Statistical Quality Analytics API

API ID 13195

Descriptive statistics, normality, correlation, and process capability (Cp/Cpk/Pp/Ppk) as a JSON API. Send a data table and a config, get clean result tables back.

API Documentation

Endpoints

Request

Test whether a numeric column follows a normal distribution using the Anderson-Darling, Shapiro-Wilk, or Kolmogorov-Smirnov method, returning the test statistic and p-value with a plain-language conclusion.

Endpoint ID: 26905
POST https://zylalabs.com/api/13195/statistical+quality+analytics+api/26905/normality+test
INPUT PARAMETERS

Normality Test — Endpoint Features

Object Description
data Required Measurement data rows as a JSON array
Request Body Required Json

Free test requests remaining: 3 of 3.


INPUT PARAMETERS

data
API EXAMPLE RESPONSE
JSON
{"title":"Normality Test","outputs":{"statistics":{"type":"table","title":"Statistics of Percent Fat","rows":[{"Column":"Percent Fat","N":10,"Mean":17.34,"Std Dev":0.5699902533,"Method":"AD","Statistic":0.2184933607,"P-Value":0.7773704367,"Result":"Data is normally distributed."}]},"table_interpretation":{"type":"table","title":"Interpretation of Percent Fat","rows":[{"Case":"Anderson-Darling Test","Method Description":"The Anderson-Darling test is more effective at detecting non-normality in the tails of the distribution. It is particularly good at detecting deviations from normality in the tail regions of the distribution.","Interpretation":"p-value > 0.05(significance level): Fail to reject null hypothesis -> Data appears to follow a normal distribution","Summary":"p-value = 0.78 is greater than the significance level 0.05. The data can be interpreted as normally distributed."}]}}}
Normality Test — CODE SNIPPETS

curl --location --request POST 'https://zylalabs.com/api/13195/statistical+quality+analytics+api/26905/normality+test' --header 'Authorization: Bearer YOUR_API_KEY' 

--data-raw '{"data":[{"Percent Fat":16.9},{"Percent Fat":18.0},{"Percent Fat":17.2},{"Percent Fat":17.9},{"Percent Fat":16.4},{"Percent Fat":17.5},{"Percent Fat":18.1},{"Percent Fat":16.8},{"Percent Fat":17.0},{"Percent Fat":17.6}],"config":{"var_column":"Percent Fat","alpha":0.05,"method_of_analysis":"AD"}}'

    
Request

Compute summary statistics such as mean, standard deviation, variance, quartiles, and confidence intervals for one or more numeric columns of a data table.

Endpoint ID: 26906
POST https://zylalabs.com/api/13195/statistical+quality+analytics+api/26906/descriptive+statistics
INPUT PARAMETERS

Descriptive Statistics — Endpoint Features

Object Description
Request Body Required Json

Free test requests remaining: 3 of 3.


INPUT PARAMETERS

API EXAMPLE RESPONSE
JSON
{"title":"Descriptive Statistics: value","outputs":{"descriptive_statistics_table":{"type":"table","title":"Statistics","rows":[{"variable":"value","n_total":5,"mean":10.0,"std_dev":0.158113883,"min":9.8,"median":10.0,"max":10.2}]}}}
Descriptive Statistics — CODE SNIPPETS

curl --location --request POST 'https://zylalabs.com/api/13195/statistical+quality+analytics+api/26906/descriptive+statistics' --header 'Authorization: Bearer YOUR_API_KEY' 

--data-raw '{"data":[{"value":9.8},{"value":10.2},{"value":9.9},{"value":10.1},{"value":10.0}],"config":{"variables":["value"],"confidence_level":0.95,"statistics_options":["n_total","mean","std_dev","min","median","max"],"graph_options":[]}}'

    
Request

Compute Pearson, Spearman, or Kendall correlation between numeric columns, with confidence intervals and p-values reported for each pair of variables.

Endpoint ID: 26907
POST https://zylalabs.com/api/13195/statistical+quality+analytics+api/26907/correlation
INPUT PARAMETERS

Correlation — Endpoint Features

Object Description
Request Body Required Json

Free test requests remaining: 3 of 3.


INPUT PARAMETERS

API EXAMPLE RESPONSE
JSON
{"title":"Correlation","outputs":{"method_table":{"type":"table","title":"Method_table","rows":[{"method":"correlation_type","value":"pearson"},{"method":"number_of_rows_used","value":5}]},"pairwise_correlation_table":{"type":"table","title":"Pairwise pearson Correlation_table","rows":[{"sample_1":"x","sample_2":"y","n":5,"correlation":0.9986517556,"p_value":0.0000594154,"ci_low":0.9786605332,"ci_high":0.9999156156}]}}}
Correlation — CODE SNIPPETS

curl --location --request POST 'https://zylalabs.com/api/13195/statistical+quality+analytics+api/26907/correlation' --header 'Authorization: Bearer YOUR_API_KEY' 

--data-raw '{"data":[{"x":1,"y":2.1},{"x":2,"y":3.9},{"x":3,"y":6.2},{"x":4,"y":7.8},{"x":5,"y":10.1}],"config":{"var_column":["x","y"],"confidence_level":0.95,"methods_of_analysis":"pearson","graph_opption":[]}}'

    
Request

Compute process capability indices Cp, Cpk, Pp, Ppk, and Cpm, plus observed and expected PPM out of specification, from subgrouped or individual measurement data against spec limits.

