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Annette [7]
3 years ago
5

PLEASE HELP ASAP! ITS DUE SOON AND PLS SHOW UR WORK HELP PLSPLSPLS

Mathematics
2 answers:
patriot [66]3 years ago
4 0

Answer:

Step-by-step explanation:

6(2x+ 10) = x - 61

12x + 60 = x - 61

11x + 60 = -61

11x = -121

x = -11

6y= -11 - 61

6y = -72

y = -12

(-11, -12)

andre [41]3 years ago
4 0

y=2x+10

Swap sides so that all variable terms are on the left hand side.

2x+10=y

Subtract 10 from both sides.

2x=y−10

Divide both sides by 2.

2

2x

​  

=  

2

y−10

​  

 

Dividing by 2 undoes the multiplication by 2.

x=  

2

y−10

​  

 

Divide y−10 by 2.

x=  

2

y

​  

−5

SOLVE FOR Y

y=2(x+5)  

6y=x−61

Swap sides so that all variable terms are on the left hand side.

x−61=6y

Add 61 to both sides.

x=6y+61

SOLVE FOR Y

y=  

6

x−61

​  

 

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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.
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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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3 years ago
Trigonometry
Mashcka [7]

The area of the triangle EFG is 7.4 square units.

Explanation:

Given that the measurements of the sides of the triangle are EF = 8, EG = 8 and m\angle E=22^{\circ}

We need to determine the area of the triangle EFG

<u>Area of the triangle:</u>

The area of the triangle EFG can be determined using the formula,

\text {Area}=\frac{1}{2} fg \sin E

Substituting the values, we get,

\text {Area}=\frac{1}{2} (5)(8) \sin 22^{\circ}

Simplifying the values, we have,

\text {Area}=\frac{1}{2}(40)(0.37)

Multiplying, we get,

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Hence, the area of the triangle EFG is 7.4 square units.

3 0
3 years ago
In 2002, the Bookstore sold 1000 paperback books and 1500 hardback books. In 2003, the store sold 15% more paperback books and 5
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Given:
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2003 sold 15% more paperback books and 5% more hardback books.
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