### Age Distribution of Best Actor and Best Actress Award WinnersThe table below displays the ages of winners for the "Best Actress" and "Best Actor" categories. This data offers insight into the age demographics commonly seen among award recipients.| Best Actress | 29 | 29 | 30 | 58 | 33 | 32 | 47 | 30 | 63 | 22 | 45 | 54 ||--------------|----|----|----|----|----|----|----|----|----|----|----|----|| Best Actor | 43 | 37 | 37 | 45 | 52 | 46 | 57 | 52 | 39 | 55 | 43 | 32 |**Analysis:**- The ages of Best Actress winners range from 22 to 63, showing a significant variation which indicates diverse age representation among winners.- The ages of Best Actor winners range from 32 to 57, suggesting a slightly older age bracket compared to the actresses. This comparison can help explore industry trends and perceptions regarding age. The text describes an educational task related to correlational statistics using fictional data about acting award winners. Here's a transcription and explanation suitable for an educational website:---**Correlation Exercise with Acting Award Winners' Ages**The accompanying table lists the ages of acting award winners matched by the years in which the awards were won. The task involves constructing a scatterplot, calculating the linear correlation coefficient (r), and determining the P-value for r. Finally, you must assess whether there is sufficient evidence to support a claim of linear correlation between the two variables using a significance level of α = 0.01.### Instructions:1. **Scatterplot Construction** - Choose the correct scatterplot from four options: - **Option A**: Indicates a moderate positive correlation. - **Option B**: Shows no apparent correlation. - **Option C**: Illustrates a negative correlation. - **Option D**: Displays a moderate positive correlation with less variability than Option A.2. **Linear Correlation Coefficient (r)** - Compute the value of r to three decimal places. - **Enter the Value**: A box is provided for input.3. **Determine Hypotheses** - Null Hypothesis (H₀): There is no correlation (ρ = 0). - Alternative Hypothesis (H₁): There is a correlation (ρ ≠ 0).4. **Test Statistic and P-value** - Calculate the test statistic, t, and round it to two decimal places, providing an answer box for input. - Compute the P-value, rounding to three decimal places, with another answer box for input.5. **Conclusion Based on P-value** - Compare the P-value against the significance level to decide: - If there is sufficient evidence to claim a linear correlation. - Multiple-choice for reasoning: - **A**: No correlation due to typical age differences in separate movies. - **B**: Correlation likely due to similar age range. - **C**: Expectation of correlation from concurrent movie releases. - **D**: Expectation of no correlation due to disparate ages.### Key Considerations:- Understand the implications of r values close to -1, 0, or 1.- P-value interpretation relative to α = 0.01.- Contextual reasoning behind actor and actress ages in movies.This task helps in understanding statistical hypothesis testing

(A)Excel function: =CORREL(X,Y)= r = -0.312 _______________________________________________ H0:ρ = 0H1:ρ ≠ 0 _______________________________________________ Test - stats (t) = rn-21-r2= -0.31212-21-(-0.312)2=-1.04 _____________________________________________ P-value = T.DIST.RT(1.04,10 )-------------Excel function = 0.761 ============================= Greater than, is not __________________________________________________ (A)