One common method for measuring the size of a company is to use its market capitalization, which is computed by multiplying the number of stock shares by the price of a share of stock. A sample of 26 companies that make up the DJIA from April 2019 is provided below.

Tasks:

1. Normality Assessment Using Characteristics:
- Decide whether the data set appears to be approximately normally distributed by comparing data characteristics to theoretical properties.
- Note: The data are approximately normally distributed if the mean, range, and interquartile range match expected values.

2. Normality Assessment Using a Normal Probability Plot:
- Construct a normal probability plot.
- Choose the correct graph from options OA, OB, OC, or OD.

3. Interpret the Normal Probability Plot:
- What is the best description of the given data based on the normal probability plot?
- A. The data are skewed to the left.
- B. The data have too many outliers to be considered normally distributed.
- C. The data are skewed to the right.
- D. The data appear to be normally distributed.

4. Normality Assessment Using a Histogram:
- Construct a histogram.
- Choose the correct graph from options OA, OB, OC, or OD.

Market Capitalization Data (in $billion):

- 3M: 100.2
- American Express: 79.5
- Apple: 724.7
- Caterpillar: 52.5
- Chevron: 203.1
- Cisco: 147.5
- CocaCola: 178.5
- Disney: 188.3
- ExxonMobil: 371.2
- GE: 271.2
- Goldman Sachs: 85.3
- Home Depot: 140.3
- IBM: 170.4
- Johnson & Johnson: 275.9
- JPMorgan & Chase: 240.7
- McDonald's: 92.2
- Merck: 171.1
- Microsoft: 385.1
- Nike: 86.3
- P&G: 217.2
- Pfizer: 209.5
- Travelers: 32.6
- United Health Group: 107.9
- United Technologies: 103.7
- Visa: 162
- Walmart: 252

Instructions:
- Use the provided data to complete the tasks.
- Print and review the data to assist with completing the tasks.

It can be analyzed through comparing data characteristics like mean, range, and interquartile range with their theoretical expectations, constructing a normal probability plot, and plotting a histogram. Both graphical tools (the probability plot and the histogram) should show specific patterns for data to be considered normally distributed.

To analyze the data characteristics, you look at parameters such as the mean, range, and interquartile range. If these figures conform to what you would theoretically expect from a normally distributed set, it may be an indication that the data is indeed normally distributed.

The normal probability plot is a graphical tool that is used to understand if a data set is normally distributed. If the dots (representing data points) in the graph form a straight, diagonal line then it means the data is normally distributed. Any significant deviations from that straight line may indicate that the data isn't normally distributed.

Similarly, a histogram can also help to understand the distribution of a data set. A normally distributed data set would result in a bell-shaped curve. If the histogram of the given data forms this bell curve, it's another indication of normally distributed data. However, if the data is skewed to the right or left, or has too many outliers, it may not be normally distributed.

Learn more about the topic of Data Distribution Analysis here:

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