MATLAB: An Introduction with Applications
6th Edition
ISBN: 9781119256830
Author: Amos Gilat
Publisher: John Wiley & Sons Inc
expand_more
expand_more
format_list_bulleted
Related questions
Question
I need
![**Correlation Analysis of Bear Weight and Chest Size**
In a study involving fifty-four wild bears, both weight and chest size measurements were recorded after anesthetizing the animals. The aim is to determine if there's a linear correlation between a bear's weight and chest size, assessing whether chest size can potentially predict weight, especially when capturing the weight of an anesthetized bear might be challenging. The significance level for the analysis is set at \(\alpha = 0.05\).
**Determine the Null and Alternative Hypotheses:**
- Null Hypothesis, \( H_0: \rho = 0 \)
- Alternative Hypothesis, \( H_1: \rho \neq 0 \)
(*Note: Use integers or decimals without rounding when entering the hypotheses.*)
**Identify the Correlation Coefficient, \( r \):**
- \( r = \) [Enter your value here] (Round to three decimal places as needed.)
**Correlation Results:**
- **Correlation Coefficient \( r \):** \( 0.967366 \)
- **Critical \( r \):** \( \pm 0.2680885 \)
- **P-value (two-tailed):** \( 0.000 \)
The data suggests a significant correlation between the bears' chest sizes and weights, as indicated by the high correlation coefficient and a p-value less than 0.05.](https://content.bartleby.com/qna-images/question/77cc80fa-a1ab-48f0-b2a0-996110f4bcad/b1ced14f-8d36-4f9f-b5a9-56f20400809f/ewxm1cg_processed.jpeg)
Transcribed Image Text:**Correlation Analysis of Bear Weight and Chest Size**
In a study involving fifty-four wild bears, both weight and chest size measurements were recorded after anesthetizing the animals. The aim is to determine if there's a linear correlation between a bear's weight and chest size, assessing whether chest size can potentially predict weight, especially when capturing the weight of an anesthetized bear might be challenging. The significance level for the analysis is set at \(\alpha = 0.05\).
**Determine the Null and Alternative Hypotheses:**
- Null Hypothesis, \( H_0: \rho = 0 \)
- Alternative Hypothesis, \( H_1: \rho \neq 0 \)
(*Note: Use integers or decimals without rounding when entering the hypotheses.*)
**Identify the Correlation Coefficient, \( r \):**
- \( r = \) [Enter your value here] (Round to three decimal places as needed.)
**Correlation Results:**
- **Correlation Coefficient \( r \):** \( 0.967366 \)
- **Critical \( r \):** \( \pm 0.2680885 \)
- **P-value (two-tailed):** \( 0.000 \)
The data suggests a significant correlation between the bears' chest sizes and weights, as indicated by the high correlation coefficient and a p-value less than 0.05.
Transcribed Image Text:The task involves matching the values of the correlation coefficient, \( r \), to the corresponding scatterplots. The given values are 0.395, -0.995, -1, -0.78, and 0.78.
**Scatterplots Overview:**
- **Scatterplot 2:** This scatterplot features points that appear to be densely spaced without any noticeable trend, suggesting a near-zero correlation.
- **Scatterplot 3:** This scatterplot displays points aligned closely along a line with a negative slope, indicating a strong negative correlation.
- **Scatterplot 5:** Points in this scatterplot are more dispersed, following a general downward trend, which suggests a moderate negative correlation.
**Task Instructions:**
- Match each scatterplot (1 through 5) to one of the correlation coefficients provided.
- Use the dropdown menu next to each scatterplot to select the correct correlation value based on the plot's pattern.
**Considerations:**
- A perfect negative linear relationship is represented by \( r = -1 \).
- Strong negative correlations have values close to -1.
- Moderate correlations have values around ±0.5.
- Weak correlations have values closer to 0.
This task helps in understanding the relationship between data points and how the correlation coefficient reflects that relationship.
Expert Solution
This question has been solved!
Explore an expertly crafted, step-by-step solution for a thorough understanding of key concepts.
This is a popular solution
Trending nowThis is a popular solution!
Step by stepSolved in 2 steps
Knowledge Booster
Similar questions
- After finding the range for the age variable, you worry that perhaps this value is being influenced by extreme scores! So, you decide to find the interquartile range. After all, you know the IQR has some benefits. This time, you go to SPSS and select Analyze > Descriptives > Frequencies. You put the age variable in the "variables" box and then click Statistics. Make sure Quartiles, Mean, Median, Mode, Range, Standard Deviation, and Variance are checked (because it never hurts to get lots of information :). You click continue, then ok.Using the Percentiles that are reported in the output (these are equivalent to Q1, Q2, and Q3), you are able to determine that the interquartile range for the "age" variable is: Group of answer choices 35 20 22 23.29 24 4arrow_forwardfirst picture is background informationarrow_forwardShow the steps on how to solve this. Preferrbly if you could do all of them if not can you please do C-Earrow_forward
- *PLEASE show in excel A different investigator conducts a second study to investigate whether there is a difference in mean PEF in children with chronic bronchitis as compared to those without. Data on PEF are collected and summarized below. Based on the data, is there statistical evidence of a lower mean PEF in children with chronic bronchitis as compared to those without? Run the appropriate test at a=0.05. Group Number of Children Mean PEF Std Dev PEF Chronic Bronchitis 25 281 68 No Chronic Bronchitis 25 319 74 Step 1. Set up hypotheses and determine level of significance. Ho: H1: alpha: Step 2. Select the appropriate test statistic and give its formula. Step 3. Set up decision rule. Step 4. Compute the test statistic Step 5. Conclusion.arrow_forwardHi team, Please provide the answer for the 4 parts of the question, thanks in advance. The last senence says : (please pick the correct lines) "(Fail to reject / Reject ) H0 . There is (Sufficient / Not sufficient) evidence to conclude that the mean lead concentration for all such medicines is (Not / Equal to / Greater than / Less than ) 18 μg/g."arrow_forward
arrow_back_ios
arrow_forward_ios
Recommended textbooks for you
- MATLAB: An Introduction with ApplicationsStatisticsISBN:9781119256830Author:Amos GilatPublisher:John Wiley & Sons Inc
Probability and Statistics for Engineering and th...StatisticsISBN:9781305251809Author:Jay L. DevorePublisher:Cengage Learning
Statistics for The Behavioral Sciences (MindTap C...StatisticsISBN:9781305504912Author:Frederick J Gravetter, Larry B. WallnauPublisher:Cengage Learning 
Elementary Statistics: Picturing the World (7th E...StatisticsISBN:9780134683416Author:Ron Larson, Betsy FarberPublisher:PEARSON
The Basic Practice of StatisticsStatisticsISBN:9781319042578Author:David S. Moore, William I. Notz, Michael A. FlignerPublisher:W. H. Freeman
Introduction to the Practice of StatisticsStatisticsISBN:9781319013387Author:David S. Moore, George P. McCabe, Bruce A. CraigPublisher:W. H. Freeman
MATLAB: An Introduction with Applications
Statistics
ISBN:9781119256830
Author:Amos Gilat
Publisher:John Wiley & Sons Inc
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
ISBN:9781305504912
Author:Frederick J Gravetter, Larry B. Wallnau
Publisher:Cengage Learning
Elementary Statistics: Picturing the World (7th E...
Statistics
ISBN:9780134683416
Author:Ron Larson, Betsy Farber
Publisher:PEARSON
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
Statistics
ISBN:9781319013387
Author:David S. Moore, George P. McCabe, Bruce A. Craig
Publisher:W. H. Freeman