{"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."}]}}}
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"}}'
{"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}]}}}
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":[]}}'
{"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}]}}}
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":[]}}'
{"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}]}}}
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"}}'
注册后,每个开发者都会被分配一个个人 API 访问密钥,这是一个唯一的字母和数字组合,用于访问我们的 API 端点。要使用 统计质量分析 API 进行身份验证,只需在 Authorization 标头中包含您的 bearer token。
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统计质量分析API通过简单的HTTP接口将工程级统计数据引入任何应用程序。发送一张测量数据表和一个小配置对象,并接收干净、结构化的JSON结果。它是为制造、质量工程、实验室和数据团队而构建的,这些团队需要可辩护的统计结果用于报告、仪表板、审核和自动化管道。
计算描述性统计(均值、标准差、四分位数、置信区间)、正态性检验(安德森-达林、沙皮罗-维尔克、科尔莫哥洛夫-史密诺夫)、相关性(皮尔逊、斯皮尔曼、肯德尔带置信区间和p值)、和过程能力(Cp、Cpk、Pp、Ppk、Cpm带PPM)。每个端点的形状相同:一个数据数组加一个配置对象,并返回带标签的JSON表。结果经过高精度的数值验证,以符合行业标准参考软件。
每个端点返回结构化的JSON数据。例如,正态性检验端点提供了测试统计量和p值等统计信息,而描述性统计端点返回了平均值、标准差和四分位数等汇总指标
关键字段因端点而异 对于正态性检验 字段包括“统计量”和“P值” 在描述性统计中 字段包括“均值”“标准差”和“最小值” 每个端点的响应都针对其特定分析量身定制
响应数据被组织成带标签的表格。每个表格包含相关统计信息或解释的行。例如,相关性端点包含一个包含每对变量的相关值和p值的“成对相关表”
每个端点接受一个数据数组和一个配置对象。例如,正态性检验需要一个数值列和所选的检验方法(安德森-达林,沙皮罗-威尔克或科尔莫哥罗夫-斯米尔诺夫)作为参数
用户可以通过在配置对象中指定要分析的数值列和选择统计方法来自定义请求。例如,在相关性端点中,用户可以选择皮尔逊、斯皮尔曼或肯德尔方法
典型的用例包括制造业中的质量控制 审计的统计报告 和实验室中的数据分析 用户可以利用 API 来生成仪表板或自动化统计分析流程
数据准确性通过与行业标准参考软件进行数值验证来保持。这确保了API提供的统计结果在质量工程应用中是可靠和可辩护的
用户可以期待不同端点之间的数据模式一致,例如描述性统计中的汇总统计和相关性端点中的相关系数。结果被结构化以便于轻松解释和整合到报告中