah Wadhwani
01:00:09
bet
Alex Xi
01:01:33
Great lecture, thank you professor
YeJin Ahn
01:01:35
Thank you!
Laura Nguyen
01:01:36
thanks
Brendan Co
01:01:37
thanks
Jingyu Han
01:01:37
thank you!
chelsie
01:01:39
thank uu
Joshua
01:01:39
Thanks!
mayareuven
01:01:39
Thanks!
louisnorris
01:01:40
Thank you!
Sophie Liu
01:01:40
thanks!
Dayne Tran
01:01:46
Thanks!
sophia
01:02:13
Would you please explain discrete vs. continuous again?
lauraherron
01:10:01
thank you professor!