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Statistical Analysis in Business

1-2. Explain the principles of statistical thinking. Why is statistical thinking an important managerial skill?
1-10.Explain the differences between categorical, ordinal, interval, and ratio data.
1-11. Explain the difference between cross-sectional and time-series data.
1-12. What is the difference between a population and a sample?
2-3. Explain the principal types of descriptive statistics measures that are used for describing data.
2-5. What statistical measures are used for describing dispersion in data? How do they differ from one another?
2-6. Explain the importance of the standard deviation in interpreting and drawing conclusions about risk.
2-12. Explain the concept of correlation and how to interpret correlation coefficients of 0.3, 0, and -0.95.

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To understand this question, we first need to have information on each of the subjects covered:
1-2
Statistical Thinking is the process of using statistics when making business decisions. What this looks like in a company is a manager has the ability to choose a project that has a 70% chance of making a profit instead of a project that has only a 30% chance of making the same profit. This is important for the firm to use all available data when making decisions to maximize profitability.
1-10
Categorical Data can be organized into "categories" based on an observable trait. Examples would be male/female, eye color, political party or any other characteristic.
Ordinal Data contains values that can be counted and ranked into a certain order. For example, runners position in a race, number of years in school or some other characteristic that can be counted.
Interval Data is measured along a scale with an equivalent measurement. This allows comparison between two pairs. Examples would be temperature or rating something on a scale of 1-10.
Ratio Data has numbers that can be compared to one another. An example of ...

Solution Summary

Statistical analysis in business are examined. The principles of statistical thinking are determined.

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