Mathematic Problem ?
Four bucks.
4 x 0.6 = 2.4
So, $4 - $2.4 = $1.60
...I think.
Correct Stu. But if Peter has to show the workings for his Year 7 homework, he should show it this way:
$1.60 / 0.4
= 1.60 / (4/10)
= (1.60 x 10) / 4
= 16.00 / 4
= 4
How much wood would a wood chuck chuck if a wood chuck could chuck wood ?
Answer in lineal metres please.
The whole planet and then some. Again, and again, and again.
Same old same old, with variety to keep it real different but.
Or else we'll shoot ya. Democratically of course. Again, and again, and again.
But never chuck it the same way twice... or we'll shoot yas... again... amesy's gort a war to fight aye!!!! Unnuver won aye!!!! Go amesy... we lurv ya mmmmaaayyytte!!!!!
What... another one.... again....
Shut up or we'll shoot yas!!!
The tide's moving at snappy rocks. Fucker keeps doing the same thing!!!! Blow the the fucking tide up!!! We've managed to blot out the stinkin' fuckin' sun... at snappy rorcks!!! Rab's is stoked!!! Cunt of a fucking thing keeps risin' but.... new emporium on every street corner, chuck some more wood into the furnace!!! What's that shit... its a fuckin' lyre bird... fuck orf ya useless cunt, or we'll shoot ya too!!! In a nutshell!!??? Again and again...
Fuck ya 80,000 years, shoot the fuck'n cunts!!!! Never the same way twice but....
Its advanced!!!!
Uhh that’s the wrong answer lil Crippy
But how many beans make five?
You just see if ya can chuck ya fuck'n log over the edge ya goofy nutless numbskull!!!
I gotta go train people again. A guy with parkinson's today. Come and meet him. This arvo. Again. He's stoked!!! I always, without fail, again and again, wipe his sweat up for him, its a pleasure.
Crypto get off the roids
Ah yes, clues to a cryptic crossword puzzle again. Here’s one: who once named himself after a hulking, heaving, hairy-arsed Hellenic half-god?
Stu & Coaster are smart enough to know that Peter Pedestrian ain't doing year 7 maths.
Not so pedestrian at all, as our Peter is playing politics with crypto's currency converter.
Cryptic equation reads more like this...
$1.60 = Average U.S. inflation rate
60% is the $U.S. Greenback < > $Australian Dollar Conversion.
Year being 2018 then obviously the answer Peter is seeking is [1946]
(WW2 end) Being the only other year U.S debt outweighed GDP...
Meaning Trump can't technically pay off U.S. debt as they are officially bankrupt as of 2018.
All here recall that most recent D-Day deadline debacle.
Unlike Greece/Argentina that both use foreign currency...U.S. can't go broke!
U.S. Reserve prints more sovereign greenbacks for yet more world monopoly money.
Yes! They can & are bankrupting the Planet by not paying their record debt... End of World?
World is hanging on Professor Peter Pedestrian's calculations.
Fingers crossed that our fellow learned swellnetonians may have just saved Oz's bacon.
What about.
1.60 = x*40/100
Then 1.60*100 = x*40
160 = x*40
160/40 = x
x= 4
X is original price. Y is new price
X-60/100X = Y
X-60/100X = 1.60
40/100 X=1.60
X=1.60 x 100/40
therefore X=4
Nice loungey, very similar I just leftout the switch from 60% discount to actual price = 40%
everyone has got it right, teachers love an x and a y...
$4.00 . Yes . Correct.
I finally worked it out by equation. Saying that if 160 was 40 % of original price rhen finding 10% would get me thus.
160 = 40%
40 = 10%
400 cents = 100%
$4.00
Thank you for your efforts
That's a long way around to getting to it Peter, haha and if the numbers aren't as easy as that you'll get into a bit of trouble :p
Well done everybody, you can all get an "At Standard" for Year 7 Maths, Number & Algebra only, more work to do in other areas.
Good to see mathematical discussions on here...might see some of you at this mathematical logic seminar next week?
Thursday 22 November, 3pm - 5pm
The Muniment Room (room S401 Main Quadrangle Building) University of Sydney
A Gentle Introduction to Abstract Algebraic Logic
Petr Cintula (Czech Academy of Sciences, Prague)
Algebraic logic is the branch of mathematical logic that
studies logical systems by giving them algebraic semantics.
It mainly capitalizes on the standard Linbenbaum-Tarski
proof of completeness of classical logic w.r.t. the
two-element Boolean algebra, which can be analogously
repeated in other logical systems yielding completeness
w.r.t. other kinds of algebras. Abstract algebraic logic
(AAL) determines what are the essential elements in these
proofs and develops an abstract theory of the possible ways
in which logical systems can be related to an algebraic
counterpart. The usefulness of these methods is witnessed
by the fact that the study of many logics, relevant for
mathematics, computer science, linguistics or philosophical
purposes, has greatly benefited from the algebraic
approach, that allows to understand their properties in
terms of equivalent algebraic properties of their semantics.
This course is a self-contained introduction to AAL. We
start from the very basics of AAL, develop its general and
systematic theory and illustrate the results with
applications to particular examples of propositional logics.
Thanks chook. Blindboy would like to go but he sends his apologies because he will be attending this one about statistics on the following day:
https://www.maths.usyd.edu.au/u/SemConf/StatSem/
Friday November 23, 2pm, Carslaw 173
Munir Hiabu
University of Sydney, School of Mathematics and Statistics
Structured survival models with least squares backfitting
Linear models are great. They are very well understood, estimators can be quickly calculated via matrix algebra, and results have nice interpretations.
But there is one catch: They do not account for non-linearity.
The much hyped solution today for dealing with incredibly complex data is deep learning. They provide flexibility for fitting functions with very general shape.
But the downside here is that firstly, they need incredible amounts of replication to achieve an accurate fit and secondly, interpretation of the entering covariates is lost.
A middle ground solution is structured models.
Structured models are not as flexible as deep learning, but they still allow for enough non-linearity to be captured. Additionally, interpretation of the entering covariates is not lost. See here a Wikipedia article for the most prominent representative, (generalized) additive models, https://en.wikipedia.org/wiki/Additive_model.
The models are most often solved via a backfitting algorithm proposed by Buja, Hastie & Tibshirani (1989, Ann. Stat.). However, their backfitting algorithm is not derived from a proper optimisation criteria/loss function and is heuristic in nature. One major weakness from that is that this backfitting algorithm suffers problems with correlated covariates and multicollinearity.
In this talk I will propose least-squares motivated estimators which do not suffer this problem.
The least squares treatment will lead to integral equations of the second kind allowing for a nice mathematical treatment. I will mostly focus on survival models where interpretation is of the data is often key.
I gave my place to Blowin. He's the least square around here.
Gary is more of an inverted trapezoid
Fun for all the family, make your own Gary G using tangrams!
https://www.tangram-channel.com/tangram-puzzles/animals-easy1/
Today I am exactly 2/3rds of the way to being 100 years old. What day was I born on?
Thursday 17 April 1952?
Devil for a minute ay BB?
Right! I was looking at the date on my phone and it just occurred to me.
Here's a trip for you BB.
x*(1-.6)=1.6
.4x=1.6
x=1.6/.4
x=4
If the ticket price on an item is $1.60 and that current price is a 60% discount off the original price...what WAS the original price on the item. Please show work . Thank you