Quality and laboratory
stat linearitystat linearitystat precisionstat precisionstat gage_rrstat gage_rrstat gage_biasstat gage_biasstat control_chartstat control_chartstat capabilitystat run_chartstat grubbsstat grubbsstat dixonstat cochranstat mandel_hkstat proficiencystat proficiencystat inverse_predictionstat inverse_predictionstat lod_blankstat recoverystat tolerance_intervalstat tolerance_intervalstat toststat anderson_darlingstat box_coxstat box_coxstat fleiss_kappastat oc_curvestat oc_curvestat sampling_planstat sampling_planstat sigma_levelstat nordteststat westgardstat probitstat probit
stat linearity <json_concentrations> <json_responses>
Description
Linearity of a calibration: slope and intercept with intervals, r and r2, residual sd, residuals, lack of fit test (replicated levels), Mandel test (quadratic against linear), LOD = 3.3 sd / slope and LOQ = 10 sd / slope (ICH Q2)
Linearity of a calibration: slope and intercept with intervals, r and r2, residual sd, residuals, lack of fit test (replicated levels), Mandel test (quadratic against linear), LOD = 3.3 sd / slope and LOQ = 10 sd / slope (ICH Q2)
Parameters
json_concentrations: The concentrations of the standards - String - required
json_responses: The responses - String - required
json_concentrations: The concentrations of the standards - String - required
json_responses: The responses - String - required
admin
stat linearity "[0, 1, 2, 4, 6, 8, 10, 0, 1, 2, 4, 6, 8, 10]" "[0.012, 0.462, 0.921, 1.809, 2.722, 3.588, 4.503, 0.018, 0.455, 0.898, 1.825, 2.701, 3.611, 4.488]"
mentdb
{"n": 14, "slope": 0.44839078498293516, "slope_se": 8.183024618508042E-4, "slope_lower": 0.4466078570805063, "slope_upper": 0.450173712885364, "intercept": 0.015197952218429567, "intercept_se": 0.004597915383467819, "intercept_lower": 0.005179955191205473, "intercept_upper": 0.02521594924565366, "intercept_p_value": 0.006277635750687294, "r": 0.9999800173725857, "r2": 0.9999600351444773, "residual_sd": 0.010588358655694291, "residuals": [-0.0031979522184295665, -0.0015887372013647094, 0.009020477815700145, 2.3890784982971702E-4, 0.016457337883959422, -0.014324232081910804, 0.003894197952218903, 0.002802047781570432, -0.008588737201364716, -0.013979522184299875, 0.01623890784982973, -0.004542662116040486, 0.008675767918089328, -0.011105802047781665], "lack_of_fit": {"f": 0.4242170417076589, "df1": 5, "df2": 7, "p_value": 0.8188791544438891}, "mandel_test": {"f": 1.3233589816699634, "p_value": 0.27438467305185954, "rule": "p < 0.05: the quadratic model fits better, the response is not linear"}, "lod": 0.07792663171059808, "loq": 0.23614130821393356, "lod_basis": "residual sd of the calibration"}
stat linearity <json_concentrations> <json_responses> <json_params>
Description
Linearity with parameters: blank_sd (sd of the blank for LOD and LOQ instead of the residual sd), level
Linearity with parameters: blank_sd (sd of the blank for LOD and LOQ instead of the residual sd), level
Parameters
json_concentrations: The concentrations - String - required
json_responses: The responses - String - required
json_params: The parameters - String - required
json_concentrations: The concentrations - String - required
json_responses: The responses - String - required
json_params: The parameters - String - required
admin
stat linearity "[0, 1, 2, 4, 6, 8, 10, 0, 1, 2, 4, 6, 8, 10]" "[0.012, 0.462, 0.921, 1.809, 2.722, 3.588, 4.503, 0.018, 0.455, 0.898, 1.825, 2.701, 3.611, 4.488]" "{\"blank_sd\": 0.004}"
mentdb
{"n": 14, "slope": 0.44839078498293516, "slope_se": 8.183024618508042E-4, "slope_lower": 0.4466078570805063, "slope_upper": 0.450173712885364, "intercept": 0.015197952218429567, "intercept_se": 0.004597915383467819, "intercept_lower": 0.005179955191205473, "intercept_upper": 0.02521594924565366, "intercept_p_value": 0.006277635750687294, "r": 0.9999800173725857, "r2": 0.9999600351444773, "residual_sd": 0.010588358655694291, "residuals": [-0.0031979522184295665, -0.0015887372013647094, 0.009020477815700145, 2.3890784982971702E-4, 0.016457337883959422, -0.014324232081910804, 0.003894197952218903, 0.002802047781570432, -0.008588737201364716, -0.013979522184299875, 0.01623890784982973, -0.004542662116040486, 0.008675767918089328, -0.011105802047781665], "lack_of_fit": {"f": 0.4242170417076589, "df1": 5, "df2": 7, "p_value": 0.8188791544438891}, "mandel_test": {"f": 1.3233589816699634, "p_value": 0.27438467305185954, "rule": "p < 0.05: the quadratic model fits better, the response is not linear"}, "lod": 0.029438606773558838, "loq": 0.08920789931381466, "lod_basis": "sd of the blank"}
Used in use cases (1 recipe)
Chemist › Validation of an analytical method: linearity, LOD/LOQ, precision, trueness
Chemist › Validation of an analytical method: linearity, LOD/LOQ, precision, trueness
stat precision <json_values> <json_groups>
Description
Precision (ISO 5725-2, one way ANOVA): repeatability sd, between-series sd, intermediate precision sd, RSD %, repeatability and reproducibility limits r = 2.8 sr and R = 2.8 sR
Precision (ISO 5725-2, one way ANOVA): repeatability sd, between-series sd, intermediate precision sd, RSD %, repeatability and reproducibility limits r = 2.8 sr and R = 2.8 sR
Parameters
json_values: The results - String - required
json_groups: The series (day, operator, laboratory) of each result - String - required
json_values: The results - String - required
json_groups: The series (day, operator, laboratory) of each result - String - required
admin
stat precision "[23.65, 24.0, 24.08, 25.63, 25.93, 26.12, 24.19, 25.15, 25.6, 25.33, 25.48, 25.67, 26.68, 26.53, 26.96]" "[\"day1\", \"day1\", \"day1\", \"day2\", \"day2\", \"day2\", \"day3\", \"day3\", \"day3\", \"day4\", \"day4\", \"day4\", \"day5\", \"day5\", \"day5\"]"
mentdb
{"n": 15, "groups": 5, "mean": 25.400000000000002, "repeatability_sd": 0.37648373138822366, "between_sd": 1.0260133202514166, "intermediate_sd": 1.0929059123883154, "repeatability_rsd_percent": 1.4822194149142662, "intermediate_rsd_percent": 4.302779182631163, "repeatability_limit": 1.0541544478870262, "reproducibility_limit": 3.0601365546872827, "anova_f": 23.281007478481754, "anova_p_value": 4.7288736254258446E-5}
stat precision <json_values> <json_groups> <json_params>
Description
Precision and trueness with parameters: reference (certified value: bias with its interval, recovery %, t test), level
Precision and trueness with parameters: reference (certified value: bias with its interval, recovery %, t test), level
Parameters
json_values: The results - String - required
json_groups: The series - String - required
json_params: The parameters - String - required
json_values: The results - String - required
json_groups: The series - String - required
