Answer:
ay i use this- technically isnt cheating bc my teachers didnt block it so..
math calculator for exponent thingys:
https://www.mathpapa.com/calc.html
-and i think you combine them..maybe-
Answer:
4a-b-c
Step-by-step explanation:
we first look at the letters of each number. if there is only a letter, there is an imaginary 1 in front of it. we need to make sure that we add the numbers that have the exact same letters because they are known as like terms. we cannot add 6a and 4b because the letters are different and they are known as unlike terms. When we add numbers with the same letter ONLY the number changes
Answer:
$57.63
Step-by-step explanation:
For x > 3, the price in dollars Mr. Roshan will pay for x pounds of beef is ...
cost = 4.99×3 +4.49(x -3) = 3(4.99 -4.49) +4.49x
cost = 1.50 +4.49x
Then for x = 12.5 pounds, the cost is ...
cost = 1.50 +4.49×12.5 = 1.50 +56.13
cost = 57.63
Mr. Roshan will pay $57.63 for 12.5 pounds of beef.
Answer: 650
Step-by-step explanation:
When prior estimate of population proportion is known , then the formula to find the required sample size is given by :-
, where p= population proportion
E= margin of error
z* = Critical value.
Let p be the proportion of adults able to identify a Toyota Scion by brand and model name.
As per given , we have
p = 12%= 0.12
E= 2.5%=0.025
Critical value for 95% confidence interval : z* = 1.960 [By z-table ]
Then, the required sample size = 
Thus , the required sample size = 650
Distributionally robust stochastic programs with side information based on trimmings
This is a research paper whose authors are Adrián Esteban-Pérez and Juan M. Morales.
Abstract:
- We look at stochastic programmes that are conditional on some covariate information, where the only knowledge of the possible relationship between the unknown parameters and the covariates is a limited data sample of their joint distribution. We build a data-driven Distributionally Robust Optimization (DRO) framework to hedge the decision against the inherent error in the process of inferring conditional information from limited joint data by leveraging the close relationship between the notion of trimmings of a probability measure and the partial mass transportation problem.
- We demonstrate that our technique is computationally as tractable as the usual (no side information) Wasserstein-metric-based DRO and provides performance guarantees. Furthermore, our DRO framework may be easily applied to data-driven decision-making issues involving tainted samples. Finally, using a single-item newsvendor problem and a portfolio allocation problem with side information, the theoretical findings are presented.
Conclusions:
- We used the relationship between probability reductions and partial mass transit in this study to give a straightforward, yet powerful and creative technique to expand the usual Wasserstein-metric-based DRO to the situation of conditional stochastic programming. In the process of inferring the conditional probability measure of the random parameters from a limited sample drawn from the genuine joint data-generating distribution, our technique generates judgments that are distributionally resilient to uncertainty. In a series of numerical tests based on the single-item newsvendor issue and a portfolio allocation problem, we proved that our strategy achieves much higher out-of-sample performance than several current options. We backed up these actual findings with theoretical analysis, demonstrating that our strategy had appealing performance guarantees.
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