Question

Use a graphing utility to graph the normal probability density function with \(\mu=0\) and \(\sigma=2,3\), and 4 in the same viewing window. What effect does the standard deviation \(\sigma\) have on the function? Explain your reasoning.

Short answer

The standard deviation, \(\sigma\), affects the spread of the distribution in the Normal probability density function. As \(\sigma\) increases, the distribution becomes wider and less peaked, indicating that the data are more dispersed around the mean.

Step-by-step solution

Plot the first graph with \(\sigma = 2\)

Use the formula \(f(x) = \frac{1}{\sqrt{2π(2)^2}}e^{-(x - 0)^2/(2(2)^2)}\) and graph it using the graphing utility. Note the width and height of the bell curve. The graph will show a normal distribution centered around 0.

Plot the second graph with \(\sigma = 3\)

Similarly, plot the second instance by using the formula \(f(x) = \frac{1}{\sqrt{2π(3)^2}}e^{-(x - 0)^2/(2(3)^2)}\). Notice the differences in the width and height as compared to the first graph.

Plot the third graph with \(\sigma = 4\)

Finally, plot the third instance with the formula \(f(x) = \frac{1}{\sqrt{2π(4)^2}}e^{-(x - 0)^2/(2(4)^2)}\). Again, compare the differences in the width and height with the previous graphs.

Analyze the Differences

Observe the differences between each of the graphs. The standard deviation, \(\sigma\), has an effect on the spread of the distribution. As \(\sigma\) increases, the curve becomes wider and less tall. This indicates that data is more dispersed around the mean.

Key concepts

Standard Deviation

The standard deviation, often denoted by the symbol \( \sigma \), is a key concept in understanding the normal distribution. It measures how spread out the values in a data set are around the mean.

In the context of the normal distribution, the standard deviation affects the shape of the bell curve, which is the graphical representation of how probabilities are distributed. A smaller standard deviation results in a steeper, taller bell curve indicating that the data points are closely clustered around the mean. Conversely, a larger standard deviation means a wider and flatter curve, suggesting that the data points are more spread out.

• A small \( \sigma \) makes the curve narrow and high; data is less spread out.
• A large \( \sigma \) makes the curve wide and low; data is more spread out.

Adjusting \( \sigma \) is like zooming in and out on the distribution; you can see how tightly or loosely the data is clustered around the mean.

Probability Density Function

The probability density function (PDF) describes the likelihood of a continuous random variable to take on a particular value. For a normal distribution, the PDF is given by the formula: \[ f(x) = \frac{1}{\sigma \sqrt{2\pi}} e^{-\frac{(x - \mu)^2}{2\sigma^2}} \] This formula is central to the normal distribution and helps determine how probabilities are allocated across different values.

The curve of the PDF provides a visual representation of which values of a random variable are more likely. Key characteristics include:

• The area under the entire curve sums to 1, representing the total probability.
• The peak of the curve is at the mean, \( \mu \).
• The shape and spread depend heavily on the standard deviation, \( \sigma \).

Understanding the PDF helps us anticipate how likely a range of outcomes is, especially when making predictions or conducting data analysis. Using this, alongside visual tools like graphing utilities, solidifies comprehension of how distribution functions work.

Graphing Utility

A graphing utility is a valuable tool for visualizing mathematical concepts, such as normal distributions. By using such tools, one can plot complex functions with ease and better understand their properties.

Graphing utilities assist in comparing multiple normal distributions by overlaying them in the same viewing window. This visualization can reveal how changes in parameters, such as the standard deviation, affect the shape and spread of distributions.

Using a graphing utility:

• Input the formula for the probability density function of a normal distribution.
• Adjust parameters like \( \sigma \) to observe its impact on the graph.
• Effortlessly compare different datasets or theories by plotting several functions simultaneously.

These graphical insights can make abstract statistical concepts clearer, allowing students to see tangible evidence of how mathematical formulas manifest as shapes and distributions. Knowing how to effectively use graphing utilities enhances one's understanding of the material and promotes a deeper engagement with the subject matter.