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valentina_108 [34]
2 years ago
11

A woman drives an SUV that gets 14 mi/gal (mpg). Her husband drives a hybrid that gets 65 mpg. Every week, they travel the same

number of miles. They want to improve their combined mpg. They have two options on how they can improve it.
Option 1: They can tune the SUV and increase it mileage by 2 mpg and keep the hybrid as it is.

Option 2: They can buy a new hybrid that gets 85 mpg and keep the SUV as it is.

Compute the combines mpg for each option.

Option 1 Answer? : Option 2 Answer? :

(round to one decimal place as needed.
Mathematics
2 answers:
VashaNatasha [74]2 years ago
4 0

Answer:

Option 1 is the best one

Step-by-step explanation:

Let's call x the number of miles travelled by the woman. Then the combined miles travelled are 2*x  .

The number of gallons consumed is, for example, x/14 for the original SUV

.

The combined mpg is: combined miles travelled/combined gallons consumed. For the original case:  

\frac{2*x}{\frac{x}{14}+\frac{x}{65}}

\frac{2*x}{\frac{79}{910}*x}

\frac{2*910}{79}

23 \; mpg

Option 1:

\frac{2*x}{\frac{x}{16}+\frac{x}{65}}

\frac{2*x}{\frac{81}{1040}*x}

\frac{2*1040}{81}

25.7 \; mpg

Option 2:

\frac{2*x}{\frac{x}{14}+\frac{x}{85}}

\frac{2*x}{\frac{99}{1190}*x}

\frac{2*1190}{99}

24 \; mpg

Oksanka [162]2 years ago
3 0
You can work this problem the same way the similar problem was worked at
brainly.com/question/8725431

Option 1: 2/(1/16 +1/65) = 25.7 mpg

Option 2: 2/(1/14 +1/85) = 24.0 mpg

_____
As before, improving the least-efficient vehicle gives best resuls.
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Step-by-step explanation:

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2 years ago
El producto de dos números consecutivos es 552 cuáles son esos números?
lara31 [8.8K]

Answer: 23 y 24 ( ó -23 y -24)

Step-by-step explanation:

Dos números consecutivos se escriben como:

n y (n + 1)

done n es un numero entero.

Entonces "El producto de dos números consecutivos es 552"

Se escribe como:

n*(n + 1) = 552

n^2 + n = 552

n^2 + n - 552 = 0

Tenemos una cuadrática, las posibles soluciones son obtenidas con la formula de Bhaskara.

n = \frac{-1 +- \sqrt{1^2 - 4*1*(-552)} }{2} = \frac{-1 +- 47}{2}

Las dos soluciones son.

n = (-1 - 47)/2 = -48/2 = -24

n = (-1 + 47)/2 = 46/2 = 23

Si tomamos la primer solución, n = -24

Entonces los dos números consecutivos son:

n = -24

(n + 1) = -23

Si n = 23 entonces

n + 1 = 24

Lo cual tiene sentido, por que lo único que cambia son los signos, los cuales se cancelarían en la multiplicación.

7 0
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Answer:

B) 5 (x minus 3)

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tep-by-step explanation:

5x minus 15

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5(x - 3)

4 x + 3 y minus 15 minus 3 y + x

4x + 3y - 15 - 3y + x

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-20 - 3x + 5 + 8x

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Read 2 more answers
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Answer:

(C) The slope of the regression equation is not significantly different from zero

Step-by-step explanation:

Let's suppose that we have the following linear model:

y= \beta_o +\beta_1 X

Where Y is the dependent variable and X the independent variable. \beta_0 represent the intercept and \beta_1 the slope.

In order to estimate the coefficients \beta_0 ,\beta_1 we can use least squares estimation.

If we are interested in analyze if we have a significant relationship between the dependent and the independent variable we can use the following system of hypothesis:

Null Hypothesis: \beta_1 = 0

Alternative hypothesis: \beta_1 \neq 0

Or in other wouds we want to check is our slope is significant.

In order to conduct this test we are assuming the following conditions:

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b) We have the same probability distribution for the variable Y with the same deviation for each value of the independent variable

c) We assume that the Y values are independent and the distribution of Y is normal

The significance level is provided and on this case is \alpha=0.05

The standard error for the slope is given by this formula:

SE_{\beta_1}=\frac{\sqrt{\frac{\sum (y_i -\hat y_i)^2}{n-2}}}{\sqrt{\sum (X_i -\bar X)^2}}

Th degrees of freedom for a linear regression is given by df=n-2 since we need to estimate the value for the slope and the intercept.

In order to test the hypothesis the statistic is given by:

t=\frac{\hat \beta_1}{SE_{\beta_1}}

The confidence interval for the slope would be given by this formula:

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And using the last formula we got that the confidence interval for the slope coefficient is given by:

-0.88 < \beta_1

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