The passage indicates that the farm's managers reacted to Nawab's receipt of a motorcycle with disgust. The correct answer is A.
Given a passage shown below in the attached pictures.
Option A is the best answer. The passage says that Nawab's new motorcycle led to "the disgust of the ranchers" (line 7 ).
Options other than A are B, C, and D are incorrect because the passage specifically states that Nawab's new motorcycle resulted in "the disgust of the ranchers", not happiness. their happiness (option B), their jealousy (option C) or their indifference (option D). The passage is therefore given that the managers of the farm reacted to Nawab's receipt of a motorcycle with disgust.
Learn more about the passage from here brainly.com/question/12555695
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<span>A parallelogram is a 4-sided shape where opposites sides are parallel. A rectangle is a special case of a parallelogram. All rectangles are parallelograms.
But a rectangle is a shape where opposites sides are parallel *and* all the corners are 90 degree angles. So you can't say that all parallelograms would be rectangles. Some parallelograms would be rectangles, but not all.
Rectangles are a subset of the shapes called parallelograms. But parallelograms are *not* a subset of the shapes called rectangles.
It's similar to saying all cars are vehicles. But you can't say that all vehicles are cars. </span>
Answer:
0.25
Step-by-step explanation:
Answer:
the rate of change is 5
Step-by-step explanation:
$90 / 18 cars = $5 per car
$45 / 9 cars = $5 per car
$60 / 12 cars = $5 per car
That means the rate of change is 5 (5/1)
Looks like a badly encoded/decoded symbol. It's supposed to be a minus sign, so you're asked to find the expectation of 2<em>X </em>² - <em>Y</em>.
If you don't know how <em>X</em> or <em>Y</em> are distributed, but you know E[<em>X</em> ²] and E[<em>Y</em>], then it's as simple as distributing the expectation over the sum:
E[2<em>X </em>² - <em>Y</em>] = 2 E[<em>X </em>²] - E[<em>Y</em>]
Or, if you're given the expectation and variance of <em>X</em>, you have
Var[<em>X</em>] = E[<em>X</em> ²] - E[<em>X</em>]²
→ E[2<em>X </em>² - <em>Y</em>] = 2 (Var[<em>X</em>] + E[<em>X</em>]²) - E[<em>Y</em>]
Otherwise, you may be given the density function, or joint density, in which case you can determine the expectations by computing an integral or sum.
