Diagnostic Statistics and Predictive Statistics as a Re-Definition to Inferential Statistics | Chapter 13 | Recent Advances in Mathematical Research and Computer Science Vol. 3

 In any statistical book, the typical method for statistics is to begin with descriptive statistics, then probability, and finally inferential statistics. The relationship between descriptive and inferential statistics is considered probability. Inferential statistics has a broad definition, and it is defined as "the field of statistics concerned with making inferences about a population using sample data." Predictions are produced and conclusions are reached for the target population based on the sample in inferential statistics." Estimation, Testing Hypotheses concerning Means, Variances, Goodness of Fit, and Proportions, Correlation, Regression, and Time Series are the main themes of inferential statistics.

We are attempting to arrange statistics in this article by dividing inferential statistics into two components, Diagnostic Statistics and Predictive Statistics, and explaining the significance of each. We'll also go over certain statistics from a different perspective.

As a result, we'll have four tiers of statistics to work with while analysing data (Descriptive, Diagnostic, Predictive and Perspective Statistics). Graphs, frequency tables, measures of central tendency, measurements of variation, and measures of form are all examples of descriptive statistics. The effects of the Independent variables (inputs) on the Dependent (Target) variable, as determined by Tests of Correlation or Association, Tests for Mean Differences, and Tests for Classification, are the focus of diagnostic statistics. Estimation, regression procedures, and time series analysis for the dependent (target) variable are the main concerns of predictive statistics. Perspective statistics are mostly related to the preceding three levels and serve as a prescription for how to solve or prevent an issue, implying that a decision must be made ahead of time. In this article, we'll explain the concept by using a real-world example of Gynecologic Cancer data to demonstrate how perspective analytics might help avoid it.

Author(S) Details

Ezz H. Abdelfattah
Department of Statistics, Faculty of Science, King Abdulaziz University, Saudi Arabia.

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