#help-49
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i cannot find anything in my textbook talking about this situation
The vector lies outside the square, this event corresponds to the empty set, which is Jordan measurable and has probability zero
if P(empty set) != 0 ur violating the axioms of probability space @ancient ridge
@faint tree Has your question been resolved?
I disagree as what you are saying because it sounds like you are trying to say that everything outside of the sample space is just wrapped up in some equivalence relation and labeled the empty set. I would prefer to not get pinged about this further unless the message is the relevant passage out of OPs book.
as in phi is a probability density function from $\Omega$ to $\mathbb{P}$
LY
C is asking for $\mathbb{P}(([-7, 7]^{2})^{c})$
LY
everything outside the sample space is literally the complementary of the sample space come on
look at the definition he gave
@faint tree Has your question been resolved?
yh but it looks like the question wants B D F G I J
cus otherwise like...what's wrong with what OP put?
thats what we're trying to figure out
as in i'm pretty sure it's probably not C cus they're explicitly asking for ([-7, 7]^2)^C
in R^2
which isn't in sigma
Omega = [-7,7]²
yeah
well i think probably wait for OP to try everything except C and then see what happens
then being outside the square doesnt make sense
yeah ik
if it is C, it feels more like english rather than like...actual maths
but oh well
this is not correct either
@faint tree Has your question been resolved?
@faint tree Has your question been resolved?
@faint tree Has your question been resolved?
This all looks correct to me
and i wish it was
BCDFGIHJ?
No confusing cause you have to select the events that can’t happen lol
i did already; look above
i originally did BCDFGIJ
H corresponds to the top half of the square, minus the axes
so i dont think its probability 0
Ur right idk why I said H
This appears to be correct
i wish it was
Mistake by the teacher?
i think there's an error in the question...
im not sure ill have to check it i guess
Email your teacher maybe?
i guess so
If an event has probability 0 then it doesn’t occur lmao
Imagine that’s it 💀
You were actually taking a riddle class
i give up zzz
ill check in with my teacher
usually these dont contain mistakes but maybe this time there is
I’m very curious wether it’s a mistake or what his interpretation is
Same
well thanks for the help anyways
I see you are greek?
i am not lmao
Greek in the discord bio 😔
Not quite
Yea yea I know it was a joke
Oh? Alr
Oh wait what if this is the interpretation?
Events that have P = 0 but are still in the SS
That's certainly the interpretation
Wait nvm that’s been tried
So my concern is it says φ is arbitrary
But that’s shouldn’t matter no?
What’s a delta function
In mathematical analysis, the Dirac delta function (or δ distribution), also known as the unit impulse, is a generalized function on the real numbers, whose value is zero everywhere except at zero, and whose integral over the entire real line is equal to one. Thus it can be represented heuristically as
δ
(
...
Thus we can choose to assign any measure 0 subset of the distribution space non-zero probability
But then there no one answer
Like phi could be half the time it's a uniformly random distribution, and the other half it select (0,0)
But then you don’t have enough information to solve the question
I don’t think that really applies here, this is very likely a Probability I course
I don't see an error with this at all
C is wrong
Since lying outside the square is not an event
^ I think this is right
Maybe
P(outside the square) is undefined
P(outside the square) = 0
Any event outside of the space of possible events has probability 0
Since P maps from Ω -> R
Yea but he tried them both anyways
Broken question, hate those
Especially ones that are so damn pedantic about their answers.
Well usually someone else who does the assignment early would catch the error and it would get fixed pretty quickly
well the assignment was released only 30 min before i made the post
this is the only question i didnt finish in that time
Be the goat of the class
Email the teacher say you think it’s wrong and it will be fixed
You are that student now lmao
it seems like it yeah
well thanks for helping
at least i know im not crazy
im gonna sleep
Yea it will probably be fixed by the time you wake up
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Show that $\lim_{n\to \infty} \frac{b_{n+1}}{b_n} = 1\$ Where $b_n = \int_0^{\frac{\pi}{2}} \sin^n(x) \text{d}x$
MæthIsAlwaysRight
oh and I can't use the wallis product because this is part of its proof
idk where to start, because i dont really know how can I work out the ratio of 2 integrals
@dreamy lichen Has your question been resolved?
try reduction formula
Reduction formula helps me prove that if the limit is 1, then the wallis product formula is right
i have these 2 formulas
wait how do i apply squeeze?
