PHP Statistics Library using Al-Kashi PHP Statistical Analysis Functions - Al-Kashi package blog

Introduction to Al-Kashi PHP Statistics Class

Example Data and Statistics

Summary Statistics

Statistical Graphics

Correlation, Regression, and t-Test

Distributions

Chi-square test or Contingency tables (A/B testing)

Diversity index

Analysis of Variance (ANOVA)

Cluster Analysis

Time Series Analysis

To-do list

Introduction to Al-Kashi PHP Statistics Class

The Al-Kashi is a project that can be used in applications that may incude an online log file analysis, advertising campaign statistics, or survey or voting results on-the-fly analysis.

This project is published under GPL license. You can download it from the PHP Classes web site. Here and you can see the log of changes .

Example Data and Statistics

Below follows examples of statistics obtained with this class from example data set.

The data presented in this example was extracted from the 1974 Motor Trend US magazine. It comprises fuel consumption and 10 aspects of automobile design and performance for 32 cars 1973-74 models). You can download the example data file from here.

Description (Motor Trend Car Road Tests)

Format: A data frame with 32 observations on 12 variables.

ID	Title	Description
1	model	Car models
2	mpg	Miles/(US) gallon
3	cyl	Number of cylinders
4	disp	Displacement (cu.in.)
5	hp	Gross horsepower
6	drat	Rear axle ratio
7	wt	Weight (lb/1000)
8	qsec	1/4 mile time
9	vs	V/S
10	am	Transmission (0 = automatic, 1 = manual)
11	gear	Number of forward gears
12	carb	Number of carburetors

Example code read example data and feed it to Al Kashi

$sep = "\t"; $nl = "\n";
$content = file_get_contents('data.txt');
$records = explode($nl, $content);
$header = explode($sep, trim(array_shift($records)));
$data = array_fill_keys($header, array());
foreach ($records as $id=>$record) {
$record = trim($record);
if ($record == '') continue;
$fields = explode($sep, $record);
$titles = $header;
foreach ($fields as $field) {
$title = array_shift($titles);
$data[$title][] = $field;
}
}
$x = $data['wt'];
$y = $data['mpg'];
require('kashi.php');
$kashi = new Kashi();

PHP Statistical Functions Summary

Mean (x)	3.21725
Mean (x, "geometric")	3.0701885671208
Mean (x, "harmonic")	2.9182632148104
Median (x)	3.325
Mode (x)	Array ( [0] => 3.44 )
Variance (x)	0.95737896774194
SD (x)	0.9784574429897
%CV (x)	30.412850819479
Skewness (x)	0.46591610679299
Is it significant (i.e. test it against 0)?	bool(false)
Kurtosis (x)	0.41659466963493
Is it significant (i.e. test it against 0)?	bool(false)

Rank (x)

9, 12, 7, 16, 18, 21, 23, 15, 13, 18, 18, 29, 25, 26, 30, 32, 31, 6, 2, 3, 8, 22, 17, 27, 28, 4, 5, 1, 14, 10, 23, 11

// $x is an array of values  
echo 'Arithmetic Mean: ' . $kashi->mean($x) . '<br>';  
echo 'Aeometric Mean: '  . $kashi->mean($x, "geometric") . '<br>';  
echo 'Harmonic Mean: '   . $kashi->mean($x, "harmonic")  . '<br>';  
  
echo 'Mode: '     . print_r($kashi->mode($x)) . '<br>';  
echo 'Median: '   . $kashi->median($x)   . '<br>';  
echo 'Variance: ' . $kashi->variance($x) . '<br>';  
echo 'SD: '       . $kashi->sd($x)       . '<br>';  
echo '%CV: '      . $kashi->cv($x)       . '<br>';  
  
echo 'Skewness: ' . $kashi->skew($x) . '<br>';  
echo 'Is it significant (i.e. test it against 0)? ';  
var_dump($kashi->isSkew($x));  
  
echo 'Kurtosis: ' . $kashi->kurt($x) . '<br>';  
echo 'Is it significant (i.e. test it against 0)? ';  
var_dump($kashi->isKurt($x));  
  
echo 'Rank (x): ';    
echo implode(', ', $kashi->rank($x)) . '<br>';  

