The SAT has the following population parameters (µ = 1000, σ = 200). Plot (draw and upload a picture of) the SAT population distribution. Label the:

mean
standard deviations (-3, -2, -1, 0, +1, +2, +3)
the raw scores that correspond to each standard deviation.
Then:

denote where someone with a score of 900 could be located
denote where someone with a score of 1300 would be located.

Here is the plot of the SAT population distribution, which includes the following elements:

- **Mean (µ = 1000)**, marked by the green vertical line.
- **Standard deviations** from -3σ to +3σ (at 600, 800, 1000, 1200, 1400), marked by red dashed lines.
- **Raw scores** corresponding to each standard deviation.
- **Score of 900**, marked in orange, located between -1σ and 0σ.
- **Score of 1300**, marked in purple, located between +1σ and +2σ.

Do you need further details or clarifications?

Here are some related questions to consider:
1. What percentage of students score between 1000 and 1200 on the SAT?
2. How does a score of 900 compare to the overall SAT population?
3. What is the probability of scoring below 1300 on the SAT?
4. How would the distribution change if the mean SAT score increased?
5. What SAT score corresponds to being at the 95th percentile?

**Tip:** Standard deviation helps in understanding how spread out the scores are from the mean in any normal distribution.

The SAT has the following population parameters (µ = 1000, σ = 200). Plot (draw and upload a picture of) the SAT population distribution. Label the: mean, standard deviations (-3, -2, -1, 0, +1, +2, +3), and the raw scores that correspond to each standard deviation. Then: denote where someone with a score of 900 could be located and where someone with a score of 1300 would be located.

Explore the SAT population distribution with a mean of 1000 and a standard deviation of 200. Learn to plot the distribution and identify scores at different standard deviations, including specific locations for scores of 900 and 1300. Ideal for students in Grades 10-12 studying normal distribution and statistics.

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