json_params: The parameters - String - required
admin
stat precision "[23.65, 24.0, 24.08, 25.63, 25.93, 26.12, 24.19, 25.15, 25.6, 25.33, 25.48, 25.67, 26.68, 26.53, 26.96]" "[\"day1\", \"day1\", \"day1\", \"day2\", \"day2\", \"day2\", \"day3\", \"day3\", \"day3\", \"day4\", \"day4\", \"day4\", \"day5\", \"day5\", \"day5\"]" "{\"reference\": 25.2}"
mentdb
{"n": 15, "groups": 5, "mean": 25.400000000000002, "repeatability_sd": 0.37648373138822366, "between_sd": 1.0260133202514166, "intermediate_sd": 1.0929059123883154, "repeatability_rsd_percent": 1.4822194149142662, "intermediate_rsd_percent": 4.302779182631163, "repeatability_limit": 1.0541544478870262, "reproducibility_limit": 3.0601365546872827, "anova_f": 23.281007478481754, "anova_p_value": 4.7288736254258446E-5, "reference": 25.2, "bias": 0.20000000000000284, "bias_lower": -1.1022385903457166, "bias_upper": 1.5022385903457223, "recovery_percent": 100.7936507936508, "bias_p_value": 0.6917547969957272}
Used in use cases (1 recipe)
Chemist › Validation of an analytical method: linearity, LOD/LOQ, precision, trueness
Chemist › Validation of an analytical method: linearity, LOD/LOQ, precision, trueness
stat gage_rr <json_measures> <json_parts> <json_operators>
Description
Gage R&R crossed study (ANOVA method as AIAG and Minitab): ANOVA table, interaction pooled when p > 0.25, variance components, % contribution, % study variation, number of distinct categories (ndc), verdict (< 10 % acceptable, 10 to 30 % conditional)
Gage R&R crossed study (ANOVA method as AIAG and Minitab): ANOVA table, interaction pooled when p > 0.25, variance components, % contribution, % study variation, number of distinct categories (ndc), verdict (< 10 % acceptable, 10 to 30 % conditional)
Parameters
json_measures: The measures - String - required
json_parts: The part of each measure - String - required
json_operators: The operator of each measure - String - required
json_measures: The measures - String - required
json_parts: The part of each measure - String - required
json_operators: The operator of each measure - String - required
admin
stat gage_rr "[17.29, 17.27, 17.84, 17.79, 18.19, 18.08, 20.72, 20.68, 21.41, 20.84, 20.93, 20.86, 21.99, 22.0, 22.06, 22.12, 22.37, 22.46, 19.17, 19.0, 18.76, 19.26, 19.02, 19.27, 17.03, 17.32, 17.33, 17.53, 18.33, 17.66]" "[\"P1\", \"P1\", \"P1\", \"P1\", \"P1\", \"P1\", \"P2\", \"P2\", \"P2\", \"P2\", \"P2\", \"P2\", \"P3\", \"P3\", \"P3\", \"P3\", \"P3\", \"P3\", \"P4\", \"P4\", \"P4\", \"P4\", \"P4\", \"P4\", \"P5\", \"P5\", \"P5\", \"P5\", \"P5\", \"P5\"]" "[\"Op1\", \"Op1\", \"Op2\", \"Op2\", \"Op3\", \"Op3\", \"Op1\", \"Op1\", \"Op2\", \"Op2\", \"Op3\", \"Op3\", \"Op1\", \"Op1\", \"Op2\", \"Op2\", \"Op3\", \"Op3\", \"Op1\", \"Op1\", \"Op2\", \"Op2\", \"Op3\", \"Op3\", \"Op1\", \"Op1\", \"Op2\", \"Op2\", \"Op3\", \"Op3\"]"
mentdb
{"parts": 5, "operators": 3, "replicates": 2, "interaction_pooled": false, "anova": [{"source": "part", "ss": 97.31338666666663, "df": 4, "ms": 24.328346666666658, "f": 263.4458561940531, "p_value": 1.601623800251417E-8}, {"source": "operator", "ss": 1.1054599999999946, "df": 2, "ms": 0.5527299999999973, "f": 5.98538117239389, "p_value": 0.02575024549628302}, {"source": "part x operator", "ss": 0.7387733333333283, "df": 8, "ms": 0.09234666666666604, "f": 2.1769605531981626, "p_value": 0.0923533681606612}, {"source": "repeatability", "ss": 0.6362999999999999, "df": 15, "ms": 0.04241999999999999}], "components": [{"source": "total_gage_rr", "variance": 0.11342166666666614, "contribution_percent": 2.731239060976778, "sd": 0.3367813336078265, "study_var": 2.0206880016469593, "study_var_percent": 16.52646078559102}, {"source": "repeatability", "variance": 0.04241999999999999, "contribution_percent": 1.0214905526572122, "sd": 0.2059611613872868, "study_var": 1.2357669683237207, "study_var_percent": 10.106881579682291}, {"source": "reproducibility", "variance": 0.07100166666666614, "contribution_percent": 1.7097485083195656, "sd": 0.26646137931540126, "study_var": 1.5987682758924076, "study_var_percent": 13.075735192789603}, {"source": "operator", "variance": 0.046038333333333126, "contribution_percent": 1.1086214653485011, "sd": 0.21456545232943053, "study_var": 1.2873927139765833, "study_var_percent": 10.529109484417479}, {"source": "part_x_operator", "variance": 0.02496333333333302, "contribution_percent": 0.6011270429710646, "sd": 0.1579978902812725, "study_var": 0.947987341687635, "study_var_percent": 7.753238310351776}, {"source": "part_to_part", "variance": 4.039333333333332, "contribution_percent": 97.26876093902321, "sd": 2.0098092778503465, "study_var": 12.05885566710208, "study_var_percent": 98.62492633154318}, {"source": "total", "variance": 4.152754999999998, "contribution_percent": 100, "sd": 2.0378309547163127, "study_var": 12.226985728297876, "study_var_percent": 100}], "gage_rr_percent": 16.52646078559102, "ndc": 8, "verdict": "conditionally acceptable (10 - 30 %)"}
stat gage_rr <json_measures> <json_parts> <json_operators> <json_params>
Description
Gage R&R with parameters: tolerance (USL - LSL: % tolerance), alpha_interaction (0.25)
Gage R&R with parameters: tolerance (USL - LSL: % tolerance), alpha_interaction (0.25)
Parameters
json_measures: The measures - String - required
json_parts: The parts - String - required
json_operators: The operators - String - required
json_params: The parameters - String - required
json_measures: The measures - String - required
json_parts: The parts - String - required
json_operators: The operators - String - required
json_params: The parameters - String - required
admin
stat gage_rr "[17.29, 17.27, 17.84, 17.79, 18.19, 18.08, 20.72, 20.68, 21.41, 20.84, 20.93, 20.86, 21.99, 22.0, 22.06, 22.12, 22.37, 22.46, 19.17, 19.0, 18.76, 19.26, 19.02, 19.27, 17.03, 17.32, 17.33, 17.53, 18.33, 17.66]" "[\"P1\", \"P1\", \"P1\", \"P1\", \"P1\", \"P1\", \"P2\", \"P2\", \"P2\", \"P2\", \"P2\", \"P2\", \"P3\", \"P3\", \"P3\", \"P3\", \"P3\", \"P3\", \"P4\", \"P4\", \"P4\", \"P4\", \"P4\", \"P4\", \"P5\", \"P5\", \"P5\", \"P5\", \"P5\", \"P5\"]" "[\"Op1\", \"Op1\", \"Op2\", \"Op2\", \"Op3\", \"Op3\", \"Op1\", \"Op1\", \"Op2\", \"Op2\", \"Op3\", \"Op3\", \"Op1\", \"Op1\", \"Op2\", \"Op2\", \"Op3\", \"Op3\", \"Op1\", \"Op1\", \"Op2\", \"Op2\", \"Op3\", \"Op3\", \"Op1\", \"Op1\", \"Op2\", \"Op2\", \"Op3\", \"Op3\"]" "{\"tolerance\": 12}"
mentdb
