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Please show the process to work the following problems.

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Solution Summary

Problems 1 - 3 refer to the chest size and weight data for the first 12 male bears found in Data Set 3: Bears. The steps are provided in detail.

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Problems 1 - 3 refer to the chest size and weight data for the first 12 male bears found in Data Set 3: Bears.

[1] Find the linear correlation coefficient for the paired male bear data (chest size, weight).

Using Excel, we get the following regression table.
SUMMARY OUTPUT

Regression Statistics
Multiple R 0.981981
R Square 0.964286
Adjusted R Square 0.960715
Standard Error 24.60492
Observations 12

ANOVA
df SS MS F Significance F
Regression 1 163461.6 163461.6 270.005 1.45E-08
Residual 10 6054.022 605.4022
Total 11 169515.7

Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept -219.956 28.71178 -7.66083 1.72E-05 -283.93 -155.982 -283.93 -155.982
X Variable 1 11.7714 0.716378 16.43183 1.45E-08 10.17521 13.36759 10.17521 13.36759

RESIDUAL OUTPUT

Observation Predicted ...

Solution provided by:
Changping Wang, MA
Education
  • BSc , Wuhan Univ. China
  • MA, Shandong Univ.
Recent Feedback
  • "Your solution, looks excellent. I recognize things from previous chapters. I have seen the standard deviation formula you used to get 5.154. I do understand the Central Limit Theorem needs the sample size (n) to be greater than 30, we have 100. I do understand the sample mean(s) of the population will follow a normal distribution, and that CLT states the sample mean of population is the population (mean), we have 143.74. But when and WHY do we use the standard deviation formula where you got 5.154. WHEN & Why use standard deviation of the sample mean. I don't understand, why don't we simply use the "100" I understand that standard deviation is the square root of variance. I do understand that the variance is the square of the differences of each sample data value minus the mean. But somehow, why not use 100, why use standard deviation of sample mean? Please help explain."
  • "excellent work"
  • "Thank you so much for all of your help!!! I will be posting another assignment. Please let me know (once posted), if the credits I'm offering is enough or you ! Thanks again!"
  • "Thank you"
  • "Thank you very much for your valuable time and assistance!"
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