the ratio is (wallis product) / (pi / 2)
in the limit
and i dont yet know whether wallis product = pi / 2
$b_{2n+1}\le b_{2n}\le b_{2n-1}$
TargetVN
it can be proven using sin(x) <= 1
right, i can believe this
so b_n is decreasing
that'd mean that
(b_(n+1) / b_n) <= 1
in the limit
the opposite inequality is tougher tho
$\frac{b_{n+1}}{b_{n}}\ge \frac{b_{n+1}}{b_{n-1}}=\frac{n}{n+1}$
TargetVN
its not that tough
OH
That makes so much sense
i just needed to go one step further
thanks man
np
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how do i prove RHSs are equal using taylor series?
Consider the expansion of exp maybe
expand e^x and e^(-x) using taylor series
there will be some terms that cancel each other
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in a dataset strictly consisting of repeats
is that data point technically all percentiles?
like [2,2,2,2,2] then 2 is the 50th percentile
and also the 89th percentile right
right okay thanks
and uh
in a normal distribution for example
each percentile has the same number of “frequency” right
i mean isn’t that by definition
but the area under the curve just tells you how much of the data a specific datapoint is above or below?
not exactly
Percentiles are equally spaced in terms of proportion, not frequency.
wdym?
for example, the area between the 49th and 50th percentile is 1% of the total area of the curve, but for a standard normal distribution, the curve is taller near the mean meaning that the data points are more densely packed in that region
yeah but the area between consecutive percentiles are still the same
1% in specific
what did you want to say with the second line
maybe i’m not getting that
u mean two data points could be “nearer” if it’s around the mean
compared to datapoint around the “endpoints”
but both still are 1%?
You asked if the frequency is same for each percentile right? I'm saying suppose an interval with range d corresponds to an area of 1% near the mean, and another interval with range d' corresponds to same area of 1% far away from the mean. Then clearly d' > d
Yes
hmmm yeah fair
wait then
what does frequency even mean then
in terms of a continuous distribution
yeah okay i guess i see how u use it
thanks

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You can't talk about the frequency of a specific value because the probability of any exact value is technically zero.
yeah that’s right
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Find $\int_{S} (x^2-y^2)dx dy$ where $S$ is bounded by the curve $y=\sin(x)$ and the interval $[0,\pi]$
What a wonderful world it is !
check your bounds
What a wonderful world it is !
yes
Next, I want to find the volume of the pyramid bound by the 3 coordinate planes and the plane $x+2y+3z=6$
What a wonderful world it is !
So The region is $x \geq 0, y\geq 0, x+2y \leq 6$
What a wonderful world it is !
so $\int_{0}^{3} \int_{0}^{6-2y} 6-(x+2y) dx dy$
What a wonderful world it is !
looks correct
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A solid is bounded by the surface $z=x^2-y^2$, the $xy$ plane, and the planes $x=1$ and $x=3$. Find the volume of the solid
What a wonderful world it is !
This is essentially the region between x=y and x=-y in the first quadrant
What have u tried?
Well, honestly, there's nothing to try here... Since u just need to put these into a triple integral
double, right
you can do both
if you do a triple integral you'd just be integrating 1
but writing it as a triple integral is easier
It's this region, right
with the constraints x=1 and x=3
$\int_{1}^{3} \int_{-x}^{x} (x^2 -y^2) dy dx$
What a wonderful world it is !
looks aight
nq
What a wonderful world it is !
What a wonderful world it is !
Is this fine>
what a wonderful world it is !
why is there two integrals
This is a double integral
yea
thanks
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For context: I have absolutely no knowledge of physics, yet they have introduced these problems in class.\
So, I was working through my notes (which you're supposed to answer the questions and do the homework for), and I never saw this concept when I was reviewing on Khan Academy or from CollegeBoard. They introduced a physics concept, in which $\mathbf{r}(t)=[(v_0\cos(\theta))t] \mathbf{i}+\qty[-\frac12 gt^2+(v_0\sin(\theta))t+h]\mathbf{j}$, where $\mathbf{r}(t)$ is the position vector as a function, $\theta$ represents the angle of elevation of the projectile, and $v_0$ is the initial velocity. Can anyone explain why this is?
;(
Or maybe I should post this somewhere else?
And ping me if/when you reply, since I'm working right now.