{"parts": 5, "operators": 3, "replicates": 2, "interaction_pooled": false, "anova": [{"source": "part", "ss": 97.31338666666663, "df": 4, "ms": 24.328346666666658, "f": 263.4458561940531, "p_value": 1.601623800251417E-8}, {"source": "operator", "ss": 1.1054599999999946, "df": 2, "ms": 0.5527299999999973, "f": 5.98538117239389, "p_value": 0.02575024549628302}, {"source": "part x operator", "ss": 0.7387733333333283, "df": 8, "ms": 0.09234666666666604, "f": 2.1769605531981626, "p_value": 0.0923533681606612}, {"source": "repeatability", "ss": 0.6362999999999999, "df": 15, "ms": 0.04241999999999999}], "components": [{"source": "total_gage_rr", "variance": 0.11342166666666614, "contribution_percent": 2.731239060976778, "sd": 0.3367813336078265, "study_var": 2.0206880016469593, "study_var_percent": 16.52646078559102, "tolerance_percent": 16.839066680391326}, {"source": "repeatability", "variance": 0.04241999999999999, "contribution_percent": 1.0214905526572122, "sd": 0.2059611613872868, "study_var": 1.2357669683237207, "study_var_percent": 10.106881579682291, "tolerance_percent": 10.29805806936434}, {"source": "reproducibility", "variance": 0.07100166666666614, "contribution_percent": 1.7097485083195656, "sd": 0.26646137931540126, "study_var": 1.5987682758924076, "study_var_percent": 13.075735192789603, "tolerance_percent": 13.323068965770062}, {"source": "operator", "variance": 0.046038333333333126, "contribution_percent": 1.1086214653485011, "sd": 0.21456545232943053, "study_var": 1.2873927139765833, "study_var_percent": 10.529109484417479, "tolerance_percent": 10.728272616471527}, {"source": "part_x_operator", "variance": 0.02496333333333302, "contribution_percent": 0.6011270429710646, "sd": 0.1579978902812725, "study_var": 0.947987341687635, "study_var_percent": 7.753238310351776, "tolerance_percent": 7.899894514063625}, {"source": "part_to_part", "variance": 4.039333333333332, "contribution_percent": 97.26876093902321, "sd": 2.0098092778503465, "study_var": 12.05885566710208, "study_var_percent": 98.62492633154318, "tolerance_percent": 100.49046389251733}, {"source": "total", "variance": 4.152754999999998, "contribution_percent": 100, "sd": 2.0378309547163127, "study_var": 12.226985728297876, "study_var_percent": 100, "tolerance_percent": 101.89154773581565}], "gage_rr_percent": 16.52646078559102, "ndc": 8, "verdict": "conditionally acceptable (10 - 30 %)"}
Used in use cases (1 recipe)
Industrial quality engineer › Is the caliper good enough? Gage R&R study
Industrial quality engineer › Is the caliper good enough? Gage R&R study
stat gage_bias <json_references> <json_measures>
Description
Gage linearity and bias study (AIAG): regression of the bias on the reference value (slope = linearity), average bias with its t test
Gage linearity and bias study (AIAG): regression of the bias on the reference value (slope = linearity), average bias with its t test
Parameters
json_references: The reference values - String - required
json_measures: The measures - String - required
json_references: The reference values - String - required
json_measures: The measures - String - required
admin
stat gage_bias "[2, 2, 4, 4, 6, 6, 8, 8, 10, 10]" "[2.05, 1.98, 4.06, 4.01, 6.10, 6.04, 8.13, 8.09, 10.15, 10.11]"
mentdb
{"slope": 0.015250000000000031, "slope_p_value": 0.0020060457003978627, "intercept": -0.019500000000000267, "intercept_p_value": 0.4111005161737187, "r2": 0.7166795069337437, "average_bias": 0.07199999999999993, "bias_p_value": 0.002176309368357332, "rule": "slope p < 0.05: the bias changes with the size (linearity problem); bias p < 0.05: a constant bias"}
stat gage_bias <json_references> <json_measures> <json_params>
Description
Gage linearity and bias with parameters: process_variation (6 sd of the process: linearity and bias in %)
Gage linearity and bias with parameters: process_variation (6 sd of the process: linearity and bias in %)
Parameters
json_references: The reference values - String - required
json_measures: The measures - String - required
json_params: The parameters - String - required
json_references: The reference values - String - required
json_measures: The measures - String - required
json_params: The parameters - String - required
admin
stat gage_bias "[2, 2, 4, 4, 6, 6, 8, 8, 10, 10]" "[2.05, 1.98, 4.06, 4.01, 6.10, 6.04, 8.13, 8.09, 10.15, 10.11]" "{\"process_variation\": 12}"
mentdb
{"slope": 0.015250000000000031, "slope_p_value": 0.0020060457003978627, "intercept": -0.019500000000000267, "intercept_p_value": 0.4111005161737187, "r2": 0.7166795069337437, "average_bias": 0.07199999999999993, "bias_p_value": 0.002176309368357332, "linearity": 0.18300000000000038, "linearity_percent": 1.525000000000003, "bias_percent": 0.5999999999999993, "rule": "slope p < 0.05: the bias changes with the size (linearity problem); bias p < 0.05: a constant bias"}
stat control_chart <type> <json_data>
Description
Control chart: imr (individuals and moving range), xbar_r, xbar_s (subgroups), p, np, u (counts with sizes), c, ewma; center line, limits, out of control points and the Western Electric rules (beyond 3 sigma, 2 of 3 beyond 2, 4 of 5 beyond 1, 8 on one side, 6 in a trend)
Control chart: imr (individuals and moving range), xbar_r, xbar_s (subgroups), p, np, u (counts with sizes), c, ewma; center line, limits, out of control points and the Western Electric rules (beyond 3 sigma, 2 of 3 beyond 2, 4 of 5 beyond 1, 8 on one side, 6 in a trend)
Parameters
type: imr, xbar_r, xbar_s, p, np, c, u or ewma - String - required
json_data: The values, or the subgroups [[...], ...] - String - required
type: imr, xbar_r, xbar_s, p, np, c, u or ewma - String - required
json_data: The values, or the subgroups [[...], ...] - String - required
admin
stat control_chart "xbar_r" "[[10.42, 9.34, 9.86, 10.33], [9.95, 10.19, 10.05, 9.5], [10.28, 9.59, 10.94, 9.75], [10.34, 10.0, 10.48, 9.7], [10.5, 9.94, 10.63, 10.31], [10.07, 9.61, 10.38, 10.23], [10.82, 9.81, 10.28, 10.25], [10.14, 10.1, 10.09, 9.7], [9.49, 9.8, 10.3, 10.76], [10.49, 10.53, 10.39, 10.35]]"
mentdb
{"type": "xbar_r", "charts": [{"chart": "xbar", "center": 10.14225, "lcl": 9.540428724450987, "ucl": 10.744071275549015, "values": [9.987499999999999, 9.9225, 10.139999999999999, 10.129999999999999, 10.345, 10.072500000000002, 10.290000000000001, 10.0075, 10.0875, 10.44], "out_of_control": [], "western_electric": []}, {"chart": "range", "center": 0.8260000000000005, "lcl": 0, "ucl": 1.8852054449662652, "values": [1.08, 0.6899999999999995, 1.3499999999999996, 0.7800000000000011, 0.6900000000000013, 0.7700000000000014, 1.0099999999999998, 0.4400000000000013, 1.2699999999999996, 0.17999999999999972], "out_of_control": []}]}
Individuals with a shift at the end
admin
stat control_chart "imr" "[50.18, 50.77, 51.77, 49.67, 49.31, 51.96, 50.24, 50.97, 50.87, 48.86, 52.43, 51.78, 50.18, 50.75, 50.13, 49.12, 49.89, 50.37, 51.12, 50.71, 51.56, 52.85, 50.74, 52.29, 52.61, 51.21]"
mentdb
