Answer:
The standard deviation of this probability distribution is 1.2.
Step-by-step explanation:
We have that:
P(X = 0) = 0.25
P(X = 1) = 0.3
P(X = 2) = 0.1
P(X = 3) = 0.35
Mean:
Each value multiplied by its probability. So
Variance:
Sum of the squares of the values subtracted from the mean, and multiplied by its probability.
Standard deviation:
Square root of the variance. So
The standard deviation of this probability distribution is 1.2.
Answer:
Options A, B and C are correct.
Step-by-step explanation:
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.
For a proportion p in a sample of size n, the sampling distribution of the sample proportion will be approximately normal with mean and standard deviation
From the Central Limit Theorem, we have that:
The larger the sample size, the closer to the normal distribution the distribution of sample means is.
No matter the sample size, the mean is the same.
The larger the sample size, the smaller the standard deviation.
The smaller the sample size, the larger the standard deviation.
So the correct options are:
A, B, C