Answer:
The 95% confidence interval estimate of the true population proportion of U.S. employers that were likely to require higher employee contributions for health care coverage is 0.52 +/- 0.0370
= (0.4830, 0.5570)
The margin of error M.E = 0.0370
Step-by-step explanation:
Confidence interval can be defined as a range of values so defined that there is a specified probability that the value of a parameter lies within it.
The confidence interval of a statistical data can be written as.
p+/-z√(p(1-p)/n)
p+/-M.E
Given that;
M.E = margin of error
Proportion p = 52% = 0.52
Number of samples n = 700
Confidence interval = 95%
z value (at 95% confidence) = 1.96
Substituting the values we have;
0.52 +/- 1.96√(0.52(1-0.52)/700)
0.52 +/- 1.96(0.0189)
0.52 +/- 0.0370
( 0.4830, 0.5570)
The 95% confidence interval estimate of the true population proportion of U.S. employers that were likely to require higher employee contributions for health care coverage is 0.52 +/- 0.0370
= (0.4830, 0.5570)
The margin of error M.E = 0.0370
Answer: 141.3/3=47.1 so then 47.1x3.14=147.89400
Step-by-step explanation:
Answer:
I’m doing that exact question right now on a test so I just guessed
Step-by-step explanation:
Answer:
Percentage rise in patients from 2004 to 2014 = 7.59%
Explanation:
The number of patients in 2004 = 39080
The number of patients in 2014 = 42045
Increase in the number of patients = (The number of patients in 2014) - (The number of patients in 2004)
Rise in the number of patients = 42045 - 39080
Rise in the number of patients = 2965

Percentage rise in patients from 2004 to 2014 = 7.59%
Answer:
0
Step-by-step explanation:
The remainder theorem says when you are dividing P(x) by (x-c), then the remainder is P(c).
So we need to evaluate x^2+2x-15 for x=3.
3^2+2(3)-15
9+6-15
15-15
0
So the remainder is 0.