Answer:
Step-by-step explanation:
Will give brainliest so no links^^
2 answers:
You might be interested in
Answer:
Step-by-step explanation:
Assuming this info from R
hist(gifted$count)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 21.00 28.00 31.00 30.69 34.25 39.00
## Sd
## [1] 4.314887
Data given and notation
represent the mean
represent the sample standard deviation
sample size
represent the value that we want to test
represent the significance level for the hypothesis test.
t would represent the statistic (variable of interest)
represent the p value for the test (variable of interest)
State the null and alternative hypotheses.
We need to conduct a hypothesis in order to check if the mean is different than 32, the system of hypothesis would be:
Null hypothesis:
Alternative hypothesis:
If we analyze the size for the sample is > 30 but we don't know the population deviation so is better apply a t test to compare the actual mean to the reference value, and the statistic is given by:
(1)
t-test: "Is used to compare group means. Is one of the most common tests and is used to determine if the mean is (higher, less or not equal) to an specified value".
Calculate the statistic
We can replace in formula (1) the info given like this:
P-value
The first step is calculate the degrees of freedom, on this case:
Since is a two sided test the p value would be:
The objective function is simply a function that is meant to be maximized. Because this function is multivariable, we know that with the applied constraints, the value that maximizes this function must be on the boundary of the domain described by these constraints. If you view the attached image, the grey section highlighted section is the area on the domain of the function which meets all defined constraints. (It is all of the inequalities plotted over one another). Your job would thus be to determine which value on the boundary maximizes the value of the objective function. In this case, since any contribution from y reduces the value of the objective function, you will want to make this value as low as possible, and make x as high as possible. Within the boundaries of the constraints, this thus maximizes the function at x = 5, y = 0.