Seems to be projectile motion
v_0 is the initial velocity, theta is the angle at which the projectile is thrown from the ground
@dusty portal
The only force acting on the projectile is the force of gravity, which is vertical, so the horizontal velocity stays the same, meaning the horizontal position is simply the initial position times time passed, whereas the vertical position obeys (initial height) + (initial vertical velocity)t - gt^2/2
;(
Well, I know gravity can vary, but you get the point.
Yeah
I see.
Thank you.
Let me write this down, I do want to study calculus-based physics but I'm not the greatest at it.
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Can someone give me a direction?
I know from the given that r(A)=1 and that (1 -1 3) is a linear span of columns of A, but how to find the space of rows of A ?
@graceful ferry Has your question been resolved?
@graceful ferry Has your question been resolved?
what does Ax=0 tell you about the rows of A
All I know is that the dimension of the amount of answers(?) for Ax=0 (2) is the same as KerA which is the same as n-r(A) (the amount of zero rows)
And this is how I concluded that r(A)=1...
Oh I'll see then
KerA is {(3 -1 1) (-1 -1 -1)} ,,
How do I use this? could I find ImA ? How to use the given that Ax=(1 -1 3) has a solution,,
I know it means (1 1 -3) is a span or columns of A but I'm confused by this since all columns of A looks like this: (x 0 0) so how can I get (1 -1 3)? Is thus where the trick is?
the columns of A do not look like this
thats not what rank 1 means
what does it mean that A*(3,-1,1)=0
let (a1,a2,a3) be the first row of A
That 3a1-1a2+1a3=0 and so on.. ?
ok I wanted a less specific statement. "I can solve for a1,a2,a3"
can you find another such equation?
Yes -a1-a2-a3=0
no
the rank is the maximal number of linearly independent columns or rows
so here at most 1 row is linearly independent
aka, they are all multiples of another
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Ugh that means (1 -1 3) is useless
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Can someone help me intuit the central limit thm
Go ahead
Hii

Yea so
We r taking N samples from any (ANY) distribution
And it's sum is supposed to follow
The same family of distributions?
The sum of samples
in the limit
one restriction: it has to be a distribution with finite variance
And if u subtract N times the mean of the OG distribution and divide that by std deviation times sqrt N, then that expression will follow the same normal distribution
What would be a case where this is violated
Right, as N-> Inf.
cauchy distribution is one example
most distributions you deal with in practice have finite variance though
Why does it violate it..?
it has infinite variance (its tails are too "heavy")
its pdf looks like a normal distribution but more spread out
Ahh ok
So if we sample from the cauchy distribution
Then the CLT won't apply?
Can you help me intuit all this
right, iirc if you take a sum of iid samples from the cauchy distribution the result is still cauchy
it doesn't converge to normal in the limit
Ohh
So
- Why does CLT apply for finite variance case
- Why it doesn't apply for indefinite variance case
what kind of intuition are you looking for? have you ever seen one of those animations where they show the distribution of the sum of the number of "heads" in a sequence of coin flips, as the number of flips increases?
as far as mathematical intuition, when you add independent samples from some probability distribution, the distribution of the resulting sum is the convolution of N copies of the original distribution (where N = the number of samples)
and the convolution of the normal pdf with itself is the normal pdf again
Galton board iirc
so it's kind of a "fixed point" for the operation
Convolution as in the convolution operation?
yea
why is this so
just works out mathematically, it's pretty easy to show
Hmm ok
Idk why im not able to like
"get" it
I'm trying to make it intuitively click
as for how you might guess that is a solution to "what distribution convolved with itself gives itself again" without already knowing it, there's probably some cool way to deduce that but offhand i can't recall how
Hmm kinda the way we got e^x
yea maybe kind of analogous
Lévy distribution too
So can we just say that
It's cuz
It's very unlikely to get sums that sum to the largest possible sun
Sum
Random sums will be closer to the average
I think I get that this is the intuition here
But like why
hedge, whats the confusion u have here
this is true (and is basically the content of the law of large numbers), but from that alone you wouldn't be able to conclude that the limiting distribution is normal
What is the sort of underlying logic that goes "If we take sum of a bunch of samples, we will with a higher probability get sums which lie in the middle of the distribution than the rare cases where the sum is the largest or smallest possible"
right that would be my second question
But as u said that seems more of a mathematical technicality
Which we can get to by uhh algerbraing our way there
The Law of Large Numbers states that as the number of samples n increases, the average of the samples converges to the expected value (mean) of the distribution
Righttt
Extreme sums (either very large or very small) are rare because they require all the individual samples to be extreme simultaneously
And what makes that rarer than the mean-ish values?
