How does a combination of transformations differ from a single transformation? How are they the same?

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A combination of transformations can create a wider array of figures as compared to a single transformation. However, both rely on the same general transformations such as reflection and scaling.

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Test the claim that the mean GPA of night students is larger than 3.1 at the .10 significance level. The null and alternative hy

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Answer:

H 0 : μ = 3.1 H 1 : μ > 3.1

And this test is defined a a right tailed test since the symbol for the alternative hypothesis H1 is >.

$t = \frac{3.13-3.1}{\frac{0.03}{\sqrt{75}}}= 8.66$

$df = n-1=75-1 =74$

The p value is:

$p_v = P(t_{74} >8.66) =3.65x10^{-13}$

The significance level is: 0.1 or 10%

And since the p value is very low compared to the significance level we have enough evidence to:

Reject the null hypothesis

Step-by-step explanation:

For this case we want to test the claim that mean GPA of night students is larger than 3.1 at the .10 significance level. The claim needs to be on the alternative hypothesis so then we have the following system of hypothesis:

H 0 : μ = 3.1 H 1 : μ > 3.1

And this test is defined a a right tailed test since the symbol for the alternative hypothesis H1 is >.

We have the following info given:

$\bar X = 3.13 , s =0.03 , n =75$