Statistical Graphics

Boxplot	Array ( [min] => 1.513 [q1] => 2.62 [median] => 3.325 [q3] => 3.73 [max] => 5.282 [outliers] => Array ( [0] => 5.345 [1] => 5.424 ) )
Histogram	Array ( [1.513-2.002] => 4 [2.002-2.491] => 4 [2.491-2.98] => 4 [2.98-3.469] => 9 [3.469-3.957] => 7 [3.957-4.446] => 1 [4.446-4.935] => 0 [4.935-5.424] => 3 )
Normal Q-Q Plot	x = -0.62609901275838, -0.36012989155586, -0.83051087731871, -0.039176085543034, 0.27769043950814, 0.36012989155586, 0.62609901275838, -0.11776987461046, -0.27769043950814, 0.19709908415753, 0.11776987461046, 1.2298587580185, 0.72451438304624, 0.83051087731871, 1.417797139161, 2.1538746917937, 1.6759397215193, -0.94678175657479, -1.6759397215193, -1.417797139161, -0.72451438304624, 0.44509652516901, 0.039176085543034, 0.94678175657479, 1.0775155681381, -1.2298587580185, -1.0775155681381, -2.1538746917937, -0.19709908415753, -0.53340970683585, 0.53340970683585, -0.44509652516901 y = 2.62, 2.875, 2.32, 3.215, 3.44, 3.46, 3.57, 3.19, 3.15, 3.44, 3.44, 4.07, 3.73, 3.78, 5.25, 5.424, 5.345, 2.2, 1.615, 1.835, 2.465, 3.52, 3.435, 3.84, 3.845, 1.935, 2.14, 1.513, 3.17, 2.77, 3.57, 2.78
Ternary Plot	x = 0.729, 0.722, 0.734, 0.706, 0.695, 0.675, 0.659, 0.723, 0.701, 0.692, 0.679, 0.663, 0.676, 0.654, 0.577, 0.574, 0.625, 0.779, 0.785, 0.788, 0.716, 0.667, 0.664, 0.645, 0.691, 0.763, 0.766, 0.796, 0.689, 0.723, 0.672, 0.718 y = 0.356, 0.36, 0.369, 0.382, 0.376, 0.419, 0.407, 0.364, 0.406, 0.387, 0.408, 0.398, 0.395, 0.422, 0.463, 0.459, 0.403, 0.312, 0.317, 0.31, 0.394, 0.407, 0.417, 0.41, 0.368, 0.34, 0.323, 0.3, 0.375, 0.354, 0.381, 0.377

echo 'Boxplot: <br><pre>';  
print_r($kashi->boxplot($x));  
echo '</pre><br>';  
  
echo 'Histogram: <br><pre>';  
print_r($kashi->hist($x, 8));  
echo '</pre><br>';  
  
echo 'Normal Q-Q Plot: <br>';  
$qq = $kashi->qqnorm($x);  
echo 'x = ' . implode(', ', $qq['x']) . '<br>';  
echo 'y = ' . implode(', ', $qq['y']) . '<br>';  
  
echo 'Ternary Plot: <br>';  
$xy = $kashi->ternary($data['wt'], $data['mpg'], $data['qsec']);  
echo 'x = ' . implode(', ', $xy['x']) . '<br>';  
echo 'y = ' . implode(', ', $xy['y']) . '<br>';  

Correlation, Regression, and t-Test

Covariance (x, y)	-5.1166846774194
Correlation (x, y)	-0.86765937651723
Significant of Correlation	1.2939593840855E-10
Regression (y = a + b*x)	Array ( [intercept] => 37.285126167342 [slope] => -5.3444715727227 [r-square] => 0.24716720634174 [adj-r-square] => 0.22207277988646 [intercept-se] => 1.8776273372559 [intercept-2.5%] => 33.450499570026 [intercept-97.5%] => 41.119752764658 [slope-se] => 0.55910104509932 [slope-2.5%] => -6.486308238383 [slope-97.5%] => -4.2026349070623 [F-statistic] => 91.375325003762 [p-value] => 1.2939604943085E-10 )
t-Test unpaired	-15.632569384303
Test of null hypothesis that mean of x = mean of y Probability is	5.5511151231258E-16
t-Test paired	-13.847209446072
Test of null hypothesis that mean of x-y = 0 Probability is	8.1046280797636E-15

echo 'Covariance: '  . $kashi->cov($x, $y) . '<br>';  
echo 'Correlation: ' . $kashi->cor($x, $y) . '<br>';  
  
