# Statistical analysis

.ml.stats Statistical functions

Descriptive statistics describe Descriptive information about a table percentile Percentile calculation for an array

Statistical estimation methods describeFuncs Modify statistical functions applied to data OLS.fit Train an ordinary least squares model on data WLS.fit Train a weighted least squares model on data

This statistical library contains functionality ranging from descriptive statistical methods to gain more insight into your data, to linear-regression estimation methods to investigate unknown parameters in a model.

### Modifying description functionality

The statistical functions applied to the data can be altered by either

• modifying stats/describe.json
• calling .ml.stats.describeFuncs

The JSON file follows the format

 "count":{
"func":"count",
"type":["num","temporal","other"]
},
"type":{
"func":"{.ml.stats.i.metaTypes .Q.ty x}",
"type":["num","temporal","other"]
},
"mean":{
"func":"avg",
"type":["num"]
},
"std":{
"func":"sdev",
"type":["num"]
},
"min":{
"func":"min",
"type":["num","temporal"]
}

## .ml.stats.describe

Generates descriptive statistics of columns in a table

.ml.stats.describe tab

Where tab is a simple table, returns a tabular description of aggregate information (count, standard deviation, quartiles etc) for each column.

q)n:1000
q)tab:([]sym:n?4;x:n?10000f;x1:1+til n;x2:reverse til n;x3:n?100f)
q).ml.stats.describe tab
| sym     x           x1       x2       x3
-----        | -------------------------------------------------
count        | 1000    1000        1000     1000     1000
type         | symbol float      long    long    float
mean         | ::      5101.113    500.5    499.5    51.59885
std          | ::      2833.686    288.8194 288.8194 29.33562
min          | ::      9.771725    1        0        0.02741043
max          | ::      9973.398    1000     999      99.96445
q1           | ::      2708.268    250.75   249.75   27.0071
q2           | ::      5148.468    500.5    499.5    51.23665
q3           | ::      7375.113    750.25   749.25   78.01016
nulls        | 0i      0i          0i       0i       0i
inf          | ::      0i          0i       0i       0i
range        | ::      9963.627    999      999      99.93704
skew         | ::      -0.03914009 0f       0f       -0.07741558
countDistinct| 994     1000        1000     1000     1000
mode         | lnmj   5300.464    1        999      90.61246
freq         | 1       1           1        1        1
sampleDev    | ::      2833.686    288.8194 288.8194 29.33562
standardError| ::      89.56419    9.128705 9.128705 0.9272099

// Generate a table containing only temporal data
q)timeTab:([]"z"$n?100;"d"$n?til 5)
q).ml.stats.describe timeTab
| x                       x1
-----        | ----------------------------------
count        | 1000                    1000
type         | datetime               date
min          | 2000.01.01T00:00:00.000 2000.01.01
max          | 2000.04.09T00:00:00.000 2000.01.05
nulls        | 0i                      0i
range        | 99f                     4i
countDistinct| 100                     5
mode         | 2000.03.09T00:00:00.000 2000.01.05
freq         | 3                       187

Deprecated

The above function was previously defined as .ml.describe. That is still callable but will be removed after version 3.0.

## .ml.stats.describeFuncs

.ml.stats.describeFuncs loads the dictionary defined in stats/describe.json and returns it as a table

q)5#.ml.stats.describeFuncs
| func                              type
-----| ------------------------------------------------------------
count| "count"                           ("num";"temporal";"other")
type | "{.ml.stats.i.metaTypes .Q.ty x}" ("num";"temporal";"other")
mean | "avg"                             ,"num"
std  | "sdev"                            ,"num"
min  | "min"                             ("num";"temporal")

in which

• the key of the dictionary defines the name of the function that will appear in the table returned from .ml.stats.describe.
• func is the function to be applied to the data
• type defines the type of data that the function will be applied to. The valid types allowed are numtemporalother
num      "hijef"
temporal "pmdznuvt"
other    All other remaining types

## .ml.stats.percentile

Percentile calculation for an array

.ml.stats.percentile[array;perc]

Where

• array is a numerical array
• perc is the percentile of interest between 0-1

returns the value below which perc percent of the observations within the array are found.

q).ml.stats.percentile[10000?1f;0.2]
0.2030272
q).ml.stats.percentile[10000?1f;0.6]
0.5916521

## .ml.stats.OLS.fit

Train an ordinary least squares model on data

.ml.stats.OLS.fit[endog;exog;trend]

Where

• endog is the numerical endogenous variable
• exog are the numerical exogenous variables in n dimensions
• trend indicates whether a trend is included or not when calculating the parameters

returns the coefficients and statistical values calculated during the fitting process (modelInfo) and a projection of the fit function allowing for prediction on new data (predict).

