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Y_Kistochka [10]
3 years ago
7

Number 7 and 8 please

Mathematics
1 answer:
Ierofanga [76]3 years ago
6 0

7. You are given two angles and their sum, all in terms of x. Set the sum equal to the sum:

... (4x -1) +(2x +15) = (8x +8)

... 6 = 2x . . . . collect terms, subtract 6x+8

... 3 = x . . . . . divide by 2

To find the angles, fill in this value for x

∠AOB = 4·3 -1 = 11

∠BOC = 2·3 +15 = 21

∠AOC = 8·3 +8 = 32

8. Same deal.

... ∠BOC + ∠COD = ∠BOD

... (3x -10) +(8x +13) = (12x -6)

... 9 = x . . . . . . . . subtract 11x-6

And the angle values are ...

∠BOC = 3·9 -10 = 17

∠COD = 8·9 +13 = 85

∠BOD = 12·9 -6 = 102

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Find each missing measure.
Anni [7]

Answer:

m∠E= 37°

m∠F=37°

Step-by-step explanation:

Total angle value: 180°

180-106=74

The lengths are the same, so we can determine that the angles E and F are the same. So we divide 74 by 2

74/2= 37

m∠E= 37°

m∠F=37°

Hope this helps! Have a nice day :)

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Draw a quick picture 143 divided by 11 equals 13
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Find the equation of the line which is parallel to y = 4x + 3 and goes through the point (4, 5)​
skad [1K]

Answer:

  • y = 4x - 11

Step-by-step explanation:

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We know parallel lines have equal slopes.

<u>Use point - slope equation and substitute the coordinates of the given point to get:</u>

  • y - 5 = 4(x - 4)
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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.
  • 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 :

brainly.com/question/11234923

#SPJ4

7 0
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