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slega [8]
2 years ago
14

A restaurant earns $1073 on Friday and $1108 write and solve an equation to find the amount X (in dollars) the restaurant needs

to earn on Sunday to average $1000 per day over the three day period. Write your equation so that the units on each side of the equation are dollars owe day
Mathematics
1 answer:
Murljashka [212]2 years ago
6 0
($1,073 + $1,108 +$X)/3 =$1,000
$2,181 + $X = 3 x $1,000 = $3,000
$X = $3,000 - $2,181 = $819
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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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