ILFifty-four wild bears were anesthetized, and then their weights and chest sizes were measured and listed in a data set. Results are shown in the accompanying Correlation Resultsdi-ufficient evidence to support the claim that there is a linear correlation between the weights of bears and their chest sizes? When measuringQuestion Vieweran ancouzon Jear, is it easier to measure chest size than weight? If so, does it appear that a measured chest size can be used to predict the weight? Use asignificance level of α = 0.05.Correlation coeff, r: 0.973641Critical r:P-value (two tailed): 0.000Determine the null and alternative hypotheses.Ho: PH₁P(Type integers or decimals. Do not round.)+0.2680855

The objective of this question is to determine if there is a significant linear correlation between the weights of bears and their chest sizes, and if chest size can be used to predict weight.First, we need to understand the concept of correlation. Correlation is a statistical measure that describes the size and direction of a relationship between two or more variables. A correlation coefficient, denoted as r, measures the strength and direction of a linear relationship between two variables. The value of r is always between -1 and 1. A positive correlation indicates that as one variable increases, the other also increases. A negative correlation indicates that as one variable increases, the other decreases.In this case, the correlation coefficient r is given as 0.973641. This is a very high positive correlation, indicating a strong linear relationship between the weights of bears and their chest sizes.Next, we need to understand the concept of hypothesis testing. The null hypothesis (Ho) is a statement of no effect or no difference. The alternative hypothesis (H₁) is a statement that something is happening, a new phenomenon is taking place. In this case, the null hypothesis would be that there is no correlation between the weights of bears and their chest sizes (ρ = 0), and the alternative hypothesis would be that there is a correlation (ρ ≠ 0).The p-value is a probability that measures the evidence against the null hypothesis. A small p-value (typically ≤ 0.05) indicates strong evidence against the null hypothesis, so you reject the null hypothesis. A large p-value (> 0.05) indicates weak evidence against the null hypothesis, so you fail to reject the null hypothesis.In this case, the p-value is given as 0.000, which is less than the significance level of 0.05. This means that we reject the null hypothesis and accept the alternative hypothesis. There is sufficient evidence to support the claim that there is a linear correlation between the weights of bears and their chest sizes.As for the question of whether it is easier to measure chest size than weight, this would depend on the specific circumstances and equipment available. However, given the strong correlation between chest size and weight, it does appear that a measured chest size could be used to predict the weight of a bear.There is a strong positive correlation between the weights of bears and their chest sizes, as indicated by the high correlation coefficient of 0.973641 and the low p-value of 0.000. Therefore, it is reasonable to use chest size to predict the weight of a bear.