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Diagnostic Statistics and Predictive Statistics as a Re-Definition to Inferential Statistics

Ezz H. Abdelfattah

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Abstract

The traditional way for statistics in any statistical book starting with descriptive statistics, followed by Probability and ends with inferential Statistics. The probability is considered as the link between descriptive and inferential statistics. Inferential Statistics has wide definition and is defined as “the branch of statistics concerned with using sample data to make inferences about a population. In inferential statistics, predictions are made and conclusions are drawn for the target population based on the sample”. The main topics of inferential statistics are Estimation, Testing Hypotheses about means, variances, goodness of fit and proportions, Correlation, Regression and Time series. In this article we are trying to organize the statistics by splitting the inferential statistics into two parts, namely Diagnostic Statistics and Predictive Statistics and explaining the importance of each part. Also we will discuss a Perspective Statistics. Based on that we will have four levels in statistics that can be used to analyze data (Descriptive, Diagnostic, Predictive and Perspective Statistics). Descriptive statistics are primarily concerned with graphs, frequency tables, measures of central tendency, measures of variation, and measures of shape. Diagnostic statistics are primarily concerned with the effects of the Independent variables (inputs) on the Dependent (Target) variable, as measured by Tests of Correlation or Association, Tests for Mean Differences, and Tests for Classification. Predictive statistics are primarily concerned with estimation, regression techniques, and time series analysis for the dependent (target) variable. Perspective statistics mainly related to the previous three levels and acts as a prescription to how to solve or prevent the problem, which means to take a decision in advance. In this article, we will clarify idea through giving an example on a real data related to Gynecologic Cancer, and show how the perspective analytics can prevent it.

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What this paper is about

The traditional way for statistics in any statistical book starting with descriptive statistics, followed by Probability and ends with inferential Statistics. The probability is considered as the link between descriptive and inferential statistics. Inferential Statistics has wide definition and is defined as “the branch of statistics concerned with using sample data to make inferences about a population. In inferential statistics, predictions are made and conclusions are drawn for the target population based on the sample”. The main topics of inferential statistics are Estimation, Testing Hypotheses about means, variances, goodness of fit and proportions, Correlation, Regression and Time series. In this article we are trying to organize the statistics by splitting the inferential statistics into two parts, namely Diagnostic Statistics and Predictive Statistics and explaining the importance of each part. Also we will discuss a Perspective Statistics. Based on that we will have four levels in statistics that can be used to analyze data (Descriptive, Diagnostic, Predictive and Perspective Statistics). Descriptive statistics are primarily concerned with graphs, frequency tables, measures of central tendency, measures of variation, and measures of shape. Diagnostic statistics are primarily concerned with the effects of the Independent variables (inputs) on the Dependent (Target) variable, as measured by Tests of Correlation or Association, Tests for Mean Differences, and Tests for Classification. Predictive statistics are primarily concerned with estimation, regression techniques, and time series analysis for the dependent (target) variable. Perspective statistics mainly related to the previous three levels and acts as a prescription to how to solve or prevent the problem, which means to take a decision in advance. In this article, we will clarify idea through giving an example on a real data related to Gynecologic Cancer, and show how the perspective analytics can prevent it.

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Available abstract

The traditional way for statistics in any statistical book starting with descriptive statistics, followed by Probability and ends with inferential Statistics. The probability is considered as the link between descriptive and inferential statistics. Inferential Statistics has wide definition and is defined as “the branch of statistics concerned with using sample data to make inferences about a population. In inferential statistics, predictions are made and conclusions are drawn for the target population based on the sample”. The main topics of inferential statistics are Estimation, Testing Hypotheses about means, variances, goodness of fit and proportions, Correlation, Regression and Time series. In this article we are trying to organize the statistics by splitting the inferential statistics into two parts, namely Diagnostic Statistics and Predictive Statistics and explaining the importance of each part. Also we will discuss a Perspective Statistics. Based on that we will have four levels in statistics that can be used to analyze data (Descriptive, Diagnostic, Predictive and Perspective Statistics). Descriptive statistics are primarily concerned with graphs, frequency tables, measures of central tendency, measures of variation, and measures of shape. Diagnostic statistics are primarily concerned with the effects of the Independent variables (inputs) on the Dependent (Target) variable, as measured by Tests of Correlation or Association, Tests for Mean Differences, and Tests for Classification. Predictive statistics are primarily concerned with estimation, regression techniques, and time series analysis for the dependent (target) variable. Perspective statistics mainly related to the previous three levels and acts as a prescription to how to solve or prevent the problem, which means to take a decision in advance. In this article, we will clarify idea through giving an example on a real data related to Gynecologic Cancer, and show how the perspective analytics can prevent it.

Key concepts: Statistics, Descriptive statistics, Statistical inference, Summary statistics, Population, Mathematics, Econometrics, Demography

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