Create a graph that has the following intercepts. Make sure to label each of the intercepts on
1 answer:
Answer:
see below for the graph
Step-by-step explanation:
The desired graph has two y-intercepts and one x-intercept. It is not the graph of a function.
Here's one way to get there.
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Start with the parent function y = |x| and scale it down so that it has a y-intercept of -1 and x-intercepts at ±1.
Now, it is ...
f(x) = |x| -1
We want to scale this vertically by a factor of -5. this puts the y-intercept at +5 and leaves the x-intercepts at ±1.
Horizontally, we want to scale the function by an expansion factor of 3. The transformed function g(x) will be ...
g(x) = -5f(x/3) = -5(|x/3| -1) = -5/3|x| +5
This function has two x-intercepts at ±3 and one y-intercept at y=5. By swapping the x- and y-variables, we can get an equation for the graph you want:
x = -(5/3)|y| +5
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<em>Comment on this answer</em>
Since there are no requirements on the graph other than it have the listed intercepts, you can draw it free-hand through the intercept points. It need not be describable by an equation.
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Answer:
The approximate probability is 0.1921.
Step-by-step explanation:
The <em>p</em>-value is defined as the probability, [under the null-hypothesis (H₀)], of attaining a result equivalent to or greater than what was the truly observed value of the test statistic.
The hypothesis to test whether the proportion of consumers that plan to buy a new TV screen within the next year is 0.22, is defined as:
<em>H₀</em>: The proportion of consumers that plan to buy a new TV screen within the next year is 0.22, i.e. <em>p</em> = 0.22.
<em>Hₐ</em>: The proportion of consumers that plan to buy a new TV screen within the next year is 0.22, i.e. <em>p</em> > 0.22.
The information provided is:
<em>n</em> = 230
<em>X</em> = 56
Compute the value of sample proportion as follows:
Compute the test statistic as follows:
The test statistic value is, 0.87.
Compute the <em>p</em>-value as follows:
*Use a <em>z</em>-table for the probability.
The <em>p-</em>value of the test is 0.1921.
Thus, the approximate probability of obtaining a sample proportion equal to or larger than the one obtained here is 0.1921.