{"type": "imr", "charts": [{"chart": "individuals", "center": 50.85923076923076, "lcl": 47.751783960720125, "ucl": 53.9666775777414, "values": [50.18, 50.77, 51.77, 49.67, 49.31, 51.96, 50.24, 50.97, 50.87, 48.86, 52.43, 51.78, 50.18, 50.75, 50.13, 49.12, 49.89, 50.37, 51.12, 50.71, 51.56, 52.85, 50.74, 52.29, 52.61, 51.21], "out_of_control": [], "western_electric": []}, {"chart": "moving_range", "center": 1.1683999999999999, "lcl": 0, "ucl": 3.8171627999999993, "values": [0.5900000000000034, 1, 2.1000000000000014, 0.35999999999999943, 2.6499999999999986, 1.7199999999999989, 0.7299999999999969, 0.10000000000000142, 2.009999999999998, 3.5700000000000003, 0.6499999999999986, 1.6000000000000014, 0.5700000000000003, 0.6199999999999974, 1.0100000000000051, 0.7700000000000031, 0.4799999999999969, 0.75, 0.4099999999999966, 0.8500000000000014, 1.2899999999999991, 2.1099999999999994, 1.5499999999999972, 0.3200000000000003, 1.3999999999999986], "out_of_control": []}]}
stat control_chart <type> <json_data> <json_params>
Description
Control chart with parameters: sizes (p, np, u), lambda (EWMA, 0.2), L (EWMA width, 3), target and sigma (EWMA, else estimated)
Control chart with parameters: sizes (p, np, u), lambda (EWMA, 0.2), L (EWMA width, 3), target and sigma (EWMA, else estimated)
Parameters
type: The type - String - required
json_data: The data - String - required
json_params: The parameters - String - required
type: The type - String - required
json_data: The data - String - required
json_params: The parameters - String - required
admin
stat control_chart "p" "[3, 5, 2, 8, 4, 3, 6, 2, 12, 4]" "{\"sizes\": [100, 100, 100, 100, 100, 100, 100, 100, 100, 100]}"
mentdb
{"type": "p", "charts": [{"chart": "p", "center": 0.049, "values": [0.03, 0.05, 0.02, 0.08, 0.04, 0.03, 0.06, 0.02, 0.12, 0.04], "lcl": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], "ucl": [0.11376040456945896, 0.11376040456945896, 0.11376040456945896, 0.11376040456945896, 0.11376040456945896, 0.11376040456945896, 0.11376040456945896, 0.11376040456945896, 0.11376040456945896, 0.11376040456945896], "out_of_control": [8]}]}
EWMA: detects the small shift
admin
stat control_chart "ewma" "[50.18, 50.77, 51.77, 49.67, 49.31, 51.96, 50.24, 50.97, 50.87, 48.86, 52.43, 51.78, 50.18, 50.75, 50.13, 49.12, 49.89, 50.37, 51.12, 50.71, 51.56, 52.85, 50.74, 52.29, 52.61, 51.21]" "{\"lambda\": 0.2, \"target\": 50}"
mentdb
{"type": "ewma", "charts": [{"chart": "ewma", "center": 50, "values": [50.036, 50.18280000000001, 50.500240000000005, 50.334192, 50.12935360000001, 50.49548288000001, 50.44438630400001, 50.54950904320002, 50.613607234560014, 50.26288578764801, 50.69630863011841, 50.913046904094735, 50.76643752327579, 50.763150018620635, 50.63652001489651, 50.33321601191721, 50.24457280953377, 50.26965824762702, 50.439726598101615, 50.4937812784813, 50.70702502278505, 51.13562001822804, 51.056496014582436, 51.30319681166595, 51.56455744933276, 51.49364595946621], "lcl": [49.378510638297875, 49.20410528095569, 49.11024947027686, 49.05506144644205, 49.02137305191966, 49.00040814481375, 48.98721833154429, 48.97886625763161, 48.97355659513882, 48.97017277795057, 48.96801295775368, 48.96663304187618, 48.965750861929365, 48.965186661444285, 48.964825734512914, 48.96459480730391, 48.96444704091552, 48.964352481491495, 48.96429196799082, 48.96425324120582, 48.964228456823314, 48.96421259512974, 48.964202443773324, 48.96419594695743, 48.96419178901664, 48.9641891279433], "ucl": [50.621489361702125, 50.79589471904431, 50.88975052972314, 50.94493855355795, 50.97862694808034, 50.99959185518625, 51.01278166845571, 51.02113374236839, 51.02644340486118, 51.02982722204943, 51.03198704224632, 51.03336695812382, 51.034249138070635, 51.034813338555715, 51.035174265487086, 51.03540519269609, 51.03555295908448, 51.035647518508505, 51.03570803200918, 51.03574675879418, 51.035771543176686, 51.03578740487026, 51.035797556226676, 51.03580405304257, 51.03580821098336, 51.0358108720567], "out_of_control": [21, 22, 23, 24, 25]}]}
Used in use cases (1 recipe)
Industrial quality engineer › Filling line: control chart, small shift and capability
Industrial quality engineer › Filling line: control chart, small shift and capability
stat capability <json_values> <json_params>
Description
Process capability: Cp, Cpk (with intervals), Pp, Ppk, Cpm, expected ppm out of the specifications (within and overall) and observed; params: lsl, usl, target, subgroup (size: pooled within sd), level
Process capability: Cp, Cpk (with intervals), Pp, Ppk, Cpm, expected ppm out of the specifications (within and overall) and observed; params: lsl, usl, target, subgroup (size: pooled within sd), level
Parameters
json_values: The values - String - required
json_params: The parameters - String - required
json_values: The values - String - required
json_params: The parameters - String - required
admin
stat capability "[50.18, 50.77, 51.77, 49.67, 49.31, 51.96, 50.24, 50.97, 50.87, 48.86, 52.43, 51.78, 50.18, 50.75, 50.13, 49.12, 49.89, 50.37, 51.12, 50.71]" "{\"lsl\": 46, \"usl\": 54}"
mentdb
{"n": 20, "mean": 50.55400000000001, "sd_within": 1.0120380739081745, "sd_overall": 0.958850188616828, "cp": 1.3174734900875982, "cp_lower": 0.902025931158042, "cp_upper": 1.7323999092040523, "pp": 1.3905543839509582, "cpm": 1.2272560285268594, "cpk": 1.1350034117104628, "cpk_lower": 0.7456832322966869, "cpk_upper": 1.5243235911242388, "ppk": 1.1979626017737472, "ppm_within": 334.2082501461236, "ppm_overall": 163.90999276069363, "ppm_observed": 0, "verdict": "barely capable (1 <= Cpk < 1.33)"}
Used in use cases (1 recipe)
Industrial quality engineer › Filling line: control chart, small shift and capability
Industrial quality engineer › Filling line: control chart, small shift and capability
stat run_chart <json_values>
Description
Run chart test of randomness: number of runs about the median against the expected number (clustering or mixtures), longest run on one side and longest trend
Run chart test of randomness: number of runs about the median against the expected number (clustering or mixtures), longest run on one side and longest trend
Parameters
json_values: The values in time order - String - required
json_values: The values in time order - String - required
admin
stat run_chart "[50.18, 50.77, 51.77, 49.67, 49.31, 51.96, 50.24, 50.97, 50.87, 48.86, 52.43, 51.78, 50.18, 50.75, 50.13, 49.12, 49.89, 50.37, 51.12, 50.71, 51.56, 52.85, 50.74, 52.29, 52.61, 51.21]"
mentdb
{"median": 50.760000000000005, "runs": 14, "expected_runs": 14, "p_clustering": 0.5, "p_mixtures": 0.5, "longest_run_one_side": 6, "longest_trend": 4, "rule": "p < 0.05: clustering (too few runs) or mixtures (too many); a run of 8 on one side or a trend of 6 is a signal"}
stat grubbs <json_values>
Description
