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aleksklad [387]
3 years ago
11

Jia spends $8 on 4 pounds of fruit. How much would he pay for one pound of fruit?

Mathematics
2 answers:
e-lub [12.9K]3 years ago
5 0

Answer:

$2 per pound

Step-by-step explanation:

$8 / 4 pounds = $2 / 1 pound

krek1111 [17]3 years ago
4 0

Answer:

2 dollars

Step-by-step explanation:

For 4 pounds of fruit he pays 8 dollars. If you use the unit rate it is 2 dollars for every one pound of fruit

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lutik1710 [3]

Answer:

3/5

Step-by-step explanation:

There are 12 window seats and 8 aisle seats giving us a total of 20 seats.

12/20 = 3/5

Therefore, the probably the next person will be assigned a window seat is 3/5.

5 0
3 years ago
a. esteban-perez and j. m. morales ´ , distributionally robust stochastic programs with side information based on trimmings, mat
sleet_krkn [62]

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.

To learn more about probability, visit :

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A horse travels 100 meters at a velocity of 30m/s W. How long does it take the horse to travel this distance?
Dafna1 [17]

The formula for finding time is the distance divided by the velocity.So the answer is 100÷30=3.3333...

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3 years ago
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Serga [27]

Answer:

D

Step-by-step explanation:

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Ipatiy [6.2K]

Answer:

x=5

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or

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Step-by-step explanation:

I guess you can make it up as long as it equals 15, I guess that's what the question is saying??

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