Police sometimes measure shoe prints at crime scenes so that they can learn something about criminals. Listed below are shoe print lengths, foot lengths, and heights of males. Construct a scatterplot, find the value of the linear correlation coefficient $r$, and find the P-value of $r$. Determine whether there is sufficient evidence to support a claim of linear correlation between the two variables. Based on these results, does it appear that police can use a shoe print length to estimate the height of a male? Use a significance level of $\alpha=0.01$.
\begin{tabular}{l|ccccc}
Shoe Print $(\mathrm{cm})$ & 29.8 & 29.8 & 32.6 & 33.1 & 27.0 \\
\hline Foot Length $(\mathrm{cm})$ & 26.3 & 26.3 & 27.0 & 25.9 & 24.3 \\
\hline Height $(\mathrm{cm})$ & 178.6 & 181.4 & 184.3 & 171.3 & 178.4
\end{tabular}

The linear correlation coefficient is $r=\square$.
(Round to three decimal places as needed.)
Determine the null and alternative hypotheses.
\[
\begin{array}{ll|l|}
\mathrm{H}_{0}: \rho & \mathbf{\nabla} \square \\
\mathrm{H}_{1}: \rho & \boldsymbol{\nabla} \\
\hline
\end{array}
\]
(Type integers or decimals. Do not round.)
The test statistic is $\mathrm{t}=\square$.
(Round to two decimal olaces as needed.)
The P-value is $\square$.
(Round to three decimal places as needed.)
Because the P.value of the linear correlation coefficient is the significance level, there sufficient evidence to support the claim that there is a linear correlation between shoe print lengths and heights of males.

Based on these results, does it appear that police can use a shoe print length to estimate the height of a male?
A. No, because shoe print tength and height appear to be correlated.
B. Yes, because shoe print length and height appear to be correlated.
C. No, because shoe print length and height do not appear to be correlated.
D. Yes, because shoe print length and height do not appear to be correlated.

Step 1 ：Construct a scatterplot of the data to visually inspect the relationship between shoe print length and height.

Step 2 ：Calculate the linear correlation coefficient $r$ to quantify the strength and direction of the linear relationship between the two variables.

Step 3 ：Calculate the P-value of $r$ to test the null hypothesis that there is no linear correlation between the two variables against the alternative hypothesis that there is a linear correlation.

Step 4 ：If the P-value is less than the significance level of $\alpha=0.01$, reject the null hypothesis and conclude that there is sufficient evidence to support a claim of linear correlation between the two variables.

Step 5 ：Based on these results, determine whether it appears that police can use a shoe print length to estimate the height of a male.

Step 6 ：The scatterplot shows a positive linear relationship between shoe print length and height, which suggests that as shoe print length increases, height also tends to increase.

Step 7 ：The linear correlation coefficient $r$ is a measure of the strength and direction of this linear relationship. A positive $r$ value close to 1 indicates a strong positive linear relationship, while a negative $r$ value close to -1 indicates a strong negative linear relationship. A $r$ value close to 0 indicates no linear relationship.

Step 8 ：The P-value is a measure of the probability that we would observe the calculated $r$ value (or a more extreme $r$ value) if the null hypothesis were true.

Step 9 ：If the P-value is less than the significance level of $\alpha=0.01$, reject the null hypothesis and conclude that there is sufficient evidence to support a claim of linear correlation between the two variables.

Step 10 ：The linear correlation coefficient $r$ is 0.858, which indicates a strong positive linear relationship between shoe print length and height.

Step 11 ：The P-value is 0.073, which is greater than the significance level of $\alpha=0.01$. Therefore, we do not reject the null hypothesis and conclude that there is not sufficient evidence to support a claim of linear correlation between the two variables at the 0.01 level of significance.

Step 12 ：The null hypothesis is that there is no linear correlation between shoe print length and height, which is represented as $\rho = 0$. The alternative hypothesis is that there is a linear correlation between shoe print length and height, which is represented as $\rho \neq 0$.

Step 13 ：The test statistic is the value of the linear correlation coefficient $r$, which is 0.858.

Step 14 ：Based on these results, it does not appear that police can use a shoe print length to estimate the height of a male, because shoe print length and height do not appear to be correlated at the 0.01 level of significance.

Step 15 ：Final Answer: The linear correlation coefficient is $r=\boxed{0.858}$. The null and alternative hypotheses are $\mathrm{H}_{0}: \rho = 0$ and $\mathrm{H}_{1}: \rho \neq 0$. The test statistic is $\mathrm{t}=\boxed{0.858}$. The P-value is $\boxed{0.073}$. Because the P-value of the linear correlation coefficient is greater than the significance level, there is not sufficient evidence to support the claim that there is a linear correlation between shoe print lengths and heights of males. Based on these results, the answer is C. No, because shoe print length and height do not appear to be correlated.