More info on endogenous and exogenous variables can be found within the timeseries section of the toolkit

Result dictionary

The information contained within modelInfo has three parts

• coef The coefficients calculated during the fitting process
• variables Statistical values calculated for each coefficient
• statsDict Descriptive statistics for the regression model. These include:
key description
dfTotal Total degrees of freedom
dfModel The degrees of freedom of the model
dfResidual The degrees of freedom of the residuals
sumSquares Sum of squares between the true and predicted values
meanSquares Mean squares between the true and predicted values using degrees of freedom
fStat F statistic
r2 r2 score
mse Mean squared error
rse Residual squared error
pValue p Value
logLike log liklihood
q)exog:til 10
q)endog:3+2*til 10
q)trend:1b
q)show mdl:.ml.stats.OLS.fit[endog;exog;trend]
modelInfo| coefvariablesstatsDict!(3 2f;(+(,name)!,yInterce..
predict  | {[config;exog]
modelInfo:configmodelInfo;
trend:yIntercept i..

// Coefficients and statistical values calculated during the
// fitting process
q)mdl.modelInfo
coef     | 3 2f
variables| (+(,name)!,yInterceptx0)!+coefstdErrtStatpVal..
statsDict| dfTotaldfModeldfResidualSSTotalSSModelSSResidu..
q)mdl.modelInfo.variables
name      | coef stdErr       tStat        pValue C195
----------| --------------------------------------------------
yIntercept| 3    3.011588e-15 9.961524e+14 0      6.944733e-15
x0        | 2    5.64122e-16  3.545332e+15 0      1.300868e-15
q)mdl.modelInfo.statsDict
dfTotal   | 9
dfModel   | 1
dfResidual| 8
SSResidual| 2.100342e-28
MSTotal   | 36.66667
...

// Use the fitted model to predict on new data
q)newData:4 2 3 1 2 6
q)mdl.predict newData
11 7 9 5 7 15f

## .ml.stats.WLS.fit

Train a weighted least squares model on data

.ml.stats.WLS.fit[endog;exog;weights;trend]

Where

• endog is the numerical endogenous variable
• exog are the exogenous variables in n dimensions
• weights are the weights to be applied to the endog variable (must be the same length as the endog variable). If ()/(::) is passed, the model deduces the weights by using the inverse of the residuals calculated from fitting the data on an ordinary OLS model
• trend indicates whether a trend is included or not when calculating the parameters

returns the coefficients and statistical values calculated during the fitting process (modelInfo) and a projection of the fit function allowing for prediction on new data (predict)

Result dictionary

The information in modelInfo has four parts

• coef The coefficients calculated during the fitting process
• variables Statistical values calculated for each coefficient
• statsDict Descriptive statistics for the regression model. These include:
• weights The weights used for fitting the model
key description
dfTotal Total degrees of freedom
dfModel The degrees of freedom of the model
dfResidual The degrees of freedom of the residuals
sumSquares Sum of squares between the true and predicted values
meanSquares Mean squares between the true and predicted values using degrees of freedom
fStat F statistic
r2 r2 score
mse Mean squared error
rse Residual squared error
pValue p Value
logLike log liklihood
q)exog:til 10
q)endog:3+2*til 10
q)weights:10?5
q)trend:0b
q)show mdl:.ml.stats.WLS.fit[endog;exog;weights;trend]
modelInfo| coefvariablesstatsDictweights!(,2.50289;(+(,name)!..
predict  | {[config;exog]
modelInfo:configmodelInfo;
trend:yIntercept i..
// Coefficients and statistical values calculated during the
// fitting process
q)mdl.modelInfo
coef     | ,2.50289
variables| (+(,name)!,,x0)!+coefstdErrtStatpValueC195!(,2...
statsDict| dfTotaldfModeldfResidualSSTotalSSModelSSResidual..
weights  | 4 4 0 4 0 2 0 0 4 0
// Statistic information calculated during the fitting
q)mdl.modelInfo.variables
name| coef    stdErr    tStat    pValue      C195
----| -----------------------------------------------
x0  | 2.50289 0.1012509 24.71968 2.68138e-10 0.233485
q)mdl.modelInfo.statsDict
dfTotal   | 9
dfModel   | 1
dfResidual| 8
SSTotal   | 330f
SSModel   | ,516.8179
SSResidual| ,26.29573
MSTotal   | 36.66667
MSModel   | ,516.8179
MSResidual| ,3.286967
...

// Use the fitted model to predict on new data
q)newData:4 2 3 1 2 6
q)mdl.predict newData
10.01156 5.00578 7.508671 2.50289 5.00578 15.01734