Answer:
The value of x is hours.
Step-by-step explanation:
Machine A = 5 hours
Machine B = x hours
Machine A and B = 2 hours
Using the formula:
where:
T is the time spend by both machine
A is the time spend by machine A
B is the time spend by machine B
Let multiply the entire problem by the common denominator (5B)
2x + 10 = 5x
Collect the like terms
10 = 5x - 2x
10 = 3x
3x = 10
Divide both side by the coefficient of x (3)
hours.
Therefore, Machine B will fill the same lot in hours.
Answer: The comparison is mentioned below.
Step-by-step explanation:
A piece of climbing equipment at a playground is 6 feet high and extends 4 feet horizontally.
Therefore its slope = length of the equipment vertically / length of equipment horizontally
= 6/4 = 3/2 = 1.5
And, A piece of climbing equipment at a gym is 10 feet high and extends 6 feet horizontally.
Therefore slope, = 10/6 = 5/3=1.67 (approx)
Since, <
Thus the slope of equipment first is less than slope of second equipment.
Answer:
A.the type 1 error probability is
B. β = 0.0122
C. β = 0.0000
Step-by-step explanation:
Given that:
Mean = 100
standard deviation = 2
sample size = 9
The null and the alternative hypothesis can be computed as follows:
A. If the acceptance region is defined as , find the type I error probability
.
Assuming the critical region lies within or
, for a type 1 error to take place, then the sample average x will be within the critical region when the true mean heat evolved is
∴
when
From the standard normal distribution tables
Thus, the type 1 error probability is
B. Find beta for the case where the true mean heat evolved is 103.
The probability of type II error is represented by β. Type II error implies that we fail to reject null hypothesis
Thus;
β = P( type II error) - P( fail to reject )
∴
Given that
From standard normal distribution table
β = 0.0122 - 0.0000
β = 0.0122
C. Find beta for the case where the true mean heat evolved is 105. This value of beta is smaller than the one found in part (b) above. Why?
Given that
From standard normal distribution table
β = 0.0000 - 0.0000
β = 0.0000
The reason why the value of beta is smaller here is that since the difference between the value for the true mean and the hypothesized value increases, the probability of type II error decreases.