Endpoint ID: 26908
POST https://zylalabs.com/api/13195/statistical+quality+analytics+api/26908/normal+capability+analysis
INPUT PARAMETERS

Normal Capability Analysis — Endpoint Features

Object Description
Request Body Required Json

Free test requests remaining: 3 of 3.


INPUT PARAMETERS

API EXAMPLE RESPONSE
JSON
{"title":"Normal Capability Analysis","outputs":{"statistics_table":{"type":"table","title":"Process Data","rows":[{"Mean":74.006,"StDev (Overall)":0.0172378321,"N":15,"USL":74.05,"LSL":73.95,"Target":74.0,"Variable":"Diameter","StDev (Within)":0.0200630553}]},"capability_statistics":{"type":"table","title":"Capability Statistics","rows":[{"Cp":0.8307142857,"Cpl":0.9304,"Cpu":0.7310285714,"Cpk":0.7310285714,"Pp":0.9668655852,"Ppl":1.0828894554,"Ppu":0.850841715,"Ppk":0.850841715,"Cpm":0.7958862146}]},"capability_ppm":{"type":"table","title":"Capability PPM","rows":[{"Type":"PPM Below LSL","Observed":0.0,"Exp. Overall":579.7328456759,"Exp. Within":2625.6506421635},{"Type":"PPM Above USL","Observed":0.0,"Exp. Overall":5347.2594868867,"Exp. Within":14150.6020432928},{"Type":"PPM Total","Observed":0.0,"Exp. Overall":5926.9923325626,"Exp. Within":16776.2526854563}]}}}
Normal Capability Analysis — CODE SNIPPETS

curl --location --request POST 'https://zylalabs.com/api/13195/statistical+quality+analytics+api/26908/normal+capability+analysis' --header 'Authorization: Bearer YOUR_API_KEY' 

--data-raw '{"data":[{"Diameter":74.03},{"Diameter":74.0},{"Diameter":74.01},{"Diameter":74.02},{"Diameter":73.99},{"Diameter":73.98},{"Diameter":74.01},{"Diameter":74.0},{"Diameter":74.02},{"Diameter":74.03},{"Diameter":74.0},{"Diameter":73.97},{"Diameter":74.01},{"Diameter":74.02},{"Diameter":74.0}],"config":{"variable":"Diameter","subgroup_size":5,"lower_spec":73.95,"upper_spec":74.05,"target_number":74.0,"methods_of_analysis":"rbar"}}'

    

API Access Key & Authentication

After signing up, every developer is assigned a personal API access key, a unique combination of letters and digits provided to access to our API endpoint. To authenticate with the Statistical Quality Analytics API simply include your bearer token in the Authorization header.

Headers
Header Description
Authorization Required Should be Bearer access_key. See "Your API Access Key" above when you are subscribed.

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Overview

Statistical Quality Analytics API brings engineering-grade statistics to any application through a simple HTTP interface. Send a table of measurement data and a small configuration object, and receive clean, structured JSON results. It is built for manufacturing, quality engineering, laboratory, and data teams who need defensible statistical results for reports, dashboards, audits, and automated pipelines.

Compute descriptive statistics (mean, standard deviation, quartiles, confidence intervals), normality tests (Anderson-Darling, Shapiro-Wilk, Kolmogorov-Smirnov), correlation (Pearson, Spearman, Kendall with confidence intervals and p-values), and process capability (Cp, Cpk, Pp, Ppk, Cpm with PPM). Every endpoint takes the same shape: a data array plus a config object, and returns labeled JSON tables. Results are numerically validated to high precision against industry-standard reference software.

Statistical Quality Analytics API FAQs

Each endpoint returns structured JSON data. For example, the Normality Test endpoint provides statistics like test statistic and p-value, while the Descriptive Statistics endpoint returns summary metrics such as mean, standard deviation, and quartiles.

Key fields vary by endpoint. For Normality Test, fields include "Statistic" and "P-Value." In Descriptive Statistics, fields include "mean," "std_dev," and "min." Each endpoint's response is tailored to its specific analysis.

Response data is organized into labeled tables. Each table contains rows of relevant statistics or interpretations. For instance, the Correlation endpoint includes a "pairwise_correlation_table" with correlation values and p-values for each variable pair.

Each endpoint accepts a data array and a configuration object. For example, the Normality Test requires a numeric column and the chosen test method (Anderson-Darling, Shapiro-Wilk, or Kolmogorov-Smirnov) as parameters.

Users can customize requests by specifying the numeric columns to analyze and selecting the statistical methods in the configuration object. For instance, in the Correlation endpoint, users can choose between Pearson, Spearman, or Kendall methods.

Typical use cases include quality control in manufacturing, statistical reporting for audits, and data analysis in laboratories. Users can leverage the API for generating dashboards or automating statistical analysis pipelines.

Data accuracy is maintained through numerical validation against industry-standard reference software. This ensures that the statistical results provided by the API are reliable and defensible for quality engineering applications.

Users can expect consistent data patterns across endpoints, such as summary statistics in Descriptive Statistics and correlation coefficients in the Correlation endpoint. Results are structured to facilitate easy interpretation and integration into reports.

General FAQs

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