91影视

The sample sizes and means for the two samples were provided.

\(\begin{aligned}{l}n1 &= 1,400\\n2 &= 1,400\end{aligned}\)

\(\begin{aligned}{l}\bar x1 &= 69,848\\\bar x2 &= 52,012\end{aligned}\)

The process of analyzing the outcome and drawing conclusions from data with random variation is known as statistical inference. Additionally known as inferential statistics. Applications of statistical inference include hypothesis testing and confidence intervals. Statistical inference is a technique for determining a population's characteristics based on a random sample.

Analyzing the correlation between the dependent and independent variables is helpful. Estimating uncertainty or sample-to-sample variation is the goal of statistical inference. It enables us to offer a likely range of values for an item's actual values in the population.

The following elements are included in statistical inference:

• Actual relationship between X and Y
• Independence
• Normal
• Equal variance
• Random

\(\begin{aligned}{l}n1 &= 1,400\\n2 &= 1,400\end{aligned}\)

\(\begin{aligned}{l}\bar x1 &= 69,848\\\bar x2 &= 52,012\end{aligned}\)

\(\begin{aligned}{l}{\sigma ^2}1 &= Variance\,of\,population1\\{\sigma ^2}2 &= \,Variance\,of\,population2\end{aligned}\)

The test hypothesis is as follows.

Denominator\({S^2}2\) and numerator\({S^2}1\) both

\(\begin{aligned}{l}df &= n1 - 1\\df = 1,400 - 1\\df &= 1,399\end{aligned}\)

\(v1 = n1 - 1\)

\(df = n2 - 1\)

\(v2 = n2 - 1\)

\(\begin{aligned}{l}df &= 1,400 - 1\\df &= 1,399\end{aligned}\)

\(\begin{aligned}{l}S{1^2} &= \frac{{\sum {{\left( {xi - \bar x} \right)}^2}}}{{n - 1}}\\S{1^2} = \frac{{4,685,128,704}}{{1,399}}\\S{1^2} &= 3,348,912.5832737\end{aligned}\)

\(\begin{aligned}{l}S{2^2} &= \frac{{\sum {{\left( {yi - \bar y} \right)}^2}}}{{n - 1}}\\S{2^2} = \frac{{2,561,574,544}}{{1,399}}\\S{2^2} &= 1,831,003.9628305\end{aligned}\)

The test statistics will therefore be.

\(\begin{aligned}{l}F &= {\textstyle{{Larger\,\,sample\,\,variance} \over {Smaller\,\,sample\,\,variance}}}\\F &= \frac{{{S^2}1}}{{{S^2}2}}\\F &= \frac{{3,348,912.5832737}}{{1,831,003.9628305}}\\F &= 1.8290034599907\end{aligned}\)

Therefore, due to distinct sample variances and dependent variables, it is impossible to draw any conclusions from the data in the table.