10- Refer to the accompanying scatterplot. a. Examine the pattern of all 10 points and subjectively determine whether there appears to be a strong correlation between x and y. b. Find the value of the correlation coefficient r and determine whether there is a linear correlation. c. Remove the point with coordinates (9,10) and find the correlation coefficient r and determine whether there is a linear correlation. d. What do you conclude about the possible effect from a single pair of values? Click here to view a table of critical values for the correlation coefficient. a. Do the data points appear to have a strong linear correlation? Yes No b. What is the value of the correlation coefficient for all 10 data points? (Simplify your answer. Round to three decimal places as needed.) r= Table of Critical Values D₂₁ n 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 α = .05 a .950 .878 .811 .754 .707 .666 .632 .602 .576 .553 .532 514 .497 .482 .468 456 444 α = .01 .990 .959 .917 .875 .834 .798 .765 .735 .708 .684 .661 .641 .623 .606 .590 .575 .561 X 0 10

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**Correlation and Critical Values Exploration**

Refer to the accompanying scatterplot. 

**Tasks:**

1. **Examine the data and determine correlation:**
   - Subjectively determine whether the pattern of all 10 points suggests a strong correlation between x and y.

2. **Calculate correlation coefficient r:**
   - Determine the correlation coefficient for all 10 data points and decide if a linear correlation exists.

3. **Effect of removing a point:**
   - Remove the point with coordinates (9,10) and find the new correlation coefficient r. Discuss the potential impact of a single pair of values on correlation.

**Scatterplot Analysis:**

- **a. Do the data points appear to have a strong linear correlation?**
  - Yes (indicated by a checkmark).

- **b. Calculate the correlation coefficient (r):**
  - Enter your answer in the provided box. Make sure to simplify and round your answer to three decimal places.

**Table of Critical Values for the Correlation Coefficient:**

- **Explanation of Table:**
  - The table provides critical values for different sample sizes (n) at significance levels (\(\alpha\)) of 0.05 and 0.01. 
  - For example:
    - For \( n = 4 \), the critical value at \( \alpha = 0.05 \) is 0.950, and at \( \alpha = 0.01 \) is 0.990.
    - For larger sample sizes, such as \( n = 10 \), the critical values are 0.632 (at \( \alpha = 0.05 \)) and 0.765 (at \( \alpha = 0.01 \)).
  - This table helps determine the strength of the correlation by comparing the calculated r to these critical values.

Use this guide to understand how correlation is assessed and the influence of critical values in determining statistical significance.
Transcribed Image Text:**Correlation and Critical Values Exploration** Refer to the accompanying scatterplot. **Tasks:** 1. **Examine the data and determine correlation:** - Subjectively determine whether the pattern of all 10 points suggests a strong correlation between x and y. 2. **Calculate correlation coefficient r:** - Determine the correlation coefficient for all 10 data points and decide if a linear correlation exists. 3. **Effect of removing a point:** - Remove the point with coordinates (9,10) and find the new correlation coefficient r. Discuss the potential impact of a single pair of values on correlation. **Scatterplot Analysis:** - **a. Do the data points appear to have a strong linear correlation?** - Yes (indicated by a checkmark). - **b. Calculate the correlation coefficient (r):** - Enter your answer in the provided box. Make sure to simplify and round your answer to three decimal places. **Table of Critical Values for the Correlation Coefficient:** - **Explanation of Table:** - The table provides critical values for different sample sizes (n) at significance levels (\(\alpha\)) of 0.05 and 0.01. - For example: - For \( n = 4 \), the critical value at \( \alpha = 0.05 \) is 0.950, and at \( \alpha = 0.01 \) is 0.990. - For larger sample sizes, such as \( n = 10 \), the critical values are 0.632 (at \( \alpha = 0.05 \)) and 0.765 (at \( \alpha = 0.01 \)). - This table helps determine the strength of the correlation by comparing the calculated r to these critical values. Use this guide to understand how correlation is assessed and the influence of critical values in determining statistical significance.
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