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qaws [65]
3 years ago
7

-5-4-3

Mathematics
1 answer:
Burka [1]3 years ago
5 0

Answer:

Interval = (-2,1.5)

Interval = (-\infty, -2)\ u\ (1.5,\infty)

Step-by-step explanation:

Given

See attachment for graph

Solving (a): Increasing interval

To do this, we simply identify the interval at which the value of the graph increases.

The value has an increased interval between -2 and 1.5 (of the x-axis).

Hence, the increasing interval is:

Interval = (-2,1.5)

Solving (b): Decreasing interval

To do this, we simply identify the interval at which the value of the graph decreases.

The value has decreased intervals between - infinity and -2 and also 1.5 and infinity (of the x-axis).

Hence, the decreasing interval is:

Interval = (-\infty, -2)\ u\ (1.5,\infty)

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A genetic experiment involving peas yielded one sample of offspring consisting of 409 green peas and 171 yellow peas. Use a 0.05
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The usual expectation for this kind of experiment is that the peas would yield green and yellow peas in a 3:1 ratio, or 75% green to 25% yellow. So your null hypothesis is that the proportion of yellow peas is p=0.25.

You're testing the claim that 26% of the offspring will be yellow, which means the alternative hypothesis is that the proportion of yellow peas is actually 0.26, or more generally that the expected proportion is greater than 0.25, or p>0.25.

The test statistic in this case will be

Z=\dfrac{\hat p-p_0}{\sqrt{\dfrac{p_0(1-p_0)}n}}

where p_0 is the proportion assumed under the null hypothesis, \hat p is the measured proportion, and n is the sample size. You have p_0=0.25, \hat p=\dfrac{171}{171+409}\approx0.29, and n=409+171=580, so the test statistic is

Z=\dfrac{0.29-0.25}{\sqrt{\dfrac{0.25\times0.75}{580}}}\approx2.2247

Because you're testing p>p_0, this is a right-tailed test, so the P-value is

\mathbb P(Z>2.2247)\approx0.0131

The critical value for a right-tail test at a 0.05 significance level is Z_\alpha\approx1.6449, which means the rejection region is any test statistic that is larger than this critical value. Since Z>Z_\alpha in this case, we reject the null hypothesis.

So the conclusion for this test is that the sample proportion is indeed statistically significantly different from the proportion suggested by the null hypothesis.
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