An urban planner is researching commute times in the San Francisco Bay Area to find out if commute times have increased. In whic
2 answers:
Answer:
Step-by-step explanation:
The urban planner collects travel times from a random sample of 125 commuters in the San Francisco Bay Area. A traffic Study from last year claimed that the average commute time in the San Francisco Bay Area is 45 min. The urban planner will see if there is evidence the average commute time is greater than 45 minutes
( Here in this case, Null hypothesis will be Η :μ = 45
And the Alternate Hypoyhesis will be H, :μ> 45 )
C. The urban planner asks a random Sample of 100 commuters in the San Francisco Bay Area to record travel times on a Tuesday morning. One year later, the urban planner asks the same 100 commuters to record travel times on a tuesday morning . The urban planner will see the difference in commute time shows an increase.
Here in this case the null hypothesis will be, H₀ : = 0
And the Alternate Hypothesis will be H, : <0 The commute time after 1 year is more
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Answer:
0.0198 lbs per gallon
Step-by-step explanation:
amount of salt free water = 500 gallons
salt rate in = 1 gal/min
salt rate out = 1 gal/min
amount o salt in brine = 0.25 lb per gallon
Let the amount of salt in the tank be A(t) at any time t.
salt rate in = 0.25 x 1 = 0.25
salt rate out =
The differential equation is given by
where, A(0) = 0
So, the equation becomes
Here the integrating factor is
The solution of the above differential equation is given by
where, C is the integrating constant.
Put, A(0) = 0
C = - 500
As concentration is defined as
Concentration = Quantity / Volume
Now concentration at t = 10 min
Put, t = 10 min
C (10) = 0.0198 lbs per gallon
Thus, teh concentration after 10 min is 0.0198 lbs per gallon.
Answer:
10000*0.6065=6065
A. about 6,065
Step-by-step explanation:
Definitions and concepts
The Poisson process is useful when we want to analyze the probability of ocurrence of an event in a time specified. The probability distribution for a random variable X following the Poisson distribution is given by:
And the parameter represent the average ocurrence rate per unit of time.
For this distribution the expected value is the same parameter
,
,
Solution to the problem
We want "how many would have no blemishes" so first we need to find the probability that X=0, since X represent on this case the number of blemishes on each door. And if we use the mass function we got this:
And now since we have a total of 10000 doors painted we can find how many we would expect with no blemishes:
10000*0.6065=6065
A. about 6,065