Answer:
Step-by-step explanation:
We would set up the hypothesis test. This is a test of a single population mean since we are dealing with mean
For the null hypothesis,
µ = 6.8 psi
For the alternative hypothesis,
µ ≠ 6.8
This is a 2 tailed test
Since the population standard deviation is given, z score would be determined from the normal distribution table. The formula is
z = (x - µ)/(σ/√n)
Where
x = sample mean
µ = population mean
σ = population standard deviation
n = number of samples
From the information given,
µ = 6.8
x = 6.9
σ = 0.7
n = 140
z = (6.9 - 6.8)/(0.7/√140) = 0.17
Therefore, the value of the test statistic us 0.17
Answer:
(a) No solution
(b)
(c)
Step-by-step explanation:
Given
See attachment for graph
Solving (a): Solution to p(x) and f(x)
Curve p(x) and line f(x) do not intersect.
So, there is no solution to the pair of p(x) and f(x)
Solving (b): Two solutions to f(x)
This means that we select any two point on straight line f(x)
From the line of f(x), we have:
Solving (c): Solution to p(x) = g(x)
Here, we write out the point of intersection of p(x) and g(x)
From the graph, the point of intersection is:
Not quiet sure what your question is but if you’re asking me I’d say..
2.44948974278
Answer: 8
Explanation: To find the mean you need to add up all the numbers shown. After doing so you divide the sum by the amount of number shown. 64/8 = 8. Hope this helps!!!
Answer:
Only option d is not true
Step-by-step explanation:
Given are four statements about standard errors and we have to find which is not true.
A. The standard error measures, roughly, the average difference between the statistic and the population parameter.
-- True because population parameter is mean and the statistic are the items. Hence the differences average would be std error.
B. The standard error is the estimated standard deviation of the sampling distribution for the statistic.
-- True the sample statistic follows a distribution with standard error as std deviation
C. The standard error can never be a negative number. -- True because we consider only positive square root of variance as std error
D. The standard error increases as the sample size(s) increases
-- False. Std error is inversely proportional to square root of n. So when n decreases std error increases