And why does this not work for the cases you mentioned
Where variance isn't finite
ur violating the conditions of CLT 🙂
Yea that's what I'm askingz why r they the conditions for the clt
aight okey i'll think of some intuition and get back at ya
yea like i said, it comes down to the convolution property and the fact that the normal pdf is a fixed point for that operation
not sure if there's a less technical explanation for why in the limit you would get something of the form e^(-x^2)
Honestly I think my question has
Boiled down at least rn I think it has boiled down to
Just understanding why the law of large number holds true
I mean
Rn I feel like GIVEN the LLN, it OBVIOUS that the CLT will hold
Yea I just have a FEELING
Hmm
So like why does the LLN hold
the LLN is just saying that as you take the sample mean of a bunch of iid random variables, the variance of the result gets smaller, and in the limit the result "converges" (in various technical senses) to the mean of the distribution
like if you flip a large number of coins and compute the fraction of them that are heads, you'd expect the fraction to be close to 1/2
Isn't this the "consistency" assumption
and you'd expect that as you increase the number of coins, you're more likely to get a result close to 1/2
wdym by this?
I mean that that will only be true if the estimator for the mean is consistent
well the sample mean is consistent
Ohh
because expectation is linear
The sample mean is by default a consistent estimator for the population mean?
Makes sense
I mean seems trivial now that I think about it
Oh? It's required to be linear?
it is by definition
And wdym when u say expectation is linear
$$E\left[\frac{1}{n}\sum_{i=1}^n x_i\right] = \frac{1}{n}\sum_{i=1}^n E[x_i] = \frac{1}{n} n\mu = \mu$$
Bungo
Ahhh rightt
it's defined as a particular sum (or integral) right?
and integration is linear
N
Rightt
So given it's linear
It has to be consistent
I mean even if it was quadratic
for the mean yes
It would have been consistent I suppose
if you want an unbiased estimate of the variance you have to tweak the 1/n to 1/(n-1), for example
Sum of squares for example? It's also consistent?
and for other parameters there may not be any obvious unbiased estimator at all
but the the mean is simple regardless of distribution
Omg u made me remember another query I had
Please explain that to me
The whole degrees of freedom shenanigans
kekw x))
well the math works out the way it works out
but maybe the intuition here is that when you compute the sample variance, you need to know the real mean but you can only compute the sample mean, so that introduces a bit of "noise" into your calculation that you have to compensate for
Yes but why is it n-1 qnd not like n-2 for example
That's what I don't get
ah i can't give you a simple intuitive reason for that, you just do the math and that's what happens
that doesn't mean there isn't a simple intuitive reason, i just don't know it offhand haha
It's something something degrees of freedom something
degrees of freedom are closely tied to the number of independent observations or parameters in a dataset, minus the number of constraints.
Right
suppose there is n data points
so there should be n deg of fred right ?
the sample mean impose a constraint that xi - mu = 0
thats why in the variance we do : $s^2 = \frac{1}{n-1} \sum_{i=1}^n (x_i - \bar{x})^2$
Goëtia
to do n-2, u need another constraint
do to n-k, u need k constraint on the data sampling
Yea exactly, that's what I'm asking "why"
look, dgrees of freedom matter because they quantify the effective size or flexibility of a system
What's the sample-population adjustment have to do with the degrees of freedom
More degrees of freedom generally allow a model to fit data more closely, but this can lead to overfitting, fewer degrees of freedom impose simplicity, reducing overfitting but potentially increasing bias
degrees of freedom affect the precision of estimates and the power of statistical tests. More degrees of freedom typically lead to narrower confidence intervals and more sensitive tests
Thee intuitive reason for using N-1 for sample variance is that cuz we are sampling from a distribution, subtracting from the samples mean and squaring it will lead to underestimation of the numerator term in the variance
So we are trying to account for that underestimation by ourselves reducing the denominator
But this only gives us intuition for why the denominator should be reduced
you could think of it that way
Not for "by how much"
The key point here is that s² is designed to be an unbiased estimator of the population variance σ²,this means that, on average, s² should equal σ² when computed over many samples
degree of freedom or whatever, n-1 can be derived exactly from the mathematical expectation
let me derive it for ya rq
$\sum_{i=1}^n (X_i - \bar{X})^2$ this is the deviations right?
