joel chang

17:57

Yessir

Sophie Liu

17:58

yes

Brendan Co

18:00

yup

isaacecheto

18:00

yep

Nishi Rahman

18:05

yes

Rebecca Hu

20:35

1 - phi(7.23)

isaacecheto

20:37

Still working on it

Dayne Tran

20:42

its the binomial summation of p(x>134)

michellecan

20:51

^

isaacecheto

20:57

^

Zachary Zhu

21:08

phi((134-100)/sqrt(300*1/3*2/3))

Naqeeb Punjani

21:10

^^^

Zachary Zhu

21:21

1- that

ashley minooka

25:25

why is it 8.2**2 again?

isaacecheto

25:34

That’s variance

Jooee Karwande

25:36

Why couldn’t you have done the binomial summation?

isaacecheto

25:36

Not SD

Joshua

25:53

I’m doing well

Nishi Rahman

25:55

Can you explain what k is again?

Dorkhan Chang

26:21

It is normal because 300 is large enough right?

Michael navruzyan

26:21

can you explain again why you can use normal approx

Nishi Rahman

26:24

Thank you

Shannon

26:46

what is "large enough" generally?

isaacecheto

26:58

µ - 3SD > 0

Alex Xi

27:41

how do we find the probability if its within 3 standard deviations?

Laura Nguyen

27:43

is attendance actually mandatory?

Brendan Co

27:53

Lol ^

Forrest Kim

27:58

gotteeem

celinewherritt

28:04

Lmao lauras famous

Monse Lopez

28:24

How do you know there is no data at 4 sd?

Alex Xi

28:38

how do we find the probability if its within 3 standard deviations?

Matthew Simon

28:49

How do we know it is still shaped normal because couldn’t its shape be really strange but still meet those 3SD parameters?

Marisol Trejos

28:55

Why did you use P(X>=135) and not P(X>134)

Monse Lopez

29:01

thank you!

Alex Xi

29:14

ok thank you

Matthew Simon

29:51

Yes thx

Marisol Trejos

30:26

ys

sophia

30:57

Can we still put P(X>134) though？

lucy sandoe

36:13

what do you mean by "at T"?

Shannon

37:06

It's "of TS", I think

Kaci Gu

41:07

is the p-value equivalent to the sd(x bar)

Noah Wadhwani

41:13

What exactly does phi represent again?

celinewherritt

41:43

Why did you divide by root 150 again?

Jooee Karwande

42:06

That’s the formula for SD(average)

lucyzhang

42:06

Why did we shade the area towards the right again

Kaci Gu

42:26

Got it thanks

Marisol Trejos

42:30

How did we get 299,796 as the X(bar)

lucyzhang

42:38

Yea!

Marisol Trejos

43:11

Yes thanks

Michael navruzyan

43:30

why is 50 not sd of sample mean

Joshua

43:56

Why do you do 1-phi? Instead of just phi

Nishi Rahman

45:15

What does phi mean again

Joshua

45:16

yeah

Joshua

45:27

Is there a case you would just use phi?

Nishi Rahman

45:44

And you multiply that by .87 to get standard units?

Joshua

45:46

Oh that makes sense

Nishi Rahman

46:09

oh

Nishi Rahman

46:15

okay

lucyzhang

49:48

For Ho, can u simply say that the data supports the hypothesis that the unknown mean 98.6 is normal temp? So basically restating the question

Joshua

50:53

Isn’t it supposed to be 98.6-98.2?

Michael navruzyan

51:59

is it p(x bar < 98.2) or p(x < 98.2), what's the difference?

Monse Lopez

52:35

do you use sd of x bar to account for error?

lucyzhang

52:37

Do u say accept or fail to reject

Marisol Trejos

53:11

I usually say fail to reject

Joshua

54:21

Oh yeah gotcha

Matthew Simon

54:29

Can you go over again the difference between SD of x bar and SD x

Kieran Cottrell

54:53

Is there a way to test for both sides of X instead of just doing a one sided test

Michael navruzyan

55:44

yes thanks!

Monse Lopez

56:44

yes! Thank you

lucyzhang

57:44

Got it, thanks!

Newton Szeto

58:17

how do we calculate the numerical value of phi((98.2-98.6/0.15))?

Matthew Simon

58:29

Yes thx

Newton Szeto

01:00:01

Thank you!

Joshua

01:08:18

Did you assume those two probabilities were equal to each other because a large n assumes a normal distribution?

isaacecheto

01:09:39

Yep

Michael navruzyan

01:10:35

if you use X bar as TS, what would sd(x bar) be?

sophia

01:11:19

Why do we reject when X-266 is LARGE?

lucyzhang

01:11:21

Can u also do 277-266 and times 2

Joshua

01:11:37

^

lucyzhang

01:11:54

And what does T is large mean, how large is large

isaacecheto

01:11:57

Sophia we accept because it means that the observed statistic being only 11 away is probable

Joshua

01:12:16

You answered it early

Nishi Rahman

01:12:23

Where does 277 come from?

amywang

01:12:25

Why isn’t it just the P(X<255) since 255 is the observed statistic? Why do you check the other end too

amywang

01:12:56

^yeah why do you also check 277

Monse Lopez

01:13:21

Nishi the 277 is from the observed values given to us at the beginning of the problem

Michael navruzyan

01:13:56

yes thanks

sophia

01:14:31

yes thanks!

Nishi Rahman

01:14:44

Thanks Monse!

YeJin Ahn

01:16:04

Why do we accept the null because 0.44 > 0.05?

Newton Szeto

01:16:43

where do we get the numerator of SD(x bar)?

Nishi Rahman

01:17:19

yes

amywang

01:17:22

Oh my question is where does 255 come from then

amywang

01:17:26

Since 277 is observed

amywang

01:17:54

Sorry I asked my question in correctly

Marisol Trejos

01:18:54

Why dont we subtract 1-2phi((255-266)/14.1)

sophia

01:19:27

Did we assume that P(X>277) = P(X<255)?

YeJin Ahn

01:21:03

Thank you professor! :)

Newton Szeto

01:21:27

Yes

Newton Szeto

01:21:49

Yes

Newton Szeto

01:22:00

Thank you!

Matthew Simon

01:22:03

So when do you use square root of npq

sophia

01:22:21

SD of Binomial

Nishi Rahman

01:22:38

Can we go through finding the p-val with x bar as a T.S>

lucyzhang

01:23:09

Do we always have to do two tails?

Marisol Trejos

01:23:41

Okay

Marisol Trejos

01:23:45

Thanks

Marisol Trejos

01:23:55

yes

sophia

01:24:19

Thanks!

sophia

01:24:27

YES!

Matthew Simon

01:24:58

Got it

Matthew Simon

01:25:07

Yes thank you

Michael navruzyan

01:27:24

so if its a sum why don't we multiply the 14.1 for SD(x) by sqrt(n) to get sample sum sd

Michael navruzyan

01:30:44

ahhhh

Michael navruzyan

01:30:49

thanks so much

Monse Lopez

01:31:03

sorry I still can’t wrap my head around how we come to the conclusion

Monse Lopez

01:31:10

Can you explain again?

Monse Lopez

01:31:24

yes

Monse Lopez

01:31:50

By extreme you mean small?

Monse Lopez

01:32:02

oh yess

Monse Lopez

01:32:23

Ahhh yes

Monse Lopez

01:33:03

yes thank you!

Nishi Rahman

01:33:07

What is the argument when we calculate phi in python? stats.normal.cdf(____)?

Monse Lopez

01:34:21

nope thank you!

Shannon

01:34:29

thank you!