$r = $kashi->cor($x, $y);  
$n = count($x);  
echo 'Significant of Correlation: ' . $kashi->corTest($r, $n) . '<br>';  
  
echo 'Regression: ' . print_r($kashi->lm($y, $x), true) . '<br>';  
  
echo 't-Test unpaired: ' . $kashi->tTest($x, $y, false) . '<br>';  
echo 'Test: ' . $kashi->tDist($kashi->tTest($x, $y, false),   
  (count($x)-1)*(count($y)-1)) . '<br>';
echo 't-Test paired: ' . $kashi->tTest($x, $y, true) . '<br>';  
echo 'Test: ' . $kashi->tDist($kashi->tTest($x, $y, true), 
  count($x)-1) . '<br>';  

Distributions

Normal distribution (x=0.5, mean=0, sd=1)	0.3520653267643
Probability for the Student t-distribution (t=3, n=10) one-tailed	0.01334365502257
Probability for the Student t-distribution (t=3, n=10) two-tailed	0.0066718275112848
Probability for F distribution (f=2, df1=12, df2=15)	0.10268840717083
Inverse of the standard normal cumulative distribution, with a probability of (p=0.95)	1.6448536251337
t-value of the Student's t-distribution for the probability $p and $n degrees of freedom (p=0.05, n=29)	2.0452296438589

Standardize (x)
(mean=0 & variance=1)

-0.61039956748153, -0.34978526910097, -0.91700462439985, -0.002299537926887, 0.22765425476185, 0.24809459188973, 0.36051644609311, -0.027849959336746, -0.068730633592521, 0.22765425476185, 0.22765425476185, 0.8715248742903, 0.52403914311621, 0.57513998593593, 2.0775047648356, 2.2553356978483, 2.1745963661931, -1.0396466471672, -1.6375265081579, -1.4126827997511, -0.76881218022266, 0.3094156032734, 0.22254417047987, 0.63646099731959, 0.64157108160156, -1.3104811141117, -1.1009676585508, -1.7417722275101, -0.048290296464633, -0.45709703902238, 0.36051644609311, -0.44687687045844

echo 'Normal distribution (x=0.5, mean=0, sd=1): ' .
     $kashi->norm(0.5, 0, 1) . '<br>';  
  
echo 'Probability for the Student t-distribution (t=3, n=10)',
     ' one-tailed: ';   
echo $kashi->tDist(3, 10, 1) . '<br>';  
  
echo 'Probability for the Student t-distribution (t=3, n=10)',
     ' two-tailed: ';   
echo $kashi->tDist(3, 10, 2) . '<br>';  
  
echo 'F probability distribution (f=2, df1=12, df2=15): '.
     $kashi->fDist(2, 12, 15) . '<br>';  
  
echo 'Inverse of the standard normal cumulative distribution',
     ' (p=0.95): ';   
echo $kashi->inverseNormCDF(0.95) . '<br>';  
  
echo 't-value of the Student\'s t-distribution (p=0.05, n=29): ';   
echo $kashi->inverseTCDF(0.05, 29) . '<br>';  
  
echo 'Standardize (x) (i.e. mean=0 & variance=1): ';  
echo implode(', ', $kashi->standardize($x)) . '<br>';  

Chi-square test or Contingency tables (A/B testing)

Calculate the probability that number of cylinders distribution in automatic and manual transmission cars is same

0.012646605046107

$table['Automatic'] = array('4 Cylinders' => 3, '6 Cylinders' => 4,
      '8 Cylinders' => 12);  
$table['Manual']    = array('4 Cylinders' => 8, '6 Cylinders' => 3,
      '8 Cylinders' => 2);  
  
$results     = $kashi->chiTest($table);  
$probability = $kashi->chiDist($result['chi'], $result['df']);  
echo 'Chi-square test probability: ' . $probability . '<br>';  

Diversity index

Shannon index for number of forward gears	1.0130227035447
Simpson index for number of cylinders	0.357421875

