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xenn [34]
3 years ago
12

Select two points that the line would go through. Practice graphing it on a piece of paper in front of you.

Mathematics
1 answer:
Aloiza [94]3 years ago
4 0

Answer:

(2, -2) and (1,-3)

Step-by-step explanation:

The y-intercept means that the coordinate where the line touches the y-axis. In other words, the y value(-4) when x=0. The result is (0, -4). The y coordinate also can be used to fill c variable of y=mx + c

We have 2 coordinates now, (3, -1) and (0, -4), then we can find out the line equation. We need to find the slope (m), the formula will be:

m= y2-y1 / x2-x1=  

m= -4 - (-1) / (0-3)

m= -3/-3= 1

The line equation is:

y= mx + c

y= 1x + (-4)

y= x-4

After you have the equation, you can try to test the point individually to find if any of them fit the equation. You can also draw a line on the graph and see if any point touches the graph.

Red dot is the two point we have, red line is straight line that made from both point, and blue point is the option. As you can see on the graph, the points that touch the line will be (2, -2) and (1,-3)

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Answer:

(a) The correct option is: <em>H₀</em>: <em>p</em> = 0.02 vs. <em>Hₐ</em>: <em>p</em> < 0.02.

(b) Explained below.

(c) The better value of <em>α</em> will be 0.10.

Step-by-step explanation:

An automobile manufacturer is considering using robots for part of its assembly process only if there is strong evidence that the proportion of defective installations is less for the robots than for human assemblers.

To test whether the proportion of defective installations is less for the robots than for human assemblers use a single-proportion <em>z</em>-test.

(a)

The hypothesis can be defined as:

<em>H₀</em>: The proportion of defective installations is same for both the robots and  human assemblers, i.e. <em>p</em> = 0.02.

<em>Hₐ</em>: The proportion of defective installations is less for the robots than for human assemblers, i.e. <em>p</em> < 0.02.

The alternate hypothesis is the claim or the statement that is being tested.

In this case we need to test whether the proportion of defective installations is less for the robots than for human assemblers or not, so that the manufacturer can decide whether they want to apply the conversion.

Thus, the correct option is:

<em>H₀</em>: <em>p</em> = 0.02 vs. <em>Hₐ</em>: <em>p</em> < 0.02.

(b)

A type I error occurs when we discard a true null hypothesis and a type II error is made when we fail to discard a false null hypothesis.

In this case a type I error will be committed if conclude that the proportion of defective installations is less for the robots than for human assemblers when in fact it is not.

And a type II error will be committed if we fail to conclude that proportion of defective installations is less for the robots than for human assemblers.

(c)

The power of the test is the probability of rejecting a false null hypothesis.

The power of the test sis affected by the significance level of the test (<em>α</em>).

Lesser the significance level of the test the lesser is the power of the test.

If the value of <em>α</em> is reduced from 0.05 to 0.01 then the region of acceptance will increase. This implies that there is low probability of rejecting the null hypothesis even when it is false.

So higher the value of <em>α</em> the higher is the probability of making a correct decision.

Thus, the better value of <em>α</em> will be 0.10.

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