Answer:
Step-by-step explanation:
Hello!
If X₁, X₂, ..., Xₙ be the n random variables that constitute a sample, then any function of type θ = î (X₁, X₂, ..., Xₙ) that depends solely on the n variables and does not contain any parameters known, it is called the estimator of the parameter.
When the function i (.) It is applied to the set of the n numerical values of the respective random variables, a numerical value is generated, called parameter estimate θ.
This follows the concepts:
1) The function i (.) It is a function of random variables, so it is also a random variable, that is to say, that every estimator is a random variable.
2) From the above, it follows that Î has its probability distribution and therefore mathematical hope, E (î), and variance, V (î).
In this example, the variable of interest is X: response of a U.S. television viewer surveyed over the Statistics show.
The mean response was μ=6.5 and the standard deviation σ=1.5
If many samples of U.S. television viewers with size n=9 were taken and the sample mean of the user's ratings was calculated. The resulting sample mean will be a random variable and its distribution will have the same shape as the population it was originally calculated from.
The correct option is:
1. The shape of the distribution of mean ratings depends on the shape of the ratings of the general U.S. television-viewing population.
Since is impossible to study whole populations, due to economic, materials, and time reasons, especially when the populations are very big. Then since you cannot study the population mean directly, it is best to do it through the sample mean. For this, you need to know its theoretical distribution.
Correct option:
2. To conclude the population mean using a sample mean, you need to know the theoretical distribution of the possible values the sample mean ratings could have.
I hope you have a SUPER day!
