When you have ratios and some unknowns you can create complex fractions from them.Bring them to the same denominator and solve for X.
Answer:
a: 28 < µ < 34
Step-by-step explanation:
We need the mean, var, and standard deviation for the data set. See first attached photo for calculations for these...
We get a mean of 222/7 = 31.7143
and a sample standard deviation of: 4.3079
We can now construct our confidence interval. See the second attached photo for the construction steps.
They want a 90% confidence interval. Our sample size is 7, so since n < 30, we will use a t-score. Look up the value under the 10% area in 2 tails column, and degree of freedom is 6 (degree of freedom is always 1 less than sample size for confidence intervals when n < 30)
The t-value is: 1.943
We rounded down to the nearest person in the interval because we don't want to over estimate. It said 28.55, so more than 28 but not quite 29, so if we use 29 as the lower limit, we could over estimate. It's better to use 28 and underestimate a little when considering customer flow.
Answer:
The answer is below
Step-by-step explanation:
We are asked to find the perimeter of triangle CDE. The perimeter of a shape is simply the sum of all its sides, hence:
Perimeter of tiangle CDE = |CD| + |DE| + |CE|
Given that C(4, -1), D(4, -5), E(2, -3).
The distance between two points
is given as:

Therefore the lengths of the triangle are:

Perimeter of CDE = 4 + 2.83 + 2.83 = 9.66 units
Answer:
0.38% probability that the sample contains exactly two defective parts.
Step-by-step explanation:
For each part, there are only two possible outcomes. Either it is defective, or it is not. The probabilities for each part being defective are independent from each other. So we use the binomial probability distribution to solve this problem.
Binomial probability distribution
The binomial probability is the probability of exactly x successes on n repeated trials, and X can only have two outcomes.

In which
is the number of different combinations of x objects from a set of n elements, given by the following formula.

And p is the probability of X happening.
In this problem we have that:

What is the probability that the sample contains exactly two defective parts?
This is 


0.38% probability that the sample contains exactly two defective parts.