Answer:
Step-by-step explanation:
According to the problem, calculation of given data are as follows,
Volume of cylindrical vase =
As, Cone has same height and radius as cylinder, than we can calculate volume of cone by using following formula,
Volume of cone = × Volume of cylindrical vase
By putting the value, we get
Volume of cone = × 63
Volume of cone =
Answer:
b) The width of the confidence interval becomes narrower when the sample mean increases.
Step-by-step explanation:
The confidence interval can be calculated as:
a) The width of the confidence interval becomes wider as the confidence level increases.
The above statement is true as the confidence level increases the width increases as the absolute value of test statistic increases.
b) The width of the confidence interval becomes narrower when the sample mean increases.
The above statement is false. As the sample mean increases the width of the confidence interval increases.
c) The width of the confidence interval becomes narrower when the sample size n increases.
The above statement is true as the sample size increases the standard error decreases and the confidence interval become narrower.
Answer:
Step-by-step explanation:
Given that a dairy scientist is testing a new feed additive. She chooses 13 cows at random from a large population of cows. She randomly assigns nold = 8 to get the old diet, and nnew = 5 to get the new diet including the additive.
From the data given we get the following
N Mean StDev SE Mean
Sample 1 8 43 5.1824 1.832
Sample 2 5 73 21.0832 9.429
df = 11
Std dev for difference = 13.3689
a) Yes the two are independent. The two sets of cows randomly chosen are definitely independent. Paired means equal number should be there and homogeneous conditions should be maintained.
b) Enclosed
c) Comparison of two means is the test recommended here. Because independent samples are used.\
d) Test statistic= -3.1233
(because of unequal variances we use that method)
95% confidence interval = ( -56.6676 , -3.3324 )
p value <0.05 our alpha
So reject null hypothesis.
The two means are statistically significantly different.
