5.1. Statistics Tool

The result of mean, geometricMean, quadraticMean, median, mode, min, max, sum, count, variance, populationVariance, standardDeviation, totalDeviation, totalCovariance, totalCorrelation, deviationMatrix, covarianceMatrix, pearsonsCorrelationMatrix, spearmansCorrelationMatrix, kendallsCorrelationMatrix will be available.
In Linear Regression Learning case, Correlation(between predictor variable and objective variable) value >0.7 is be recommended.
https://acrolearner.p.rapidapi.com/v0_1/CltService/tools/statistics
http method: post

Request Header parameters:

Content-Type: application/json
x-rapidapi-host: acrolearner.p.rapidapi.com
x-rapidapi-key: [your rapidapi-key]

Request Query parameters:

withDeviation=false&withCovariance=false&withPearsons=false&withSpearmans=false&withKendalls=false
withDeviation: DeviationMatrix return or not
withCovariance: CovarianceMatrix return or not
withPearsons: PearsonsCorrelationMatrix return
withSpearmans: SpearmansCorrelationMatrix return or not
withKendalls: KendallsCorrelationMatrix return or not

Request JSON Example:

[
  {
    "trainText": "1.4 2.3 3.6 4.1"
  },
  {
    "trainText": "75.6 85.1 90.9 105.7"
  }
]

Response JSON:

{
    "contents": {
        "count": [
            4,
            4
        ],
        "covarianceMatrix": null,
        "deviationMatrix": null,
        "geometricMean": [
            2.6256422812396676,
            88.66915230237679
        ],
        "kendallsCorrelationMatrix": null,
        "max": [
            4.1,
            105.7
        ],
        "mean": [
            2.8499999999999996,
            89.32499999999999
        ],
        "median": [
            2.95,
            88
        ],
        "min": [
            1.4,
            75.6
        ],
        "mode": [
            1.4,
            75.6
        ],
        "pearsonsCorrelationMatrix": null,
        "popVariance": [
            1.1324999999999998,
            119.21187500000012
        ],
        "quadraticMean": [
            3.042203149035251,
            89.9898188685809
        ],
        "spearmansCorrelationMatrix": null,
        "stdDeviation": [
            1.064189832689638,
            10.91841907054314
        ],
        "sum": [
            11.399999999999999,
            357.3
        ],
        "totalCorrelation": 0.9440136486762923,
        "totalCovariance": 10.968750000000004,
        "totalDeviation": 43.875000000000014,
        "variance": [
            1.5099999999999998,
            158.94916666666683
        ]
    },
    "errors": null,
    "memo": "Congratulations on your success!",
    "result": true
}

“mean”: mean value from number array of every “trainText”
“geometricMean”: geometricMean value from number array of every “trainText”
“quadraticMean”: quadraticMean value from number array of every “trainText”
“median”: median value from number array of every “trainText”
“mode”: mode value from number array of every “trainText”
“min”: min value from number array of every “trainText”
“max”: max value from number array of every “trainText”
“sum”: sum value from number array of every “trainText”
“count”: count value from number array of every “trainText”
“variance”: variance value from number array of every “trainText”
“popVariance”: populationVariance value from number array of every “trainText”
“stdDeviation”: standardDeviation value from number array of every “trainText”, calculate by populationVariance
"totalDeviation": totalDeviation value from number array of every “trainText”
“totalCovariance”: totalCovariance value from number array of every “trainText”
“totalCorrelation”: totalCorrelation value from number array of every “trainText”, calculate by populationVariance
"deviationMatrix": deviationMatrix from number array of every “trainText”
“covarianceMatrix”: covarianceMatrix from number array of every “trainText”
“pearsonsCorrelationMatrix”: pearsonsCorrelationMatrix from number array of every “trainText”
“spearmansCorrelationMatrix”: spearmansCorrelationMatrix from number array of every “trainText”
“kendallsCorrelationMatrix”: kendallsCorrelationMatrix from number array of every “trainText”