Grubbs test of the most extreme value (normal data): G, critical value, p-value
Grubbs test of the most extreme value (normal data): G, critical value, p-value
Parameters
json_values: The values - String - required
json_values: The values - String - required
admin
stat grubbs "[2.1, 2.3, 2.2, 2.4, 2.2, 2.3, 2.1, 2.2, 2.3, 3.4]"
mentdb
{"alpha": 0.05, "tests": [{"n": 10, "value": 3.4, "g": 2.757435090054175, "critical": 2.2899540845041852, "p_value": 3.9601645098752414E-5, "outlier": true}]}
stat grubbs <json_values> <json_params>
Description
Grubbs test with parameters: alpha (0.05), iterative (remove the outlier and test again)
Grubbs test with parameters: alpha (0.05), iterative (remove the outlier and test again)
Parameters
json_values: The values - String - required
json_params: The parameters - String - required
json_values: The values - String - required
json_params: The parameters - String - required
admin
stat grubbs "[2.1, 2.3, 2.2, 2.4, 2.2, 2.3, 2.1, 2.2, 2.3, 3.4, 3.2]" "{\"iterative\": true}"
mentdb
{"alpha": 0.05, "tests": [{"n": 11, "value": 3.4, "g": 2.196148560040348, "critical": 2.3547300515654173, "p_value": 0.12123203080857281, "outlier": false}]}
stat dixon <json_values> <level>
Description
Dixon Q test of the most extreme value (3 to 10 values)
Dixon Q test of the most extreme value (3 to 10 values)
Parameters
json_values: The values - String - required
level: 0.90, 0.95 or 0.99 - Number - required
json_values: The values - String - required
level: 0.90, 0.95 or 0.99 - Number - required
admin
stat dixon "[10.2, 10.4, 10.3, 10.5, 11.6]" 0.95
mentdb
{"value": 11.6, "q": 0.7857142857142853, "critical": 0.71, "outlier": true, "level": 0.95}
stat cochran <json_values> <json_groups>
Description
Cochran test of the largest variance (ISO 5725: one series too dispersed), critical values at 5 % (straggler) and 1 % (outlier)
Cochran test of the largest variance (ISO 5725: one series too dispersed), critical values at 5 % (straggler) and 1 % (outlier)
Parameters
json_values: The results - String - required
json_groups: The series (same number of replicates) - String - required
json_values: The results - String - required
json_groups: The series (same number of replicates) - String - required
admin
stat cochran "[1.0, 1.2, 2.0, 2.1, 3.0, 3.9, 4.0, 4.05, 5.0, 5.1]" "[\"a\", \"a\", \"b\", \"b\", \"c\", \"c\", \"d\", \"d\", \"e\", \"e\"]"
mentdb
{"group": "c", "c": 0.9283667621776506, "critical_5": 0.8412552870530715, "critical_1": 0.9278688494725468, "verdict": "outlier (> 1 % critical value)"}
stat mandel_hk <json_values> <json_groups>
Description
Mandel h (between laboratories) and k (within laboratory) statistics of an interlaboratory study (ISO 5725-2), with the 5 % and 1 % critical values
Mandel h (between laboratories) and k (within laboratory) statistics of an interlaboratory study (ISO 5725-2), with the 5 % and 1 % critical values
Parameters
json_values: The results - String - required
json_groups: The laboratory of each result - String - required
json_values: The results - String - required
json_groups: The laboratory of each result - String - required
admin
stat mandel_hk "[10.1, 10.3, 10.2, 10.4, 9.9, 10.0, 10.6, 10.8, 10.2, 10.1, 11.0, 11.2]" "[\"L1\", \"L1\", \"L2\", \"L2\", \"L3\", \"L3\", \"L4\", \"L4\", \"L5\", \"L5\", \"L6\", \"L6\"]"
mentdb
{"groups": [{"group": "L1", "mean": 10.2, "sd": 0.14142135623731025, "h": -0.4727194592470678, "h_verdict": "correct", "k": 1.1547005383792548, "k_verdict": "correct"}, {"group": "L2", "mean": 10.3, "sd": 0.14142135623731025, "h": -0.23635972962353183, "h_verdict": "correct", "k": 1.1547005383792548, "k_verdict": "correct"}, {"group": "L3", "mean": 9.95, "sd": 0.0707106781186545, "h": -1.0636187833058994, "h_verdict": "correct", "k": 0.5773502691896223, "k_verdict": "correct"}, {"group": "L4", "mean": 10.7, "sd": 0.14142135623731025, "h": 0.7090791888705955, "h_verdict": "correct", "k": 1.1547005383792548, "k_verdict": "correct"}, {"group": "L5", "mean": 10.149999999999999, "sd": 0.0707106781186545, "h": -0.5908993240588358, "h_verdict": "correct", "k": 0.5773502691896223, "k_verdict": "correct"}, {"group": "L6", "mean": 11.1, "sd": 0.141421356237309, "h": 1.654518107364727, "h_verdict": "correct", "k": 1.1547005383792446, "k_verdict": "correct"}], "h_critical_5": 1.6562660739286597, "h_critical_1": 1.8722260447715955, "k_critical_5": 1.8481209893216928, "k_critical_1": 2.142143397361569}
stat proficiency <json_results>
Description
Proficiency testing scores (ISO 13528): robust assigned value and sd by the algorithm A, z scores and verdicts (|z| <= 2 satisfactory, >= 3 unsatisfactory)
Proficiency testing scores (ISO 13528): robust assigned value and sd by the algorithm A, z scores and verdicts (|z| <= 2 satisfactory, >= 3 unsatisfactory)
Parameters
json_results: The results of the participants - String - required
json_results: The results of the participants - String - required
admin
stat proficiency "[10.2, 9.8, 10.5, 10.1, 9.9, 12.4, 10.0, 10.3, 9.7, 10.2]"
mentdb
{"n": 10, "assigned": 10.134199161007286, "sigma_pt": 0.3385282993784054, "u_assigned": 0.13381505980739053, "robust_mean": 10.134199161007286, "robust_sd": 0.3385282993784054, "check": "u(assigned) > 0.3 sigma_pt: use zeta or z' scores", "scores": [{"result": 10.2, "z": 0.19437322998855425, "z_verdict": "satisfactory"}, {"result": 9.8, "z": -0.9872118863354444, "z_verdict": "satisfactory"}, {"result": 10.5, "z": 1.0805620672315586, "z_verdict": "satisfactory"}, {"result": 10.1, "z": -0.1010230490924454, "z_verdict": "satisfactory"}, {"result": 9.9, "z": -0.6918156072544447, "z_verdict": "satisfactory"}, {"result": 12.4, "z": 6.693091369770573, "z_verdict": "unsatisfactory"}, {"result": 10, "z": -0.39641932817344505, "z_verdict": "satisfactory"}, {"result": 10.3, "z": 0.4897695090695592, "z_verdict": "satisfactory"}, {"result": 9.7, "z": -1.2826081654164494, "z_verdict": "satisfactory"}, {"result": 10.2, "z": 0.19437322998855425, "z_verdict": "satisfactory"}]}
stat proficiency <json_results> <json_params>
Description
Proficiency scores with parameters: assigned, sigma_pt, u_assigned, uncertainties (zeta scores), expanded and U_assigned (En numbers)
Proficiency scores with parameters: assigned, sigma_pt, u_assigned, uncertainties (zeta scores), expanded and U_assigned (En numbers)
Parameters
json_results: The results - String - required
json_params: The parameters - String - required
json_results: The results - String - required
json_params: The parameters - String - required
admin
stat proficiency "[10.2, 9.8, 10.5, 12.4]" "{\"assigned\": 10.1, \"sigma_pt\": 0.4, \"expanded\": [0.4, 0.5, 0.4, 0.6], \"U_assigned\": 0.1}"
mentdb
