Homework Help is Here – Start Your Trial Now!
Math
Statistics
just need part b. I need the p-value for part b, please.

just need part b. I need the p-value for part b, please.

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
See similar textbooks
icon
Related questions
Question

I just need part b. I need the p-value for part b, please.

**Exercise on Correlation and Linear Regression** **Overview:** This exercise explores the relationship between daily temperatures and the number of patients admitted for winter illnesses in a small clinic. A joint sample of ten data points (X, Y) is provided: - Data Points: (70,3), (77,1), (68,7), (67,7), (61,13), (83,0), (73,2), (66,8), (81,1), (90,0) Where: - X represents the average temperature of the day. - Y signifies the number of patients admitted with winter illnesses. **Tasks:** A. **Compute the Linear Correlation Coefficient (r):** - Find the correlation coefficient and explain its meaning in the context of the data. B. **Hypothesis Test for Slope (Significance Level 0.01):** - Test if the true slope of the regression line is negative at a 0.01 level of significance. C. **Prediction for Temperature 82:** - Estimate the average number of patients with winter illnesses if the temperature is 82. D. **Prediction for Temperature 60:** - Estimate the average number of patients with winter illnesses if the temperature is 60. E. **95% Confidence Interval for True Slope:** - Construct and interpret a 95% confidence interval for the true slope of the linear relationship. F. **Test the Predictive Power of Temperature:** - At 0.01 significance, determine if temperature is a good predictor for the average number of winter illness patients. G. **Report the Sum of Squared Residuals (SSE):** - Calculate the sum of squared residuals to assess the variation of data points around the fitted regression line. H. **Conclusion on Temperature and Illness Correlation:** - Based on items A-G, discuss if a lower temperature correlates with a higher number of winter illness patients. **Note:** This exercise utilizes linear regression analysis to understand and predict trends in healthcare data.
Transcribed Image Text:**Exercise on Correlation and Linear Regression** **Overview:** This exercise explores the relationship between daily temperatures and the number of patients admitted for winter illnesses in a small clinic. A joint sample of ten data points (X, Y) is provided: - Data Points: (70,3), (77,1), (68,7), (67,7), (61,13), (83,0), (73,2), (66,8), (81,1), (90,0) Where: - X represents the average temperature of the day. - Y signifies the number of patients admitted with winter illnesses. **Tasks:** A. **Compute the Linear Correlation Coefficient (r):** - Find the correlation coefficient and explain its meaning in the context of the data. B. **Hypothesis Test for Slope (Significance Level 0.01):** - Test if the true slope of the regression line is negative at a 0.01 level of significance. C. **Prediction for Temperature 82:** - Estimate the average number of patients with winter illnesses if the temperature is 82. D. **Prediction for Temperature 60:** - Estimate the average number of patients with winter illnesses if the temperature is 60. E. **95% Confidence Interval for True Slope:** - Construct and interpret a 95% confidence interval for the true slope of the linear relationship. F. **Test the Predictive Power of Temperature:** - At 0.01 significance, determine if temperature is a good predictor for the average number of winter illness patients. G. **Report the Sum of Squared Residuals (SSE):** - Calculate the sum of squared residuals to assess the variation of data points around the fitted regression line. H. **Conclusion on Temperature and Illness Correlation:** - Based on items A-G, discuss if a lower temperature correlates with a higher number of winter illness patients. **Note:** This exercise utilizes linear regression analysis to understand and predict trends in healthcare data.
Expert Solution
steps

Step by step

Solved in 2 steps

SEE SOLUTIONCheck out a sample Q&A here
Blurred answer
Recommended textbooks for you
MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
Statistics
ISBN:
9781119256830
Author:
Amos Gilat
Publisher:
John Wiley & Sons Inc
Probability and Statistics for Engineering and th…
Probability and Statistics for Engineering and th…
Statistics
ISBN:
9781305251809
Author:
Jay L. Devore
Publisher:
Cengage Learning
Statistics for The Behavioral Sciences (MindTap C…
Statistics for The Behavioral Sciences (MindTap C…
Statistics
ISBN:
9781305504912
Author:
Frederick J Gravetter, Larry B. Wallnau
Publisher:
Cengage Learning
Elementary Statistics: Picturing the World (7th E…
Elementary Statistics: Picturing the World (7th E…
Statistics
ISBN:
9780134683416
Author:
Ron Larson, Betsy Farber
Publisher:
PEARSON
The Basic Practice of Statistics
The Basic Practice of Statistics
Statistics
ISBN:
9781319042578
Author:
David S. Moore, William I. Notz, Michael A. Fligner
Publisher:
W. H. Freeman
Introduction to the Practice of Statistics
Introduction to the Practice of Statistics
Statistics
ISBN:
9781319013387
Author:
David S. Moore, George P. McCabe, Bruce A. Craig
Publisher:
W. H. Freeman
SEE MORE TEXTBOOKS