$gear = array('3' => 15, '4' => 12, '5' => 5);  
$cyl  = array('4' => 11, '6' => 7, '8' => 14);  
  
echo 'Shannon index for gear: ' . $kashi->diversity($gear) .
     '<br>';  
echo 'Simpson index for cyl: ' . $kashi->diversity($cyl, 'simpson').
     '<br>';  

Analysis of Variance (ANOVA)

Analysis of variance procedure (ANOVA)

Typical ANOVA example output (mpg ~ cyl):

ANOVA table
 
Variate: mpg

Source of 
variation   d.f.  s.s.    m.s.    v.r.    F pr.
cyl         2     824.78  412.39  39.70   <.001
Residual    29    301.26  10.39	 	 
Total       31    1126.05	 	 	 

 
Tables of means
 
Grand mean  20.09 
 
cyl     4       6       8
        26.66   19.74   15.10
rep.    11      7       14
 
Standard errors of means
 
e.s.e.  1.218	 min.rep
        0.861	 max.rep
 
Standard errors of differences of means
 
s.e.d.  1.723X	 min.rep
        1.218X	 max.rep

Least significant differences of means
(5% level)

l.s.d.  3.524X	 min.rep
        2.492X	 max.rep
 
Stratum standard errors and coefficients
of variation
 
d.f.    s.e.    cv%
29      3.223   16.0

Array
(
 [TDF] => 2
 [EDF] => 29
 [TotDF] => 31
 [SST] => 824.7845900974
 [SSE] => 301.2625974026
 [SSTot] => 1126.0471875
 [MST] => 412.3922950487
 [MSE] => 10.388365427676
 [VRT] => 39.697515255869
 [F] => 4.9789191744003E-9
 [Mean] => 20.090625
 [Means] => Array
 (
   [4] => 26.6636364
   [6] => 19.7428571
   [8] => 15.1000000
 )

 [Reps] => Array
 (
   [4] => 11
   [6] => 7
   [8] => 14
 )

 [SE] => Array
 (
  [min] => 1.2182168131961
  [max] => 0.86140936956643
 )

 [SED] => Array
 (
   [min] => 1.7228187391329
   [max] => 1.2182168131961
 )

 [LSD] => Array
 (
   [min] => 3.5235599562701
   [max] => 2.491533138996
 )

 [CV] => 16.042799717154
)

require('kashi_anova.php');  
  
// $obj = new KashiANOVA($dbname, $dbuser, $dbpass, $dbhost);  
$obj = new KashiANOVA('test', 'root', '', 'localhost');  
  
$str = file_get_contents('anova_data.txt');  
$obj->loadString($str);   
  
// mpg ~ cyl  
$result = $obj->anova('cyl', 'mpg');  
print_r($result);  

Cluster Analysis

K-Means Clustering

Array
(
    [Mazda RX4] => 0
    [Porsche 914-2] => 0
    [Lotus Europa] => 0
    [Fiat X1-9] => 0
    [Fiat 128] => 0
    [Toyota Corona] => 0
    [Toyota Corolla] => 0
    [Honda Civic] => 0
    [Merc 280] => 0
    [Merc 280C] => 0
    [Datsun 710] => 0
    [Valiant] => 0
    [Volvo 142E] => 0
    [Merc 240D] => 0
    [Merc 230] => 0
    [Hornet 4 Drive] => 0
    [Mazda RX4 Wag] => 0
    [Pontiac Firebird] => 1
    [Maserati Bora] => 1
    [Ferrari Dino] => 1
    [Ford Pantera L] => 1
    [Camaro Z28] => 1
    [Lincoln Continental] => 1
    [Merc 450SE] => 1
    [Duster 360] => 1
    [Hornet Sportabout] => 1
    [Merc 450SL] => 1
    [Merc 450SLC] => 1
    [Dodge Challenger] => 1
    [Chrysler Imperial] => 1
    [Cadillac Fleetwood] => 1
    [AMC Javelin] => 1
)