{"n": 4, "assigned": 10.1, "sigma_pt": 0.4, "u_assigned": 0, "robust_mean": 10.725, "robust_sd": 1.3073828245774075, "check": "u(assigned) <= 0.3 sigma_pt: negligible", "scores": [{"result": 10.2, "z": 0.2499999999999991, "z_verdict": "satisfactory", "en": 0.24253562503633208, "en_verdict": "satisfactory"}, {"result": 9.8, "z": -0.7499999999999973, "z_verdict": "satisfactory", "en": -0.5883484054145499, "en_verdict": "satisfactory"}, {"result": 10.5, "z": 1.0000000000000009, "z_verdict": "satisfactory", "en": 0.9701425001453325, "en_verdict": "satisfactory"}, {"result": 12.4, "z": 5.750000000000002, "z_verdict": "unsatisfactory", "en": 3.781176708023219, "en_verdict": "unsatisfactory"}]}
stat inverse_prediction <json_concentrations> <json_responses> <json_sample>
Description
Concentration of an unknown sample from a calibration line (inverse prediction) with its standard error and interval, from the replicated responses of the sample
Concentration of an unknown sample from a calibration line (inverse prediction) with its standard error and interval, from the replicated responses of the sample
Parameters
json_concentrations: The concentrations of the standards - String - required
json_responses: Their responses - String - required
json_sample: The responses of the unknown sample - String - required
json_concentrations: The concentrations of the standards - String - required
json_responses: Their responses - String - required
json_sample: The responses of the unknown sample - String - required
admin
stat inverse_prediction "[0, 2, 4, 6, 8, 10]" "[0.02, 0.92, 1.83, 2.71, 3.62, 4.49]" "[2.05, 2.08, 2.02]"
mentdb
{"concentration": 4.519629747845515, "se": 0.018368111652219882, "lower": 4.468631694160277, "upper": 4.570627801530754, "replicates": 3, "slope": 0.44757142857142856, "intercept": 0.027142857142857135, "residual_sd": 0.011588171308956351, "in_range": true}
stat inverse_prediction <json_concentrations> <json_responses> <json_sample> <json_params>
Description
Inverse prediction with parameters: level
Inverse prediction with parameters: level
Parameters
json_concentrations: The concentrations - String - required
json_responses: The responses - String - required
json_sample: The sample responses - String - required
json_params: The parameters - String - required
json_concentrations: The concentrations - String - required
json_responses: The responses - String - required
json_sample: The sample responses - String - required
json_params: The parameters - String - required
admin
stat inverse_prediction "[0, 2, 4, 6, 8, 10]" "[0.02, 0.92, 1.83, 2.71, 3.62, 4.49]" "[3.9]" "{\"level\": 0.99}"
mentdb
{"concentration": 8.65304819661666, "se": 0.030164175171573306, "lower": 8.514169472410723, "upper": 8.791926920822599, "replicates": 1, "slope": 0.44757142857142856, "intercept": 0.027142857142857135, "residual_sd": 0.011588171308956351, "in_range": true}
stat lod_blank <json_blanks> <json_low>
Description
Limit of blank and limit of detection (CLSI EP17): LoB = mean(blank) + 1.645 sd(blank), LoD = LoB + 1.645 sd(low level sample)
Limit of blank and limit of detection (CLSI EP17): LoB = mean(blank) + 1.645 sd(blank), LoD = LoB + 1.645 sd(low level sample)
Parameters
json_blanks: The measures of blank samples - String - required
json_low: The measures of a low level sample - String - required
json_blanks: The measures of blank samples - String - required
json_low: The measures of a low level sample - String - required
admin
stat lod_blank "[0.01, 0.03, 0.02, 0.00, 0.02, 0.04, 0.01, 0.02]" "[0.12, 0.15, 0.10, 0.14, 0.11, 0.13, 0.16, 0.12]"
mentdb
{"blank_mean": 0.01875, "blank_sd": 0.01246423454758225, "low_sd": 0.020310096011589902, "lob": 0.03925184140276451, "lod": 0.07265897649116079, "rule": "LoB = mean(blank) + 1.645 sd(blank), LoD = LoB + 1.645 sd(low): 5 % false positives and 5 % false negatives"}
stat recovery <json_spiked> <json_unspiked> <added>
Description
Spike recovery: recovery % of each spiked sample, mean with its interval, test against 100 %
Spike recovery: recovery % of each spiked sample, mean with its interval, test against 100 %
Parameters
json_spiked: The results of the spiked samples - String - required
json_unspiked: The results of the same samples unspiked - String - required
added: The added quantity - Number - required
json_spiked: The results of the spiked samples - String - required
json_unspiked: The results of the same samples unspiked - String - required
added: The added quantity - Number - required
admin
stat recovery "[15.1, 14.8, 15.3, 14.9]" "[5.1, 4.9, 5.2, 5.0]" 10
mentdb
{"recoveries_percent": [100, 99, 101.00000000000001, 99], "mean_percent": 99.75, "sd_percent": 0.9574271077563443, "lower": 98.22651981937523, "upper": 101.27348018062477, "p_value_100": 0.6376180914006038}
stat tolerance_interval <json_values>
Description
Normal tolerance interval: the interval that contains 95 % of the population with 95 % confidence (k factor of Howe, exact one-sided factor)
Normal tolerance interval: the interval that contains 95 % of the population with 95 % confidence (k factor of Howe, exact one-sided factor)
Parameters
json_values: The values - String - required
json_values: The values - String - required
admin
stat tolerance_interval "[10.1, 9.8, 10.3, 10.0, 9.7, 10.2, 10.4, 9.9, 10.1, 10.0]"
mentdb
{"n": 10, "mean": 10.049999999999999, "sd": 0.21730674684008838, "k": 3.3819134904858794, "coverage": 0.95, "confidence": 0.95, "lower": 9.315087381287904, "upper": 10.784912618712093, "method": "Howe"}
stat tolerance_interval <json_values> <json_params>
Description
Tolerance interval with parameters: coverage (0.95), confidence (0.95), sides (2 or 1)
Tolerance interval with parameters: coverage (0.95), confidence (0.95), sides (2 or 1)
Parameters
json_values: The values - String - required
json_params: The parameters - String - required
json_values: The values - String - required
json_params: The parameters - String - required
admin
stat tolerance_interval "[10.1, 9.8, 10.3, 10.0, 9.7, 10.2, 10.4, 9.9, 10.1, 10.0]" "{\"coverage\": 0.99, \"sides\": 1}"
mentdb
{"n": 10, "mean": 10.049999999999999, "sd": 0.21730674684008838, "k": 3.9811178432976266, "coverage": 0.99, "confidence": 0.95, "lower_bound": 9.184876232685962, "upper_bound": 10.915123767314036, "method": "exact (non-central t)"}
stat tost <json_x> <json_y> <json_params>
Description
Equivalence test of two means (two one-sided tests, Welch or paired): equivalent when the 90 % interval of the difference is inside [low, high]; params: low, high, paired, alpha
Equivalence test of two means (two one-sided tests, Welch or paired): equivalent when the 90 % interval of the difference is inside [low, high]; params: low, high, paired, alpha
Parameters
json_x: The first sample - String - required