Goëtia
That is the variance yes
$(X_i - \bar{X})^2 = (X_i -\mu - (\bar{X}- \mu))^2 = (X_i - \mu)^2 -2(X_i -\mu)(\bar{X} - \mu) + (\bar{X} - \mu)^2$
Goëtia
$E[\sum_{i=1}^n (X_i - \mu)^2] = n \sigma^2$ agree?
Goëtia
$\bar{X} - \mu = \frac{1}{n}\sum_{i=1}^n (X_i - \mu)$
Goëtia
$\sum_{i=1}^n (X_i - \mu)(\bar{X} - \mu) = \frac{1}{n} \sum_{i=1}^n \sum_{j=1}^n (X_i - \mu)(X_j - \mu)$ if $i \neq j$ then $$E[(X_i - \mu)(X_j - \mu)] = 0$$ since they are i.i.d agree @jolly pebble
Goëtia
last term here when applied the expected value to it is just the variance of the sample mean evaluating to : $\frac{\sigma^2}{n}$
Goëtia
bringing everything together we get $E[\sum_{i=1}^n (X_i -\bar{X})^2] = (n-1)\sigma^2$
Goëtia
you got it why n-1 now? @jolly pebble
DOF?
Degrees of freedom
Hm?
u know of the MLE?
Yes
So here we are not adjusting for DOFs?
In linear regression, the degrees of freedom for the residual variance depend on the number of parameters estimated. Specifically, if you estimate p parameters (including the intercept), the residual variance is computed by dividing by n-p, suppose ur in simple linear regression y = ax + b + e (a,b parameters)
this is the residual variance
I guess I'll have to read a bit Abt degrees of freedom on wikipedia
Thx a lot for ur help goëtia
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i got a quick question
You need help factoring?
sort of
just got a question
are u allowed to put 1 outside the bracket?
like this 1(3x+1)
Definitely
ah ok
In fact, you should, because it makes factoring easier
I thought you weren't allowed to do that but thank you for clearing up the confusion!
wait so the answer would be (3x+1) (3x+1) ?
You can simplify further
||a * a = a^2||
expanding?
,w expand (3x + 1)^2
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can someone explain how this interpretation differs from margin of error: is it only because we didn't multiply by a critical value. also why would we use standard error of margian of error to make a interval for a cofindence level?
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I do not understand why it cannot be the case that A' has an enemy of B on its left or B has an enemy of A' on its right, so even if we reduced the number of hostile couples by 1, we would be adding at least 1 back, can anyone help me to understand it please?
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I don’t get what I have to do next
@regal knot do you know how to convert this into a z-score and look up values in a normal CDF table?
yeah
Convert both of these values into a z-score. Then you have 2 points on a normal distribution which you can use to find the two unknowns: mean and stdev
Don't I already have 2 points 70 and 99?
Yes, you are using the those two points, except in the z-score world as well
There are two ways to go from a value to a z-score, one is using the normal cdf and the other using the mean and stdev
You're using the normal cdf to find the z-score, then using the formula to relate regular x coord (i.e. 70 and 99) to z-score via the mean and stdev, you are coming up with two equations and two unknowns
@regal knot still with me?
You're doing the system to find both variables
Once you do that you can use them to find the z-score associated with 80 to answer the question
This one you need to use the mean and stdev
but theres not exact value for 0.16 and .99 in the z score table
or do I just do the closest one
You need to estimate
Using a linear interpolation
Or dead reckoning, if you don't need many digits of accuracy
Ummm
Those are not the correct z-scores.
At very least 99 is wrong
But yeah, that's the idea
I got -2.96 and -1
How on earth would you get -2.96?
its was suppose to be for 16% but it gets 0.16%
(wrong tail direction)
Anyway, I need to go afk, best of luck, you're 95% of the way there!
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I’m not the smartest and I’m not that cocky but I’m almost 10000000% sure I did this correct
But the computer said the answer was wrong
Nvm
I completely forgot I u subbed
.close
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"10000000% sure"
😂
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can you help me with this question
@errant fable Has your question been resolved?
!status
What step are you on?
