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SIZIF [17.4K]
2 years ago
10

The results of a mathematics placement exam at two different campuses of Mercy College follow: Campus Sample Size Sample Mean Po

pulation Standard Deviation 1 330 33 8 2 310 31 7 What is the alternative hypothesis if we want to test the hypothesis that the mean score on Campus 1 is higher than on Campus 2
Mathematics
1 answer:
Leona [35]2 years ago
5 0

Answer:

z=\frac{(33-31)-0}{\sqrt{\frac{8^2}{330}+\frac{7^2}{310}}}}=3.37  

p_v =P(Z>3.37)=1-P(Z  

Comparing the p value with a significance level for example \alpha=0.05 we see that p_v so we can conclude that we have enough evidence to reject the null hypothesis, and we can say that the mean for the Campus 1 is significantly higher than the mean for the group 2.  

Step-by-step explanation:

Data given

Campus   Sample size     Mean    Population deviation

   1                 330               33                      8

   2                310                31                       7

\bar X_{1}=33 represent the mean for sample 1  

\bar X_{2}=31 represent the mean for sample 2  

\sigma_{1}=8 represent the population standard deviation for 1  

\sigma_{2}=7 represent the population standard deviation for 2  

n_{1}=330 sample size for the group 1  

n_{2}=310 sample size for the group 2  

\alpha Significance level provided  

z would represent the statistic (variable of interest)  

Concepts and formulas to use  

We need to conduct a hypothesis in order to check if the mean for Campus 1 is higher than the mean for Campus 2, the system of hypothesis would be:

Null hypothesis:\mu_{1}-\mu_{2}\leq 0  

Alternative hypothesis:\mu_{1} - \mu_{2}> 0  

We have the population standard deviation's, and the sample sizes are large enough we can apply a z test to compare means, and the statistic is given by:  

z=\frac{(\bar X_{1}-\bar X_{2})-\Delta}{\sqrt{\frac{\sigma^2_{1}}{n_{1}}+\frac{\sigma^2_{2}}{n_{2}}}} (1)  

z-test: Is used to compare group means. Is one of the most common tests and is used to determine whether the means of two groups are equal to each other.  

With the info given we can replace in formula (1) like this:  

z=\frac{(33-31)-0}{\sqrt{\frac{8^2}{330}+\frac{7^2}{310}}}}=3.37  

P value  

Since is a one right tailed test the p value would be:  

p_v =P(Z>3.37)=1-P(Z  

Comparing the p value with a significance level for example \alpha=0.05 we see that p_v so we can conclude that we have enough evidence to reject the null hypothesis, and we can say that the mean for the Campus 1 is significantly higher than the mean for the group 2.  

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Answer:

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Step-by-step explanation:

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2 years ago
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CC Paper Company makes various types of paper products. One of their products is a 30 mils thick paper. In order to ensure that
tatuchka [14]

Answer:

Step-by-step explanation:

Hello!

X: Thickness of a cut of mils paper.

Sample

n= 256 cuts

X[bar]= 30.3 mils

S= 4 mils

a. The hypothesis is that the average thicknes of the paper is greater than 30 mils.

The hypotheses are:

H₀: μ ≤ 30

H₁: μ > 30

α: 0.01

You have no information about the variable distribution so you have to apply the central limit theorem and approximate the distribution of the sample mean to normal. Then you can use the approximation to the standard normal:

Z= (X[bar] - μ)/(S/√n) ≈ N(0;1)

This test is one-tailed to the right and it has only one critical value:

Z_{1-\alpha }= Z_{1-0.1}= Z_{0.90}= 1.28

The decision rule is:

If Z_{H_0} ≥ 1.28, you reject the null hypothesis.

If Z_{H_0} < 1.28, you don't reject the null hypothesis.

Z_{H_0}= \frac{(30.3-30)}{4/\sqrt{256} } = 1.2

The decision is to nor reject the null hypothesis, then at a significance level of 10% there is not enough evidence to reject the null hypothesis, the average thickness of the paper is less than 30 mils.

*-*

The distribution of the sample mean is:

X[bar]≈ N(μ;σ²/n)

The standard error of the mean is: \frac{S}{\sqrt{n} }= \frac{4}{\sqrt{256} }  = 0.25

b. Using the same approximation you can calculate the 95% CI:

[X[bar] ± Z_{1-\alpha /2} * \frac{S}{\sqrt{n} }]

Z_{1-\alpha /2}= Z_{1-0.25} = Z_{0.975} = 1.96

[30.3 ± 1.96 * \frac{4}{\sqrt{256} }]

[29.81 ; 30.79]

You can use the confidence interval to decide wheter or nor to reject the null hypothesis. If the interval contains the value of μ stated in the statistical hypothesis, then you don't reject the null hypothesis and if it is not contained, then you reject it. Since the value 30 mils is contained in the 95% confidence interval, you can conclude with a level of significance of 5% that the thickness of the papercuts is on average at most 30 mils.

c. Little reminder: The p-value is defined as the probability corresponding to the calculated statistic if possible under the null hypothesis (i.e. the probability of obtaining a value as extreme as the value of the statistic under the null hypothesis).

The region of the p-value is always the same type as the rejection region of the test, in this example both the test and the p-value are one tailed to the right, so you can calculate it as:

P(Z≥1.2) = 1 - P(Z<1.2)= 1 - 0.885= 0.115

The p- value is greater that the alfa level in point b. (5%) so the decision is also to not reject the null hypothesis.

I hope you have a SUPER day!

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irga5000 [103]
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In the triangle below, what is the length of the side opposite the 30 degree angle
victus00 [196]

Answer:

The correct answer is option A. √3

Step-by-step explanation:

From the figure we can see that aright angled triangle.

Points to remember

If a right angled triangle with angle 30°, 60° and 90° then the sides are in the ratio, 1 : √3 : 2

<u>To find the length of side opposite to 30°</u>

Let 'x' be the side opposite to the 30°, the we can write,

x : 3 : Hypotenuse = 1 : √3 : 2

Therefore x = 3/√3 = √3

The length of side opposite to the 30° is √3

The correct answer is option A. √3

6 0
1 year ago
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