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finlep [7]
1 year ago
15

Select the inequality that corresponds to the given graph. graph of an inequality with a solid line through the points negative

8 comma 0 and 0 comma negative 4 and shading above the line A 4x − 3y > − 12 B x + 4y > 4 C 4x − 2y < − 8 D 2x + 4y ≥ − 16

Mathematics
2 answers:
SashulF [63]1 year ago
6 0
Well by looking at the graph I can see that the equation for the line is y=-0.5x-4
rearranging I can get the answer.
y>=-0.5x-4
y+0.5x>=-4
2y+x>=-8



the answer is
2x+4y>=-16
Dominik [7]1 year ago
4 0
<h3>Answer: 2x+4y ≥ -16 </h3>

Step-by-step explanation:  In the given graph x-intercept is -8 and y-intercept is -4.

Also the graph is shaded up of the line for greater values of y's.

On the option 2x+4y ≥ -16 has x-intercept at -8 and y-intercept at -4.

Also all other inequalities has only less than or greater than sign.

In the given graph we have a solid line.

So, there should be greater than or equal to inequality sign for the inequality.

Only option 4 has greater than with equal to sign.

Therefore, correct option is 2x+4y ≥ -16



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So on this case for the significance level assumed \alpha=0.05 we see that p_v >\alpha so then we can conclude that the result is NOT significant. And we don't have enough evidence to reject the null hypothesis.

So on this case is not appropiate say that :"the more we spend on advertising this product, the fewer units we sell" since the slope for this case is not significant.

Step-by-step explanation:

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

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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 procedure.  

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 words we want to check is our slope is significant (X have an effect in the Y variable )

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 assumed 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}}

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

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The p value on this case would be given by:

p_v = 2*P(t_{n-2} > |t_{calc}|)= 0.91

So on this case for the significance level assumed \alpha=0.05 we see that p_v >\alpha so then we can conclude that the result is NOT significant. And we don't have enough evidence to reject the null hypothesis.

So on this case is not appropiate say that :"the more we spend on advertising this product, the fewer units we sell" since the slope for this case is not significant.

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