Answer:
Step-by-step explanation:
According to the central limit theorem, if independent random samples of size n are repeatedly taken from any population and n is large, the distribution of the sample means will approach a normal distribution. The size of n should be greater than or equal to 30. Given n = 100 for both scenarios, we would apply the formula,
z = (x - µ)/(σ/√n)
a) x is a random variable representing the salaries of accounting graduates. We want to determine P( x > 52000)
From the information given
µ = 50402
σ = 6000
z = (52000 - 50402)/(6000/√100) = 2.66
Looking at the normal distribution table, the probability corresponding to the z score is 0.9961
b) x is a random variable representing the salaries of finance graduates. We want to determine P(x > 52000)
From the information given
µ = 49703
σ = 10000
z = (52000 - 49703)/(10000/√100) = 2.3
Looking at the normal distribution table, the probability corresponding to the z score is 0.9893
c) The probabilities of either jobs paying that amount is high and very close.
Answer:
see explaination
Step-by-step explanation:
Here the null hypothesis is that the PCB survives against the alternate that the PCB 'does not survive'. The test says that the PCB will survice if it is classified as 'good'; or, it will not survive if it is classifies as 'bad'.
a. The Type II error is the error committed when a PCB which cannot actually survive is classified as 'good'.
b. Therefore P(Type II error) = P(The PCB is classified as 'good' | PCB does not survives) = 0.03.
Place value is the value each digit has in its position: in order from higher to lower value, there is thousands, hundreds, tens, and ones.
When you divide by 10, you are moving (only once) every digit from its present place value to the right.
For example 6430 : 10= 643, you have moved every digit to the right, making the zero disappear (or better yet, separated by a hidden and in this case useless comma).
