Answer:
A. unbiased estimator.
Step-by-step explanation:
In Statistics, an estimator is a statistical value which is used to estimate a parameter. Parameters are the determinants of the probability distribution. Therefore, to determine a normal distribution we would use the parameters, mean and variance of the population.
A function of random variables used to estimate a parameter of a distribution is an unbiased estimator.
An unbiased estimator is one in which the difference between the estimator and the population parameter grows smaller as the sample size grows larger. This simply means that, an unbiased estimator captures the true population value of the parameter on average, this is because the mean of its sampling distribution is the truth.
Also, we know that the bias of an estimator (b) that estimates a parameter (p) is given by; 
Hence, an unbiased estimator is an estimator that has an expected value that is equal to the parameter i.e the value of its bias is equal to zero (0).
<em>Generally, in statistical analysis, sample mean is an unbiased estimator of the population mean while the sample variance is an unbiased estimator of the population variance.</em>
Let me see the whole thing, theres no enough info
Answer:
Step-by-step explanation:
*Based on the survey which shows 28% of teens regularly watch TV when doing Home work, mathematically, = 0.28 of students watch TV when doing home work
* Suppose there are 379 seventh graders who complete their homework on a particular night.m mathematically, using the data of the survey that 28% of teens (seventh grader) watch TV while doing homework.
Using the survey analysis to compute the number of teens on this night that would be doing homework and also watching TV will be;
0.28* 379 =106.12.
The above computation explains that a possible number of 106 teens would be watching TV and also doing there home work, i.e in more sense 28% of this 379 teens doing homework and watching TV is 106
Answer:
Step-by-step explanation:
Given that in an investigation of pregnancy-induced hypertension, one group of women with this disorder was treated with low-dose aspirin, and a second group was given a placebo. A sample consisting of 50 women who received aspirin has mean arterial blood pressure 120mmHg and standard deviation 10mmHg; a sample of 42 women who were given the placebo has mean blood pressure 115mmHg and standard deviation 12mmHg.
Population variances are equal
(two tailed test at 5% significance level)
Here x denotes group I and Y group II
N Mean StDev SE Mean
Sample 1 50 120 10 1.4142
Sample 2 42 115 12 1.8516
Pooled std deviation = 10.9565
df=80
Mean difference= 5
Test statistic t = mean diff/std error = 
=2.1803
p value = 0.0318
since p <0.05 we reject H0
b) We find p >0.01 hence at 1% significance level we accept H0
This implies that 99% confidence interval contains mean difference =0
Answer:
a) 0.64
b) -1.13
Step-by-step explanation:
We are given the following information in the question:
Women:
Mean, μ = 64.2 inches
Standard Deviation, σ = 2.8 inches
We are given that the distribution of heights of women is a bell shaped distribution that is a normal distribution.
Men:
Mean, μ = 69.4 inches
Standard Deviation, σ = 3.0 inches
We are given that the distribution of heights of men is a bell shaped distribution that is a normal distribution.
Formula:
5.5 feet = 66 inches
a) z‑score for a woman 5.5 feet tall
b) z‑score for a man 5.5 feet tall
