Xavier earns $20 a day plus a fee

What is the Area of 20ft & 24ft?

olasank [31]

Answer:

480

Step-by-step explanation:

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7 0

3 years ago

PLEASE HELP 5x-2y=30 What is the y value

Ierofanga [76]

Answer:

y = 5/2x -15

Step-by-step explanation:

5x - 2y = 30

-2y = -5x + 30

y = $\frac{5}{2}$ x -15

7 0

4 years ago

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When we test Upper H 0: muequals0 against Upper H Subscript a: mugreater than0, we get a P-value of 0.03. a. What would the

Anastasy [175]

Answer:

(a) The null hypothesis will be rejected.

(b) Type I error

(c) The null hypothesis will not be rejected. The error is type II error.

Step-by-step explanation:

The hypothesis provided is:

$H_{0}: \mu = 0\\ H_{a} : \mu > 0$

The p-value of the test obtained is 0.03

(a)

Decision rule for hypothesis testing, based on p-value, states that if the p-value is less than the significance level (α) then the null hypothesis is rejected and vice versa.

The significance level is α = 0.10

Then,

$p-value = 0.03 < \alpha = 0.10$

Thus, the null hypothesis will be rejected.

Conclusion:

The null hypothesis is rejected stating that the value of μ is more than 0.

(b)

If the decision in (a) is an error, i.e. the null hypothesis is rejected when in fact it is true, this type of error is known as type I error.

(c)

The significance level is α = 0.01

Then,

$p-value = 0.03 > \alpha = 0.01$

Thus, the null hypothesis will not be rejected.

Conclusion:

The null hypothesis was not rejected stating that the value of μ is 0.

If this decision is an error, i.e. the null hypothesis was not rejected when in fact it is false, this type of error is known as type II error.

3 0

3 years ago

A group of researchers wants to know whether men are more likely than women to contract a novel coronavirus. They surveyed two r

olasank [31]

Answer:

Test statistic Z= 0.13008 < 1.96 at 0.10 level of significance

null hypothesis is accepted

There is no difference proportion of positive tests among men is different from the proportion of positive tests among women

Step-by-step explanation:

Step(I):-

Given surveyed two random samples of 390 men and 360 women who were tested

first sample proportion

$p_{1} = \frac{360}{390} = 0.9230$

second sample proportion

$p_{2} = \frac{47}{52} = 0.9038$

Step(ii):-

Null hypothesis : H₀ : There is no difference proportion of positive tests among men is different from the proportion of positive tests among women

Alternative Hypothesis:-

There is difference between proportion of positive tests among men is different from the proportion of positive tests among women

$Z = \frac{p_{1}- p_{2} }{\sqrt{PQ(\frac{1}{n_{1} }+\frac{1}{n_{2} } } }$

where

$P = \frac{n_{1}p_{1} +n_{2} p_{2} }{n_{1}+n_{2} }$

P = 0.920

$Z= \frac{0.9230-0.9038}{\sqrt{0.920 X0.08(\frac{1}{390}+\frac{1}{52} } )}$

Test statistic Z = 0.13008

Level of significance = 0.10

The critical value Z₀.₁₀ = 1.645

Test statistic Z=0.13008 < 1.645 at 0.1 level of significance

Null hypothesis is accepted

There is no difference proportion of positive tests among men is different from the proportion of positive tests among women

7 0

3 years ago

PLSSS HELLPPPPP ASAPPP

padilas [110]

I believe it would be 80

8 0

3 years ago

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