Answer:
The number of ways is 13860 ways
Step-by-step explanation:
Given
Senior Members = 10
Junior Members = 12
Required
Number of ways of selecting 6 students students
The question lay emphasis on the keyword selection; this implies combination
From the question, we understand that
4 students are to be selected from senior members while 2 from junior members;
The number of ways is calculated as thus;
Ways = Ways of Selecting Senior Members * Ways of Selecting Junior Members
Hence, the number of ways is 13860 ways
Answer:
v=3
Step-by-step explanation:
15v-9v=18
6v = 18
6 6
v = 3
Answer:
slope - m= −0.2
F
Step-by-step explanation:
Answer:
0.6 or 3/5
Step-by-step explanation:
If cos(t) = 4/5, then, taking the arccos of both sides, t ≈ 36.86989765. Taking the sin of that gives you 0.6, or 3/5 in fraction form.
Answer:
1. Due to the lower standard deviation, it is more likely to obtain a sample of females within $10,000 of the population mean
2. 15.87% probability that a simple random sample of 100 male graduates will provide a sample mean more than $4,000 below the population mean
Step-by-step explanation:
To solve this question, we need to understand the normal probability distribution and the central limit theorem.
Normal probability distribution
When the distribution is normal, we use the z-score formula.
In a set with mean and standard deviation , the zscore of a measure X is given by:
The Z-score measures how many standard deviations the measure is from the mean. After finding the Z-score, we look at the z-score table and find the p-value associated with this z-score. This p-value is the probability that the value of the measure is smaller than X, that is, the percentile of X. Subtracting 1 by the pvalue, we get the probability that the value of the measure is greater than X.
Central Limit Theorem
The Central Limit Theorem estabilishes that, for a normally distributed random variable X, with mean and standard deviation , the sampling distribution of the sample means with size n can be approximated to a normal distribution with mean and standard deviation .
For a skewed variable, the Central Limit Theorem can also be applied, as long as n is at least 30.
1. In which of the preceding two cases, part a or part b, do we have a higher probability of obtaining a smaple estimate within $10,000 of the population mean? why?
The lower the standard deviation, the less dispersed the values are, meaning it is more likely to find values within a certain threshold of the mean.
So
Due to the lower standard deviation, it is more likely to obtain a sample of females within $10,000 of the population mean.
2. What is the probability that a simple random sample of 100 male graduates will provide a sample mean more than $4,000 below the population mean?
We have that:
This probability is the pvalue of Z when X = 168000 - 4000 = 164000. So
By the Central Limit Theorem
has a pvalue of 0.1587
15.87% probability that a simple random sample of 100 male graduates will provide a sample mean more than $4,000 below the population mean
1. We have a higher probability of obtaining a sample estimate within $10,000 of the population mean when the standard deviation is smaller. In this case, the standard deviation for female graduates is smaller, so the probability is higher. 2. The probability that a simple random sample of 100 male graduates will provide a sample mean more than $4,000 below the population mean can be calculated using the z-score formula and the z-table.
1. In the case where the standard deviation is smaller, we have a higher probability of obtaining a sample estimate within $10,000 of the population mean. This is because a smaller standard deviation indicates less variability in the data, making it more likely for the sample mean to be closer to the population mean. In this case, the standard deviation for female graduates is smaller, so the probability is higher.
2. To calculate the probability, we need to calculate the z-score and then use the z-table. The z-score formula is z = (x - μ) / (σ / sqrt(n)), where x is the sample mean, μ is the population mean, σ is the population standard deviation, and n is the sample size. Plugging in the given values, we find the z-score and use the z-table to find the probability.
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