Which of the following is true of density curves? A legitimate density curve must follow the 68-95-99.7 rule. Areas under a density curve determine probabilities of outcomes. The total area under a density curve is 1. Density curves cannot be used to model sampling distributions. The Normal curve is a density curve.
Which of the following is true of density curves? A legitimate density curve must follow the 68-95-99.7 rule. Areas under a density curve determine probabilities of outcomes. The total area under a density curve is 1. Density curves cannot be used to model sampling distributions. The Normal curve is a density curve.
MATLAB: An Introduction with Applications
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
Transcribed Image Text:**Understanding Density Curves**
**Question:**
Which of the following is true of density curves?
1. A legitimate density curve must follow the 68–95–99.7 rule.
2. Areas under a density curve determine probabilities of outcomes.
3. The total area under a density curve is 1.
4. Density curves cannot be used to model sampling distributions.
5. The Normal curve is a density curve.
**Explanation:**
- **Density Curve:** A graphical representation showing the distribution of a continuous probability. It is often used in statistics to understand data distributions and determine probabilities.
- **68–95–99.7 Rule:** Also known as the empirical rule, it applies specifically to normal distributions. It indicates that approximately 68% of data falls within one standard deviation, 95% within two, and 99.7% within three from the mean. Not all density curves need to follow this rule.
- **Area and Probabilities:** The area under a density curve is directly related to probability. For a given interval, the area represents the probability of an outcome falling within that range.
- **Total Area Equals 1:** This is a fundamental property of density curves; they are normalized so that the entire area under the curve sums to 1, representing the certainty of the event.
- **Modeling Distributions:** Density curves can indeed model sampling distributions, providing insights into the behavior of sample data.
- **Normal Curve:** It is a type of density curve, representing a normal distribution, characterized by its bell shape and symmetry about the mean.
Each of these points is crucial for interpreting and applying density curves in statistical analysis.
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