json_y: The second sample - String - required
json_params: The parameters - String - required
json_x: The first sample - String - required
json_y: The second sample - String - required
json_params: The parameters - String - required
admin
stat tost "[5.1, 5.3, 4.9, 5.2, 5.0, 5.1, 4.8, 5.2]" "[5.0, 5.2, 5.1, 4.9, 5.1, 5.0, 5.0, 5.1]" "{\"low\": -0.3, \"high\": 0.3}"
mentdb
{"difference": 0.025000000000000355, "se": 0.06748015581318277, "df": 10.935135135135127, "ci_lower": -0.09625240256391243, "ci_upper": 0.14625240256391314, "ci_level": 0.9, "p_lower": 2.7411119202658263E-4, "p_upper": 9.285708873769517E-4, "p_value": 9.285708873769517E-4, "equivalent": true, "method": "Welch t"}
stat anderson_darling <json_values>
Description
Anderson-Darling normality test (as Minitab): A2, adjusted A2, p-value
Anderson-Darling normality test (as Minitab): A2, adjusted A2, p-value
Parameters
json_values: The values - String - required
json_values: The values - String - required
admin
stat anderson_darling "[4.2, 5.1, 4.8, 5.5, 4.9, 5.0, 5.3, 4.7, 5.2, 4.6, 5.4, 4.95]"
mentdb
{"n": 12, "a2": 0.1422658043832925, "a2_adjusted": 0.15338032035073723, "p_value": 0.9586829316390414, "verdict": "normality not rejected"}
stat box_cox <json_values>
Description
Box-Cox transformation: lambda by maximum likelihood with its 95 % interval, suggested transformation (log, square root, none), transformed values
Box-Cox transformation: lambda by maximum likelihood with its 95 % interval, suggested transformation (log, square root, none), transformed values
Parameters
json_values: The values (> 0) - String - required
json_values: The values (> 0) - String - required
admin
stat box_cox "[1.2, 2.5, 0.8, 4.1, 1.9, 7.3, 3.2, 1.1, 2.2, 5.6, 0.9, 1.6]"
mentdb
{"lambda": -0.24, "lambda_lower": -1.19, "lambda_upper": 0.6499999999999999, "suggestion": "log (lambda = 0 in the interval)", "used_lambda": -0.24, "values": [0.17839016940091323, 0.8225366334746927, -0.22922682576034248, 1.1969115950356293, 0.5948604068905591, 1.5808884538927093, 1.0149093950745, 0.09422836053019079, 0.7183486210600156, 1.4110193596481164, -0.10670391565356745, 0.4444644583091582]}
stat box_cox <json_values> <json_params>
Description
Box-Cox with parameters: lambda (fixed), return_values
Box-Cox with parameters: lambda (fixed), return_values
Parameters
json_values: The values - String - required
json_params: The parameters - String - required
json_values: The values - String - required
json_params: The parameters - String - required
admin
stat box_cox "[1.2, 2.5, 0.8, 4.1, 1.9, 7.3, 3.2, 1.1]" "{\"lambda\": 0}"
mentdb
{"lambda": -0.18999999999999995, "lambda_lower": -1.35, "lambda_upper": 0.8700000000000001, "suggestion": "log (lambda = 0 in the interval)", "used_lambda": 0, "values": [0.1823215567939546, 0.9162907318741551, -0.2231435513142097, 1.410986973710262, 0.6418538861723947, 1.9878743481543455, 1.1631508098056809, 0.09531017980432493]}
stat fleiss_kappa <json_counts>
Description
Fleiss kappa: agreement of several raters on categories (attribute agreement), standard error, z test, Landis and Koch scale
Fleiss kappa: agreement of several raters on categories (attribute agreement), standard error, z test, Landis and Koch scale
Parameters
json_counts: One row per subject: the number of raters who chose each category - String - required
json_counts: One row per subject: the number of raters who chose each category - String - required
admin
stat fleiss_kappa "[[0,0,0,0,14],[0,2,6,4,2],[0,0,3,5,6],[0,3,9,2,0],[2,2,8,1,1],[7,7,0,0,0],[3,2,6,3,0],[2,5,3,2,2],[6,5,2,1,0],[0,2,2,3,7]]"
mentdb
{"subjects": 10, "raters": 14, "kappa": 0.20993070442195527, "se": 0.016965069224393135, "z": 12.374291059190464, "p_value": 0, "observed_agreement": 0.37802197802197807, "expected_agreement": 0.21275510204081632, "agreement": "fair (Landis and Koch)"}
stat oc_curve <n> <c>
Description
Operating characteristic curve of a single sampling plan (n, c): probability of acceptance against the proportion of defectives, indifference quality
Operating characteristic curve of a single sampling plan (n, c): probability of acceptance against the proportion of defectives, indifference quality
Parameters
n: The sample size - Number - required
c: The acceptance number - Number - required
n: The sample size - Number - required
c: The acceptance number - Number - required
admin
stat oc_curve 50 2
mentdb
{"n": 50, "c": 2, "curve": [[0, 1], [0.009, 0.9895716738680108], [0.018, 0.9387985321284761], [0.027000000000000003, 0.8475828772972056], [0.036, 0.7316454706927559], [0.045, 0.6078288875041606], [0.054000000000000006, 0.48886267643018044], [0.063, 0.38246992941502556], [0.072, 0.29218199174851345], [0.08099999999999999, 0.21859682225987243], [0.09, 0.16054049072451093], [0.099, 0.11595322533606667], [0.10800000000000001, 0.08248689230394535], [0.11699999999999999, 0.057864531452744394], [0.126, 0.040067194153937336], [0.13499999999999998, 0.027406924675100197], [0.144, 0.0185314795838597], [0.153, 0.01239285018731029], [0.16199999999999998, 0.008200398743373971], [0.17099999999999999, 0.005371038835818465], [0.18, 0.0034831258110514796]], "p50": 0.0531222684792865}
stat oc_curve <n> <c> <json_params>
Description
OC curve with parameters: aql (producer risk), ltpd (consumer risk)
OC curve with parameters: aql (producer risk), ltpd (consumer risk)
Parameters
n: The sample size - Number - required
c: The acceptance number - Number - required
json_params: The parameters - String - required
n: The sample size - Number - required
c: The acceptance number - Number - required
json_params: The parameters - String - required
admin
stat oc_curve 50 2 "{\"aql\": 0.01, \"ltpd\": 0.1}"
mentdb
{"n": 50, "c": 2, "curve": [[0, 1], [0.009, 0.9895716738680108], [0.018, 0.9387985321284761], [0.027000000000000003, 0.8475828772972056], [0.036, 0.7316454706927559], [0.045, 0.6078288875041606], [0.054000000000000006, 0.48886267643018044], [0.063, 0.38246992941502556], [0.072, 0.29218199174851345], [0.08099999999999999, 0.21859682225987243], [0.09, 0.16054049072451093], [0.099, 0.11595322533606667], [0.10800000000000001, 0.08248689230394535], [0.11699999999999999, 0.057864531452744394], [0.126, 0.040067194153937336], [0.13499999999999998, 0.027406924675100197], [0.144, 0.0185314795838597], [0.153, 0.01239285018731029], [0.16199999999999998, 0.008200398743373971], [0.17099999999999999, 0.005371038835818465], [0.18, 0.0034831258110514796]], "producer_risk": 0.013817270830600359, "consumer_risk": 0.11172875634634727, "p50": 0.0531222684792865}
stat sampling_plan <aql> <ltpd>
Description
Smallest single sampling plan (n, c) with a producer risk <= 5 % at the AQL and a consumer risk <= 10 % at the LTPD
