ow is a graph of a normal distribution with mean u =-5 and standard deviation o =4. The shaded region represents the probability of o m this distribution that is between -1 and 1. 0.4- 0.3- 02- 0.1- - hade the corresponding region under the standard normal density curve below.
Continuous Probability Distributions
Probability distributions are of two types, which are continuous probability distributions and discrete probability distributions. A continuous probability distribution contains an infinite number of values. For example, if time is infinite: you could count from 0 to a trillion seconds, billion seconds, so on indefinitely. A discrete probability distribution consists of only a countable set of possible values.
Normal Distribution
Suppose we had to design a bathroom weighing scale, how would we decide what should be the range of the weighing machine? Would we take the highest recorded human weight in history and use that as the upper limit for our weighing scale? This may not be a great idea as the sensitivity of the scale would get reduced if the range is too large. At the same time, if we keep the upper limit too low, it may not be usable for a large percentage of the population!
![### Understanding Normal Distribution and Standard Normal Density Curves
Below is a graph of a normal distribution with a mean (\(\mu\)) of \(-5\) and a standard deviation (\(\sigma\)) of 4. The shaded region represents the probability of obtaining a value from this distribution that is between \(-1\) and 1.
![Normal Distribution](normal_distribution_graph.jpg)
#### Explanation of the Graph:
- *Mean (\(\mu\))*: -5
- *Standard Deviation (\(\sigma\))*: 4
- *Shaded Region*: Represents the probability of a value falling between -1 and 1.
On this graph, the x-axis shows the possible values of the normally distributed random variable, and the y-axis shows the corresponding probability densities. The curve demonstrates the bell shape characteristic of normal distributions, peaking at the mean of -5.
### Graphical Representation of the Corresponding Standard Normal Density Curve
Next, we transition to the standard normal density curve by standardizing the normal distribution shown above.
![Standard Normal Distribution](standard_normal_distribution_graph.jpg)
#### Explanation of the Graph:
This graph represents the standard normal distribution, where the mean is 0 and the standard deviation is 1. The shaded area corresponds to the same probability region as the initial graph but transformed to the standard scale.
**Key Points on the Standard Normal Density Graph:**
- The peak of the curve is at 0.
- The x-axis now shows z-scores, which are standardized values.
- The y-axis shows probability densities for the standard normal distribution.
In summary, these graphs help elucidate the concept of normal distributions and the transformation to standard normal distributions, showcasing how probabilities can be visualized and calculated within these statistical frameworks.
### Additional Resources:
- [Khan Academy: Normal Distribution](https://www.khanacademy.org/math/statistics-probability)
- [Wolfram MathWorld: Normal Distribution](http://mathworld.wolfram.com/NormalDistribution.html)
For further understanding, explore these resources to deepen your knowledge of how normal distributions are used in statistics!](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F28e9fa55-7beb-4d98-aad2-e62c3133051d%2F580e3c71-bf3d-4ea8-bffa-d573043b7c78%2F7htdq9r_processed.jpeg&w=3840&q=75)
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