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Fat contents (x, in grams) for seven randomly selected frozen pizza brands that were among the brands receiving the highest ratings by a certain website are shown below.
9, 11, 10, 13, 10, 15, 8
The normal scores for a sample of size 7 are as follows.
−1.364, −0.758, −0.353, 0, 0.353, 0.758, 1.364
Calculate the correlation coefficient for the (normal score, x) pairs. (Round your answer to four decimal places.)

The correlation coefficient for the (normal score, fat content) pairs is approximately **0.9682** (rounded to four decimal places). This indicates a strong positive correlation between the two variables.

Would you like more details on how this was calculated or the interpretation? 

Here are 5 related questions to consider:
1. How is the Pearson correlation coefficient used in statistics?
2. Why does a correlation coefficient close to 1 indicate a strong relationship?
3. What are normal scores, and how are they related to data distribution?
4. How can you interpret negative correlation values compared to positive ones?
5. What are some limitations of using Pearson correlation?

**Tip:** A correlation coefficient only measures linear relationships. Non-linear relationships require different methods.

Fat contents (x, in grams) for seven randomly selected frozen pizza brands that were among the brands receiving the highest ratings by a certain website are shown below. 9, 11, 10, 13, 10, 15, 8 The normal scores for a sample of size 7 are as follows. −1.364, −0.758, −0.353, 0, 0.353, 0.758, 1.364 Calculate the correlation coefficient for the (normal score, x) pairs. (Round your answer to four decimal places.)

Learn how to calculate the correlation coefficient for fat content in frozen pizza brands using the normal scores. This detailed step-by-step solution involves applying the Pearson correlation coefficient formula, helping you understand how fat content is related to a statistical normal score. Suitable for college or upper high school students.

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