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inysia [295]
3 years ago
5

The graph shown matches which quadratic equation?

Mathematics
2 answers:
mina [271]3 years ago
5 0
(2,0) ; 2 units to the right
so answer is
C.  y = (x - 2)^2
Tatiana [17]3 years ago
4 0
I think it's D but I'm not %100 sure
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How does recipe conversion factors work? For example, if I have a soup that is originally 24 servings and has 16 oz of fish, and
Neporo4naja [7]

You have to find the unit rate, which in this case means the amount of fish you need per serving of soup.

If 16 oz of fish makes 24 servings, then 16/24 oz = 2/3 oz makes 1 serving.

Now multiply both quantities by 10, so that 10 servings require 20/3 oz ≈ 6.67 oz of fish.

A more direct way to do this is to solve for <em>x</em> in the following equation:

(16 oz fish) / (24 servings) = (<em>x</em> oz fish) / (10 servings)

Then (omitting units)

<em>x</em> = 10 • 16 / 24 = 20/3

7 0
3 years ago
Are they equivalent please hurry
hodyreva [135]

Answer:

no there not

Step-by-step explanation:

4 0
2 years ago
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The formula to convert °F to °C is C = (F – 32).<br><br>Convert 50°C to °F
Harman [31]

50*C x 9/5 + 32 = 122 *F


4 0
3 years ago
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Select the correct answer from each drop down menu. In the figure, AB=__inches and AC=___
BaLLatris [955]

Answer: In the figure AB is about 8.4 inches and AC is about 13.05 inches.

Step-by-step explanation: We can use cosine to find the hypotenuse. cos(40)=\frac{10}{x} \\cos(40) (x)=\frac{10}{x}(x)\\cos(40) (x) =10\\\frac{cos(40) (x)}{cos (40)} =\frac{10}{cos (40)} \\x=\frac{10}{cos(40)}

Using a calculator x is about 13.05

Using tangent we can find the length opposite of <C

tan(40)=\frac{x}{10} \\tan(40) (10)=\frac{x}{10}(10)\\tan(40) (10) = x

Using a calculator x would be about 8.4

4 0
3 years ago
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A set of bivariate data was used to create a least squares regression line. Which of the following is minimized by the line
77julia77 [94]

Answer:

B) The sum of the squared residuals

Step-by-step explanation:

Least Square Regression Line is drawn through a bivariate data(Data in two variables) plotted on a graph to explain the relation between the explanatory variable(x) and the response variable(y).

Not all the points will lie on the Least Square Regression Line in all cases. Some points will be above line and some points will be below the line. The vertical distance between the points and the line is known as residual. Since, some points are above the line and some are below, the sum of residuals is always zero for a Least Square Regression Line.

Since, we want to minimize the overall error(residual) so that our line is as close to the points as possible, considering the sum of residuals wont be helpful as it will always be zero. So we square the residuals first and them sum them. This always gives a positive value. The Least Square Regression Line minimizes this sum of residuals and the result is a line of Best Fit for the bivariate data.

Therefore, option B gives the correct answer.

5 0
2 years ago
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