A manufacturer of automobile transmissions uses two different processes. Management ordered a study of the production costs to s
ee if there is a difference between the two processes. A summary of the findings is shown next.
What is the critical value of F at the 1% level of significance?
a. 9.46
b. 8.29
c. 8.18
d. 4.61
What is the critical value of F at the 1% level of significance?
a. 9.46
b. 8.29
c. 8.18
d. 4.61
1 answer:
Answer:
b. 8.29
Step-by-step explanation:
Hello!
The objective is to study both processes to see if there is any difference between the costs of manufacturing.
An ANOVA was conducted for the variable:
Y: cost of manufacturing automobile transmissions
Factor: Process
Treatments: 1 and 2
The statistic for this test is
Where
k= number of treatments
n= total number of observations
Degrees of freedom of the treatments: k-1= 2-1= 1
Degrees of freedom of errors: n-k= 20-2= 18
The critical region for this analysis is <u>always</u> one tailed to the right.
I hope this helps!
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= > x² + 7x + 12 = 12
= > x² + ( 4 + 3 )x + 12 = 12
= > x² + 4x + 3x + 12 = 12
= > x( x + 4 ) + 3( x + 4 ) = 12
= > ( x + 4 ) ( x + 3 ) = 12
Percy did correct till this step. But by doing like this, Percy can't get the values of the variable x.
Percy should follow the following steps :
= > x² + 7x + 12 = 12
Add -12 on both sides,
= > x² + 7x + 12 - 12 = 12 - 12
= > x² + 7x = 0
= > x( x + 7 ) = 0
= > ( x = 0 ) or ( x + 7 = 0 )
= > ( x = 0 ) or ( x = - 7 )
Hence, required value(s) of x is 0 or -7
= > x² + ( 4 + 3 )x + 12 = 12
= > x² + 4x + 3x + 12 = 12
= > x( x + 4 ) + 3( x + 4 ) = 12
= > ( x + 4 ) ( x + 3 ) = 12
Percy did correct till this step. But by doing like this, Percy can't get the values of the variable x.
Percy should follow the following steps :
= > x² + 7x + 12 = 12
Add -12 on both sides,
= > x² + 7x + 12 - 12 = 12 - 12
= > x² + 7x = 0
= > x( x + 7 ) = 0
= > ( x = 0 ) or ( x + 7 = 0 )
= > ( x = 0 ) or ( x = - 7 )
Hence, required value(s) of x is 0 or -7
Given:
μ = 25 mpg, the population mean
σ = 2 mpg, the population standard deviation
If we select n samples for evaluation, we should calculate z-scores that are based on the standard error of the mean.
That is,
The random variable is x = 24 mpg.
Part (i): n = 1
σ/√n = 2
z = (24 -25)/2 = -0.5
From standard tables,
P(x < 24) = 0.3085
Part (ii): n = 4
σ/√n = 1
z = (24 -25)/1 = -1
P(x < 24) = 0.1587
Part (iii): n=16
σ/√n = 0.5
z = (24 - 25)/0.5 = -2
P(x < 24) = 0.0228
Explanation:
The larger the sample size, the smaller the standard deviation.
Therefore when n increases, we are getting a result which is closer to that of the true mean.
μ = 25 mpg, the population mean
σ = 2 mpg, the population standard deviation
If we select n samples for evaluation, we should calculate z-scores that are based on the standard error of the mean.
That is,
The random variable is x = 24 mpg.
Part (i): n = 1
σ/√n = 2
z = (24 -25)/2 = -0.5
From standard tables,
P(x < 24) = 0.3085
Part (ii): n = 4
σ/√n = 1
z = (24 -25)/1 = -1
P(x < 24) = 0.1587
Part (iii): n=16
σ/√n = 0.5
z = (24 - 25)/0.5 = -2
P(x < 24) = 0.0228
Explanation:
The larger the sample size, the smaller the standard deviation.
Therefore when n increases, we are getting a result which is closer to that of the true mean.