Hierarchical Clustering

32	15	14	0.034867528963888
33	12	11	0.046511652279906
34	1	0	0.048063902847295
35	10	9	0.048146270217687
36	33	13	0.048374485470338
37	24	4	0.06456633193609
38	19	17	0.067898627038737
39	22	21	0.092305891561629
40	39	37	0.11301195978463
41	32	16	0.11529825256692
42	31	2	0.1155541020107
43	5	3	0.11717892926293
44	40	36	0.11995870908923
45	23	6	0.12445889917409
46	38	25	0.12703468709516
47	46	42	0.19819935352147
48	8	7	0.20845446781686
49	48	20	0.22553907135502
50	45	44	0.23476357897562
51	47	18	0.24068916220486
52	50	41	0.25528946686225
53	34	29	0.26595333894602
54	51	27	0.27674027068183
55	54	26	0.28056404941297
56	49	43	0.28521660028422
57	56	35	0.30779338554525
58	30	28	0.35715746216011
59	55	53	0.37801491177356
60	59	57	0.42234403985919
61	60	52	0.52592878486916
62	61	58	0.49319668374021

require('kashi_cluster.php');  
$obj = new KashiCluster();  
$obj->dataLoad($data);  
  
$result = $obj->kMean(2);  
print_r($result);  
  
// Heretical tree output has no header, and consists of four columns. For each row, the first column is the   
// identifier of the node, the second and third columns are child nodes identifier, and the fourth column used   
// to determine the height of the node when rendering a tree.  
$tree = $obj->hClust();  
echo "<pre>$tree</pre>";  

Time Series Analysis

Moving Average

2.894, 3.062, 3.201, 3.375, 3.362, 3.362, 3.358, 3.458, 3.566, 3.692, 4.054, 4.4508, 4.7058, 4.3998, 3.9668, 3.2838, 2.692, 2.327, 2.574, 3.019, 3.421, 3.315, 3.039, 2.6546, 2.5206, 2.3056, 2.6326, 2.7606

echo 'Moving Average for x: ' . implode(', ', $kashi->movingAvg($x, 5)) . '<br>';  

To-do list

Principal Component Analysis (PCA)
Multiple Linear Regression & Relative Weights
Analysis of Covariance
Extra Clustering Methods (i.e. Linkage Criteria)

PHP Statistics Library using Al-Kashi PHP Statistical Analysis Functions - Al-Kashi package blog

Contents

Introduction to Al-Kashi PHP Statistics Class

Example Data and Statistics

Summary Statistics

Statistical Graphics

Correlation, Regression, and t-Test

Distributions

Chi-square test or Contingency tables (A/B testing)

Diversity index

Analysis of Variance (ANOVA)

Cluster Analysis

Time Series Analysis

To-do list

Introduction to Al-Kashi PHP Statistics Class

Example Data and Statistics

PHP Statistical Functions Summary

Statistical Graphics

Correlation, Regression, and t-Test

Distributions

Chi-square test or Contingency tables (A/B testing)

Diversity index

Analysis of Variance (ANOVA)

Cluster Analysis

Time Series Analysis

To-do list

Principal Component Analysis (PCA)
Multiple Linear Regression & Relative Weights
Analysis of Covariance
Extra Clustering Methods (i.e. Linkage Criteria)

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PHP Statistics Library using Al-Kashi PHP Statistical Analysis Functions - Al-Kashi package blog

Contents

Introduction to Al-Kashi PHP Statistics Class

Example Data and Statistics

Summary Statistics

Statistical Graphics

Correlation, Regression, and t-Test

Distributions

Chi-square test or Contingency tables (A/B testing)

Diversity index

Analysis of Variance (ANOVA)

Cluster Analysis

Time Series Analysis

To-do list

Introduction to Al-Kashi PHP Statistics Class

Example Data and Statistics

PHP Statistical Functions Summary

Statistical Graphics

Correlation, Regression, and t-Test

Distributions

Chi-square test or Contingency tables (A/B testing)

Diversity index

Analysis of Variance (ANOVA)

Cluster Analysis

Time Series Analysis

To-do list

Principal Component Analysis (PCA) Multiple Linear Regression & Relative Weights Analysis of Covariance Extra Clustering Methods (i.e. Linkage Criteria)

You need to be a registered user or login to post a comment

1,616,107 PHP developers registered to the PHP Classes site. Be One of Us!

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Principal Component Analysis (PCA)
Multiple Linear Regression & Relative Weights
Analysis of Covariance
Extra Clustering Methods (i.e. Linkage Criteria)

1,616,107 PHP developers registered to the PHP Classes site.
Be One of Us!