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Allushta [10]
3 years ago
7

A college bookstore ordered six boxes of red pens. The store sold 28 red pens last week and 38 red pens this week. 6 pens were l

eft on the shelf. How many pens were in each​ box?
Mathematics
2 answers:
dezoksy [38]3 years ago
8 0

Answer:

Step-by-step explanation:

28 + 38 = 66

66 divided by 6 = 11

This means that there would be 11 pens in each box.

ANTONII [103]3 years ago
4 0

Answer: there were 12 red pens in each box

Step-by-step explanation:

Let x = the number of red pens that were initially in each box.

The college bookstore ordered six boxes of red pens.

This means that the total number of red pens ordered is 6×x = 6x

The store sold 28 red pens last week and 38 red pens this week. This means that the total number of red pens sold = 28 + 38 = 66

The number of red pens left will be the total number of red pens minus the number of red pens that were sold. It becomes 6x - 66

6 pens were left on the shelf. This means that

6x - 66 = 6

6x = 6 + 66 = 72

x = 72/6 = 12

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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.
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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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