1. I don't know where to begin.
2. I have begun but got stuck midway.
3. I got an answer but I was told that it's wrong.
4. I got an answer and would like my work checked.
5. I have a question about someone else's work/solution.
6. I have completed the problem and don't need help anymore. Thank you.
7. None of the above
@errant fable
6
Sorry could i ask abot another question?
oke
how can I rearrange this to be able to put into the equation
?
what equation?
Partial sum for geometric sequence
i just dont know how to rearrange to in form of a * q^r
its okay i dont think im explaining well ill be able to figure it out
.close
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its ok just take ur time to formulate ur question in ur own mother language translate it to english using AI, and post it here we'll do our best to help you @errant fable
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Calc 2: A reservoir is shaped like a sphere of radius 5 m. It is filled up to 8 m with water.
Compute the work required to pump the water to 2 m above the top of the reservoir.
My equation for work is (9800)(12-x)(25-x^2)(pi) integrating from 0 to 8. This does not give the correct answer but close to the solution answer. Where did I go wrong?
@last slate Has your question been resolved?
where does the 0 to 8 come from
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If the $\limsup a_n=y$ of a real sequence $(a_n)$ is negative infinity, why does this imply that $s_n\to -\infty$, where $s_n=\sup_{k\geq n}a_k$? The definition of $y=-\infty$ I'm working with is that ${a_1,a_2,\ldots}$ is not bounded from below (but it is bounded above) and no finite $\limsup$ exists, where the finite $\limsup$ is defined in terms of: \
For every $\epsilon>0$;\
- there exists $N\in \mathbb N$ such that $a_n<y+\epsilon$ for all $n> N$;\
- for every $N\in\mathbb N$ there exists $n>N$ such that $a_n>y-\epsilon$.\
My attempt; if $y=-\infty$, then ${a_1,a_2,\ldots}$ is unbounded. Suppose on the contrary that $s_n\geq M$ for infinitely many $n$, where $M$ is some real. I want to derive some contradiction, but I don't know how.
psie
@inland patio Has your question been resolved?
that would imply that there exist k >= n : a_k >= M, by the definition of s_n = sup_k>=n a_k
are you sure that there exists such a k>=n such that a_k>=M?
well for infinitely many n
to be sure
you can do
a_k >= M - e
honestly a straightforward proof would do it instead of by contradiction
I just don't see how 😅
s_n is the supremum of {a_n,a_{n+1}, ...} right
s_n >= a_k
since lim sup a_n = -infty
for any real M
there exist N
k>= n > N : a_k < M
this implies
sup_{k >= n}a_k <= M
thus s_n <= M
how would you motivate the existence of such an N? Sorry to bother about this 🙂
ah ok, you used the fact that limsup a_n=-infty means {a1,a2,...} is unbounded, makes sense 👍
@inland patio consider .close if ur question is answered
yes 👍
thanks for the help!
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water is filled up to 8
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i really don’t know what i’m looking at
fighting the urge to use gpt
Use the point (1/2,4) to split the region into 2 parts
Nah, don't.
but would i integrate with x or y
Depends on your preference. I usually default to x.
x
Wdym of course it's x 
You are not rotating anything so it doesnt matter
y works as well tho haha
if i split it in half wouldnt using y-axis be easier
bc it’s a straight line pmuch
They ask...tricky questions on exams sometimes. It should be an open response.
its the same basically
Yeah you use a straight line x=1/2 to split it
It's still using 2 curves. So, in terms of work, it's not necessarily better.
with respect to y you get square roots wich is uglier
rahhh i hate calculus
this is just the beginning :)
what…
it gets worse haha
8 units of hell and it’s only the beginning 💔
trust me, you'll love it when you get deeper
let’s pause on that one
Wait until you see real analysis. Then you'll cry tears of blood.
i already do
rip
do you know how to find the area under a curve?
integrate
do you know how to find the area between two curves?
okay what exactly are you stuck on then
they were always in line with either the x or y axis
and it told me what to use
so it was easy
ahh wel here its obviously x axis except if you like pain and want to work with roots, but it shouldn't matter which one you pick
Ok.
Well, try to figure out which functions you should integrate (i.e., which is greater than the other on the partitioned intervals?)
Most of the time choosing which axis only matters when it comes to volume of solid of revolution
This is area so it doesnt matter, plus, going for y is even harder
okay so would i find the area under 2 and then another 2 and then another 2?
