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Tema [17]
2 years ago
7

Chuck has a gross pay of $815.70. By how much will Chuck’s gross pay be reduced if he has the following items withheld? federal

tax of $56 Social Security tax that is 6.2% of his gross pay Medicare tax that is 1.45% of his gross pay state tax that is 19% of his federal tax
Mathematics
2 answers:
drek231 [11]2 years ago
8 0

Answer:

THE ANSWER IS B.

$129.04

Step-by-step explanation:

tangare [24]2 years ago
3 0

Chuck has a gross pay of $815.70. His gross pay will be reduced by:

  • Federal tax of $56;
  • Social Security tax that is 6.2% of his gross pay;
  • Medicare tax that is 1.45% of his gross pay;
  • State tax that is 19% of his federal tax.

Let's count:

1. gross pay of $815.70 - 100%,

Social Security tax of $x - 6.2%.

Then

\dfrac{815.70}{x}=\dfrac{100}{6.2} ,\\ \\815.70\cdot 6.2=x\cdot 100,\\ \\x=\dfrac{815.70\cdot 6.2}{100} =50.5734.

2. gross pay of $815.70 - 100%,

Medicare tax of $y - 1.45%.

Then

\dfrac{815.70}{y}=\dfrac{100}{1.45} ,\\ \\815.70\cdot 1.45=y\cdot 100,\\ \\y=\dfrac{815.70\cdot 1.45}{100} =11.82765.

3. Federal tax of $56 - 100%,

State tax $z - 19%.

Then

\dfrac{56}{z}=\dfrac{100}{19} ,\\ \\56\cdot 19=z\cdot 100,\\ \\z=\dfrac{56\cdot 19}{100} =10.64.

4. Chuck’s gross pay will be reduced by

\$56+\$50.5734+\$11.82765+\$10.64=\$129.04105\approx \$129.04.

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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:

a) We have linear relationship between Y and X

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:

\hat \beta_1 + t_{n-2, \alpha/2} \frac{\sqrt{\frac{\sum (y_i -\hat y_i)^2}{n-2}}}{\sqrt{\sum (X_i -\bar X)^2}}

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

-0.88 < \beta_1

IF we analyze the confidence interval contains the value 0. So we can conclude that we don't have a significant effect of the slope on this case at 5% of significance. And the best option would be:

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

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given the area of a rectangle is 14,628 square millimeters and the width is 12 millimeters find the length
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faltersainse [42]
<h2>Answer</h2>

She will be scoring above 85 after her 7 assessment.

<h2>Explanation</h2>

We can model this situation using a line equation.

We know that on her first assessment, Rita scored 58 points, so our first point is (1, 58)

We also know that on her second assessment, Rita scored 63 points, so our second point is (2, 63)

To find the rate, which is the slope of our line, we are using the slope formula:

m=\frac{y_{2}-y_{1}}{x_{2}-x_{1}}

where

(x_{1},y_{1}) are the coordinates of the first point

(x_{2},y_{2}) are the coordinates of the second point

From our points we can infer that x_{1}=1, y_{1}=58, x_{2}=2, y_{2}=63. Let's replace the values in our slope formula:

m=\frac{y_{2}-y_{1}}{x_{2}-x_{1}}

m=\frac{63-58}{2-1}

m=\frac{5}{1}

m=5

Now we know that Rita's score is increasing 5 points every assessment.

To complete the equation of our line, we are using the point slope formula:

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y-58=5(x-1)

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Finally, we just need to replace y with 85 in our line equation and solve for x to find after which assessment she will score above 85:

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