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What is Classical Statistics and how can it be applied to your company or industry?
Classical statistics is the foundation of numerous data analysis, modeling, and forecasting techniques. It is used to discover relationships and uncover trends found in numerical data, providing valuable insights to guide further decision making. Classical statistics is also used to guide and develop data collection techniques, such as in clinical trial design or other experimental design, and in manufacturing quality control.
Classical statistical techniques power a multitude of data analyses aimed at uncovering relationships between numerical variables, including:
- Descriptive statistics
- Information that summarizes data such as mean (average), median, minimum and maximum values, and standard deviation
- Hypothesis testing
- Comparison of two or more data samples against a hypothesis – a typical example is the t-test, which tests the hypothesis that the mean of two samples is the same
- Correlation analyses
- Determines the strength of a relationship between two data samples
- Regression analysis and Predictive modeling
- Determines the relationship between an outcome variable and one or more predictor variables, how strongly the predictors relate to the outcome, and use those relationships to predict future outcomes
- Clustering analyses
- Classifies variables into groups based on how similar they are
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About the Author
The information contained within this article is provided for informational purposes only and is current as of the date published. Online readers are advised not to act upon this information without seeking the service of a professional accountant, as this article is not a substitute for obtaining accounting, tax, or financial advice from a professional accountant.