One Click Universal Probability Calculator
First Claim
1. A product that puts together the following features:
- 1.1 Calculate probabilities for continuous and discrete data.1.2 Return an estimation of the quality/accuracy of the answer (confidence level).1.3 Based on one-click procedure;
requiring the user to perform only the following actions;
a. Provide the sample data by importing a file or pasting/typing the data.b. Enter a value of the cut-off point x for which is desired to calculate the probability and the desired math symbol (<
, ≤
, >
, ≥
, =).c. Click on a button (or equivalent trigger) as described in Section 3.1.Note that step b might be optional. If the user does not specify them, the tool can just compute probabilities for different values of x and return all probabilities to the user.1.4 Calculate probabilities without requiring statistical knowledge from the user. It means a tool requiring from the user none of the following actions;
a) Normality test.b) Test of goodness to identify which distribution function better fits the data set.c) Use of transformation methods such as Johnson'"'"'s family of distribution.d) Knowledge of the type of the probability function (gamma, log-normal, exponential and others).e) Knowledge of the nature of the variable;
continuous or discrete.f) Frequency table.g) Utilization of an assistant in the interface of the tool where the user provides answers to a set of questions to guide him in the utilization of the correct statistical method.
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Abstract
An apparatus and a method to assist people who is not an expert in statistics to calculate probabilities when in possession of a set of data. The purpose of the “One Click Universal Probability Calculator” is to be a practical and simple tool to calculate probabilities given a data set with continuous or discrete values not requiring statistical knowledge from the user. The tool is one-click based, requiring minimum actions from the user. It also provides an estimate for the uncertainty of the calculated probability in an intuitive way for the user. All the related statistical concepts are treated in the background by our new method. The tool can be presented to the user in different ways: website/software, executable file, code library file (.dll) for integration with other software, and finally, embedded into an electronic pocket calculator.
2 Citations
3 Claims
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1. A product that puts together the following features:
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1.1 Calculate probabilities for continuous and discrete data. 1.2 Return an estimation of the quality/accuracy of the answer (confidence level). 1.3 Based on one-click procedure;
requiring the user to perform only the following actions;a. Provide the sample data by importing a file or pasting/typing the data. b. Enter a value of the cut-off point x for which is desired to calculate the probability and the desired math symbol (<
, ≤
, >
, ≥
, =).c. Click on a button (or equivalent trigger) as described in Section 3.1. Note that step b might be optional. If the user does not specify them, the tool can just compute probabilities for different values of x and return all probabilities to the user. 1.4 Calculate probabilities without requiring statistical knowledge from the user. It means a tool requiring from the user none of the following actions; a) Normality test. b) Test of goodness to identify which distribution function better fits the data set. c) Use of transformation methods such as Johnson'"'"'s family of distribution. d) Knowledge of the type of the probability function (gamma, log-normal, exponential and others). e) Knowledge of the nature of the variable;
continuous or discrete.f) Frequency table. g) Utilization of an assistant in the interface of the tool where the user provides answers to a set of questions to guide him in the utilization of the correct statistical method. - View Dependent Claims (2)
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3. A product benefiting from the method described in the section 3.2, applied for continuous and discrete distributions, based on the following milestones:
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a. Method described in Section 3.2.1.1 allowing the split of a value between two adjacent intervals of the frequency table. b. Utilization of piecewise functions formed by two polynomial equations to estimate the cumulative function directly from the frequency table (Section 3.2.1.2). c. Utilization of a method that performs the calculations for different number of bins, and based on a quality score, combines the results of the best ones to have a final result (Algorithms 1 and
2).
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Specification