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The image you've provided shows four graphs under the heading "Distribution Shapes," where the user is asked to determine the shape of each distribution.

Here is a breakdown of the shapes:

1. **First Distribution (Left)**: The distribution is skewed to the right (positively skewed). This type of distribution has a long right tail.
   
2. **Second Distribution (Center Left)**: The distribution is symmetric, resembling a **normal distribution** (bell-shaped curve). This distribution is not skewed and has symmetry about the center.

3. **Third Distribution (Center Right)**: This is a **uniform distribution**, where the probability is equally spread across all values. It looks like a flat line, indicating equal probability for all outcomes within a given range.

4. **Fourth Distribution (Right)**: The distribution is skewed to the left (negatively skewed). This type of distribution has a long left tail.

Would you like more details about the characteristics of these distributions or any further clarification?

Here are 5 relative questions to expand understanding:

1. What are the real-world examples of positively skewed distributions?
2. How can a normal distribution be used in hypothesis testing?
3. Why is a uniform distribution important in probability and statistics?
4. What are the implications of a negatively skewed distribution in data analysis?
5. How do skewness and kurtosis affect the shape of a distribution?

**Tip:** A key feature of normal distributions is that about 68% of the data falls within one standard deviation of the mean, making them useful in many statistical applications.

Determine the shape of each distribution shown in the image.

Learn to identify different distribution shapes such as right-skewed, normal (bell-shaped), uniform, and left-skewed distributions. This guide is suitable for students in Grades 9-12 and covers key concepts in statistics and probability.

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