Smallest single sampling plan (n, c) with a producer risk <= 5 % at the AQL and a consumer risk <= 10 % at the LTPD
Parameters
aql: The acceptable quality level (proportion) - Number - required
ltpd: The lot tolerance percent defective (proportion) - Number - required
aql: The acceptable quality level (proportion) - Number - required
ltpd: The lot tolerance percent defective (proportion) - Number - required
admin
stat sampling_plan 0.01 0.05
mentdb
{"n": 132, "c": 3, "producer_risk": 0.04425250582596618, "consumer_risk": 0.09922830442578268}
stat sampling_plan <aql> <ltpd> <json_params>
Description
Sampling plan with parameters: alpha (producer risk), beta (consumer risk)
Sampling plan with parameters: alpha (producer risk), beta (consumer risk)
Parameters
aql: The AQL - Number - required
ltpd: The LTPD - Number - required
json_params: The parameters - String - required
aql: The AQL - Number - required
ltpd: The LTPD - Number - required
json_params: The parameters - String - required
admin
stat sampling_plan 0.02 0.08 "{\"alpha\": 0.05, \"beta\": 0.05}"
mentdb
{"n": 129, "c": 5, "producer_risk": 0.04581935737919374, "consumer_risk": 0.04906756555351954}
stat sigma_level <defects> <units> <opportunities>
Description
Six sigma: DPMO, yield and sigma level (long term shift of 1.5 sigma) from the defects, the units and the opportunities per unit
Six sigma: DPMO, yield and sigma level (long term shift of 1.5 sigma) from the defects, the units and the opportunities per unit
Parameters
defects: The number of defects - Number - required
units: The number of units - Number - required
opportunities: The opportunities per unit - Number - required
defects: The number of defects - Number - required
units: The number of units - Number - required
opportunities: The opportunities per unit - Number - required
admin
stat sigma_level 34 10000 100
mentdb
{"dpmo": 34, "yield_percent": 99.9966, "sigma_level": 5.483177248924973, "convention": "long term shift of 1.5 sigma"}
stat nordtest <json_params>
Description
Measurement uncertainty from the validation data (Nordtest TR 537): u_c = sqrt(u_Rw^2 + u_bias^2), u_bias from the RMS of the biases on reference materials and u(Cref); expanded U; params: u_rw, bias (list), u_cref, value, k
Measurement uncertainty from the validation data (Nordtest TR 537): u_c = sqrt(u_Rw^2 + u_bias^2), u_bias from the RMS of the biases on reference materials and u(Cref); expanded U; params: u_rw, bias (list), u_cref, value, k
Parameters
json_params: The parameters - String - required
json_params: The parameters - String - required
admin
stat nordtest "{\"u_rw\": 0.8, \"bias\": [0.5, -0.3, 0.9, 0.4], \"u_cref\": 0.2, \"value\": 25.3}"
mentdb
{"u_rw": 0.8, "rms_bias": 0.5722761571129799, "u_cref": 0.2, "u_bias": 0.6062177826491071, "u_c": 1.0037429949942367, "k": 2, "U": 2.0074859899884734, "share_precision_percent": 63.52357320099254, "share_bias_percent": 36.47642679900744, "value": 25.3, "lower": 23.292514010011526, "upper": 27.307485989988475}
stat westgard <json_values> <mean> <sd>
Description
Westgard rules of the internal quality control (1_2s warning, 1_3s, 2_2s, R_4s, 4_1s, 10_x): violations of each control value and run rejection
Westgard rules of the internal quality control (1_2s warning, 1_3s, 2_2s, R_4s, 4_1s, 10_x): violations of each control value and run rejection
Parameters
json_values: The control values in time order - String - required
mean: The target mean - Number - required
sd: The target sd - Number - required
json_values: The control values in time order - String - required
mean: The target mean - Number - required
sd: The target sd - Number - required
admin
stat westgard "[100, 102, 99, 104.5, 104.2, 101, 97, 100, 106.5]" 100 2
mentdb
{"n": 9, "z": [0, 1, -0.5, 2.25, 2.1000000000000014, 0.5, -1.5, 0, 3.25], "violations": [{"index": 3, "value": 104.5, "z": 2.25, "rules": ["1_2s (warning)"], "reject": false}, {"index": 4, "value": 104.2, "z": 2.1000000000000014, "rules": ["1_2s (warning)", "2_2s"], "reject": true}, {"index": 8, "value": 106.5, "z": 3.25, "rules": ["1_2s (warning)", "1_3s"], "reject": true}], "reject_run": true}
stat probit <json_doses> <json_n> <json_responses>
Description
Probit analysis of a dose-response (toxicology): probit regression on log10 of the dose, LD50 with its Fieller interval, heterogeneity chi2
Probit analysis of a dose-response (toxicology): probit regression on log10 of the dose, LD50 with its Fieller interval, heterogeneity chi2
Parameters
json_doses: The doses - String - required
json_n: The number of subjects per dose - String - required
json_responses: The number of responses per dose - String - required
json_doses: The doses - String - required
json_n: The number of subjects per dose - String - required
json_responses: The number of responses per dose - String - required
admin
stat probit "[1, 2, 4, 8, 16]" "[20, 20, 20, 20, 20]" "[1, 4, 9, 15, 19]"
mentdb
{"link": "probit", "log10_dose": true, "intercept": -1.6793314426388233, "slope": 2.658679613739445, "intercept_se": 0.31219472568608514, "slope_se": 0.43643432422814504, "chi2": 0.13678643552329323, "df": 3, "heterogeneity_p_value": 0.9870838979681468, "effective_doses": [{"probability": 0.5, "dose": 4.2819456219056455, "lower": 3.2577845276825625, "upper": 5.655374644210261}]}
stat probit <json_doses> <json_n> <json_responses> <json_params>
Description
Probit analysis with parameters: link (probit or logit), log_dose (true), doses_at (probabilities, ex: [0.1, 0.5, 0.9]), level
Probit analysis with parameters: link (probit or logit), log_dose (true), doses_at (probabilities, ex: [0.1, 0.5, 0.9]), level
Parameters
json_doses: The doses - String - required
json_n: The numbers - String - required
json_responses: The responses - String - required
json_params: The parameters - String - required
json_doses: The doses - String - required
json_n: The numbers - String - required
json_responses: The responses - String - required
json_params: The parameters - String - required
admin
stat probit "[1, 2, 4, 8, 16]" "[20, 20, 20, 20, 20]" "[1, 4, 9, 15, 19]" "{\"link\": \"logit\", \"doses_at\": [0.1, 0.5, 0.9]}"
mentdb
{"link": "logit", "log10_dose": true, "intercept": -2.8909715512615346, "slope": 4.559649632981555, "intercept_se": 0.5940868237427392, "slope_se": 0.8438250788234265, "chi2": 0.24947734443583175, "df": 3, "heterogeneity_p_value": 0.969232372578184, "effective_doses": [{"probability": 0.1, "dose": 1.4195450663541305, "lower": 0.7013878678940635, "upper": 2.0626880651506676}, {"probability": 0.5, "dose": 4.305600245777641, "lower": 3.2395788305802666, "upper": 5.750150366933044}, {"probability": 0.9, "dose": 13.059249696138792, "lower": 8.929156251321865, "upper": 26.861686740521566}]}