Answer:
b. lol.
Step-by-step explanation:
yeah I'm almost positive
Answer:
a) Null and alternative hypothesis:

b) A Type I error is made when a true null hypothesis is rejected. In this case, it would mean a conclusion that the proportion is significantly bigger than 10%, when in fact it is not.
c) The consequences would be that they would be more optimistic than they should about the result of the investment, expecting a proportion of students that is bigger than the true population proportion.
d) A Type II error is made when a false null hypothesis is failed to be rejected. This would mean that, although the proportion is significantly bigger than 10%, there is no enough evidence and it is concluded erroneously that the proportion is not significantly bigger than 10%
e) The consequences would be that the investment may not be made, even when the results would have been more positive than expected from the conclusion of the hypothesis test.
Step-by-step explanation:
a) The hypothesis should be carried to test if the proportion of students that would eat there at least once a week is significantly higher than 10%.
Then, the alternative or spectulative hypothesis will state this claim: that the population proportion is significantly bigger than 10%.
On the contrary, the null hypothesis will state that this proportion is not significantly higher than 10%.
This can be written as:

Answer:
-x² + x + 6
Step-by-step explanation:
Hey there!
To solve this, you have to use foil...
( 3 - x ) ( x + 2 )
3 ( x + 2 ) = 3x + 6
-x ( x + 2 ) = -x² - 2x
3x + 6 - 2x - x²
-x² + x + 6
Hope this helps :)
Answer and Step-by-step explanation:
Polynomial models are an excellent implementation for determining which input element reaction and their direction. These are also the most common models used for the scanning of designed experiments. It defines as:
Z = a0 + a1x1 + a2x2 + a11x12 + a22x22+ a12x1x2 + Є
It is a quadratic (second-order) polynomial model for two variables.
The single x terms are the main effect. The squared terms are quadratic effects. These are used to model curvature in the response surface. The product terms are used to model the interaction between explanatory variables where Є is an unobserved random error.
A polynomial term, quadratic or cubic, turns the linear regression model into a curve. Because x is squared or cubed, but the beta coefficient is a linear model.
In general, we can model the expected value of y as nth order polynomial, the general polynomial model is:
Y = B0 + B1x1 + B2x2 + B3x3 + … +
These models are all linear since the function is linear in terms of the new perimeter. Therefore least-squares analysis, polynomial regression can be addressed entirely using multiple regression
Answer:
Step-by-step explanation:
- cos = adjacent / hypotenuse
- cos (M) = MN / ML =
/ML =
/25 = 20/25 = 4/5
Correct choice is C