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can someone please help me on this question
what's the question?
be patient my computer is lagging right now
nevermind i don't need help anymore
.close
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it's right
Closed by @kindred dagger
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i dont get this
what did I do wrong with the formula?
,, \proj_{\vec b}\vec a = \frac{\vec a \cdot \vec b}{\vec b\cdot \vec b}\vec b
cloud
how do I find the orthagonal complemetn?
(projection) + (orthogonal complement) = original vector
what is $\vec a\cdot \vec b$?
cloud
@crude slate Has your question been resolved?
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A tower stands vertically at the base of a hill that inclines upwards at 30 degree to the horizontal. from a point 25 metres from the base of the tower and directly up the hill the tower subtends an angle of 52 find the height of the tower giving your answer correctt to the nearest metre
how do i draw this diagram
i dont understand the part where it subtends an angle of 52
if you draw a line between the top of the tower and that point
the angle between that line, and the hill, is 52 degrees
so is it angle of depression?
i drew the tower and thehill going up
so i draw a dotted line from the base tower
and then it ends at the point
where do i do the 52
blue angle is 52
sorry for the quality why is my Paint set to 77 by 128 pixels
yes except the tower needs to be taller
because if that dotted line was horizontal , then the 52 would be 30
find the height of that point on the hill
and also the horizontal distance from the tower to that point
for height u would use sin right
construct OA to be parallel to the bottom line
oh 
yeah true
the angle would be negative
ok so like this
cant u find the angles inside the triangle cuz it says its a vertical
hmm
probably lower the point down
u want the 52 to be a bigger angle and 30 to become smaller?
would it rlly matter
i just need the logic behind it
tower higher
probably not, but you would get a confusing answer if the diagram is misleading
you can see what happens if it's higher
yeah it needs to be lower
exactly
yess
now draw a line straight down from that point with the 52 angle to make a triangle
yeah
then y= 21.25
@fleet sonnet is law of sine allowed?
ye
im doing questiosn related to law of sine cosine
0.5
oh-
then you don't need the orange line, you can use the triangle that's already there
but its just easier without law of sine
oh
lmao
sine is easier tho
wait i mean sine ye
what if i forgot about both these formulas
what do i do
without using them
u never nkow bruh i might forget during test
okay
yes that
we find x using cos right
yes so 21.65
then d is tan(22) * x, and h is y + d
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hi! im prepping for a middle school competition and this was one of the questions last year, what do i do for b)?
(im supposed to find Q, by the way)
i think its gonna be something to do with pressure right? cause they put that its open to the atmosphere
also for a) i have d = this thing
@chilly cobalt Has your question been resolved?
<@&286206848099549185>
@chilly cobalt Has your question been resolved?
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When deriving tangent equations for parabolas, why does the conversion using the slope form involve replacing ( a ) with (-a) (e.g. going from ( y^2 = 4ax ) to ( y^2 = -4ax )), while in the parametric form the conversion is achieved by substituting ( x ) with (-x)? What is the reason behind these different transformation approaches in the two forms of the tangent equations?
The modest AI
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how would i do this question A parallelogram has sides of length 3x7 cm and 6x8cm and the acute angle between the sides is 48. find the lengths of the diagonals of the parallelogram
nah nvm
draw a diagram first
of the parallelogram
and indicate what you know about is given in it
okay wait a bit
what an modest ai would tell.
Thats why he is here lol.
You could just do this with the cosine rule
whats the bottom one for
magnitude of diagonal
how
R
it is the resultant
This is much simpler
i have no clue what that is
the question should be asking me to solve using cosine or sine law
Look it up
Oh
So you're restricted to those two ideas
It's simpler so it'd make sense
simple
ye i want a simple way
use cosine rule
i know cosine law formula
Hear me out
okay
Draw the other diagonal
oka y wait
And use the fact that the bottom is also 6x8
yes
oh so then u just do x= 21^2 + 48^2 - 2x48x21 cos 48
that would be the same with the other way right
Yep
So there's (a-b)^2 instead in the cosine rule... The resultant seems more apt for this problem
Remember to root that
huh
You won't get it
Algebraic Simplification
ye i dont get it
It's alright
Dw abt it you'll notice it after getting familiar with the rule
No
This
It's algebra
oh ok
The resultant is vector relatedhowever

