Answer:
Correct option: (D).
Step-by-step explanation:
A null hypothesis is a hypothesis of no difference. It is symbolized by <em>H₀</em>.
A Type I error is the probability of rejection of the null hypothesis of a test when indeed the the null hypothesis is true.
The type I error is also known as the significance level of the test.
It is symbolized by P (type I error) = <em>α</em>.
In this case the researcher wants to determine whether the absorption rate into the body of a new generic drug (G) is the same as its brand-name counterpart (B) or not.
The hypothesis for this test can be defined as:
<em>H₀</em>: The absorption rate into the body of a new generic drug and its brand-name counterpart is same.
<em>Hₐ</em>: The absorption rate into the body of a new generic drug and its brand-name counterpart is not same.
The type I error will be committed when the null hypothesis is rejected when in fact it is true.
That is, a type I error will be made when the the results conclude that the absorption rate into the body for both the drugs is not same, when in fact the absorption rate is same for both.
Thus, the correct option is (<em>D</em>).
It should be b but i don’t have paper with me so i’m not sure
Answer:
2 2/5
Step-by-step explanation:
12 can go into 5 2 times with a left over of two, so it would be 2 which is 10 and a left over of two. The denominator stays the same. So it would be 2 2/5.
This question is incomplete, the complete question is;
Suppose John is a high school statistics teacher who believes that watching many hours of TV leads to lower test scores. Immediately after giving the most recent test, he surveyed each of the 24 students in his class and asked them how many hours of TV they watched that week. He then matched each student's test grade with his or her survey response. After compiling the data, he used hours of TV watched to predict each student's test score. He found the least-squares regression line to be y" = -1.5x + 85.
He also calculated that the value of r, the correlation, was -0.61.
what is the correct value of the coefficient of determination R² and give a correct interpretation of its meaning
Answer:
Interpretation of coefficient of determination R² = 0.3721
R² = 0.3721, meaning 37.21% of the total variation in test scores can be explained by the least square regression line
Step-by-step explanation:
Given the data in the question;
the least square regression line is;
y" = -1.5x + 85
the correlation coefficient r = -0.61
Now, the coefficient of determination R² is square of correlation coefficient r
R² = -61²
R² = 0.3721
Answer:
acute
Step-by-step explanation:
