sample_dataset_emails_per_day
function sampleMean( x ) {
var sum = 0;
for ( var i = 0; i < x.length; i++ ) {
sum += x[ i ];
}
return sum/x.length;
}
function e3_sample( x, n ) {
var out = new Array( n );
var idx;
for ( var i = 0; i < n; i++ ) {
idx = stdlib.base.random.discreteUniform( 0, x.length-1 );
out[ i ] = x[ idx ][ 2 ];
}
return out;
}
sampleMean( e3_sample( sample_dataset_emails_per_day.slice( 1 ), 10 ) )
{
// Pick a random starting index and take 10 consecutive entries...
var idx = stdlib.base.random.discreteUniform( 0, sample_dataset_emails_per_day.length-11 );
return sampleMean( stdlib.pluck( sample_dataset_emails_per_day.slice( idx, idx+10 ), 2 ) );
}
e5_x = {
// Return a set of sample sizes...
return stdlib.linspace( 1, 1000, 1000 );
}
e5_samples = {
// For each sample size, generate a new random sample...
var out = new Array( e5_x.length );
var d = sample_dataset_emails_per_day.slice( 1 ) ;
for ( var i = 0; i < out.length; i++ ) {
out[ i ] = e3_sample( d, e5_x[i] );
}
return out;
}
e5_means = {
// For each sample, compute the sample mean...
var out = new Array( e5_samples.length );
for ( var i = 0; i < out.length; i++ ) {
out[ i ] = sampleMean( e5_samples[ i ] );
}
return out;
}
plt5 = stdlib.plot({
'x': [ e5_x ], // e.g., 'x': [ n ]
'y': [ e5_means ], // e.g., 'y': [ mu ]
'xLabel': 'n',
'yLabel': 'sample mean',
'yMin': 0.0,
'width': 600
})
e6_x = {
// Generate a linearly-spaced array whose elements correspond to the "indices" of the simulated numbers...
return stdlib.linspace( 1, 10000, 10000 );
}
e6_y = {
// For each element in `x`, generate a normallyd-distributed pseudorandom number:
return stdlib.inmap( new Array( e6_x.length ), stdlib.base.random.normal.factory( 10.0, 3.0 ) );
}
plt6 = stdlib.plot({
'x': [ e6_x ], // e.g., 'x': [ stdlib.linspace(1,10000,10000) ]
'y': [ e6_y ], // e.g., 'y': [ rvs ]
'xLabel': 'x',
'yLabel': 'y',
'yMin': -10.0,
'yMax': 30.0,
'width': 600
})
e7_y = stdlib.inmap( e6_y.slice(), stdlib.incrmean() )
plt8 = stdlib.plot({
'x': [ stdlib.linspace( 1, e7_y.length, e7_y.length ) ], // e.g., 'x': [ n ]
'y': [ e7_y ], // e.g., 'y': [ mu ]
'xLabel': 'n',
'yLabel': 'sample mean',
'width': 600,
'yMin': 9.0,
'yMax': 11.0
})
viewof mu = html`<input type=range min=-10.0 max=10.0 step=0.1 value=0.0>`
viewof sigma = html`<input type=range min=0.1 max=5.0 step=0.1 value=1.0>`
e9_x = stdlib.linspace( -10.0, 10.0, 1000 )
e9_y = stdlib.inmap( e9_x.slice(), stdlib.base.dists.normal.pdf.factory( mu, sigma ) )
plt9 = stdlib.plot({
'x': [ e9_x ], // e.g., 'x': [ x ]
'y': [ e9_y ], // e.g., 'y': [ pdf ]
'xLabel': 'x',
'yLabel': 'pdf',
'yMin': 0.0,
'yMax': 0.5,
'width': 600
})
'<your_code_here>'
viewof nu = html`<input type=range min=0.1 max=10 step=0.1 value=1>`
e11_x = stdlib.linspace( -10.0, 10.0, 500 )
e11_y = stdlib.inmap( e11_x.slice(), stdlib.base.dists.t.pdf.factory( nu ) )
plt11 = stdlib.plot({
'x': [ e11_x ], // e.g., 'x': [ x ]
'y': [ e11_y ], // e.g., 'y': [ pdf ]
'xLabel': 'x',
'yLabel': 'pdf',
'yMin': 0.0,
'yMax': 0.5,
'width': 600
})
'<your_code_here>'
'<your_code_here>'
'<your_code_here>'
'<your_code_here>'
'<your_code_here>'
'<your_code_here>'
'<your_code_here>'
dataset_page1 = [
1, 0, 0, 0, 0, 0, 1, 0, 0, 1,
0, 0, 0, 1, 0, 0, 0, 1, 0, 0,
0, 1, 1, 0, 0, 0, 0, 0, 0, 0
]
dataset_page2 = [
0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 1, 0, 0
]
e19_p1 = sampleMean( dataset_page1 )
e19_p2 = sampleMean( dataset_page2 )
stdlib.ttest2( dataset_page1, dataset_page2 ).print()
stdlib.ttest2( dataset_page1, dataset_page2, {'alternative': 'less'} ).print()
stdlib.ttest2( dataset_page1, dataset_page2, {'alternative': 'greater'} ).print()
viewof p1 = html`<input type=range min=0.0 max=1.0 step=0.01 value=0.5>`
viewof n1 = html`<input type=range min=1 max=1000 step=1 value=10>`
viewof p2 = html`<input type=range min=0.0 max=1.0 step=0.01 value=0.5>`
viewof n2 = html`<input type=range min=1 max=1000 step=1 value=10>`
function randomBinarySequence( n, p ) {
var out = new Array( n );
var i;
for ( i = 0; i < n; i++ ) {
if ( stdlib.base.random.randu() < p ) {
out[ i ] = 1;
} else {
out[ i ] = 0;
}
}
return out;
}
function c3_sort_descending( a, b ) {
if ( a > b ) {
return -1;
}
if ( a === b ) {
return 0;
}
return 1;
}
pltc3 = stdlib.plot({
'x': [ stdlib.linspace( 0, 1, n1 ), stdlib.linspace( 0, 1, n2 ) ], // e.g., 'x': [ stdlib.linspace(0,1,n1), stdlib.linspace(0,1,n2) ]
'y': [ randomBinarySequence( n1, p1 ).sort( c3_sort_descending ), randomBinarySequence( n2, p2 ).sort( c3_sort_descending ) ], // e.g., 'y': [ sorted_dataset1, sorted_dataset2 ]
'xLabel': 'proportion',
'yLabel': 'converted',
'yMin': -0.1,
'yMax': 1.1,
'width': 600
})
c4_p1 = 0.1
c4_p2 = stdlib.linspace( c4_p1+0.1, 1.0, 100 )
c4_ns = {
var maxN = 1000;
var out;
var res;
var n;
var i;
out = new Array( c4_p2.length );
for ( i = 0; i < c4_p2.length; i++ ) {
n = 1;
while ( true ) {
res = stdlib.ttest2( randomBinarySequence( n, c4_p1 ), randomBinarySequence( n, c4_p2[ i ] ) );
if ( res.rejected ) {
out[ i ] = n;
break;
}
n += 1;
if ( n >= maxN ) {
break;
}
}
}
return out;
}
c4_dp = {
var out = new Array( c4_p2.length );
var i;
for ( i = 0; i < out.length; i++ ) {
out[ i ] = c4_p2[ i ] - c4_p1;
}
return out;
}
pltc4 = stdlib.plot({
'x': [ c4_dp ], // e.g., 'x': [ delta_p ]
'y': [ c4_ns ], // e.g., 'y': [ n ]
'xLabel': 'Δp',
'yLabel': 'n',
'xMin': 0.0,
'xMax': 1.0,
'yMin': 0,
'width': 600,
'lineStyle': [ 'none' ],
'symbols': [ 'open-circle' ]
})
'<your_code_here>'
plt22 = stdlib.plot({
'x': [ /* insert your `x` values */ ], // e.g., 'x': [ stdlib.linspace(-10,10,n) ]
'y': [ /* insert your pseudorandom numbers */ ], // e.g., 'y': [ rvs ]
'xLabel': 'x',
'yLabel': 'y',
'width': 600
})
'<your_code_here>'
'<your_code_here>'
pltc5 = stdlib.plot({
'x': [ /* insert your `n` values */ ], // e.g., 'x': [ stdlib.linspace(1,n,n) ]
'y': [ /* insert your proportions */ ], // e.g., 'y': [ y ]
'xLabel': 'n',
'yLabel': 'proportion',
'width': 600,
'yMin': 0.0,
'yMax': 1.1,
'lineStyle': [ 'none' ],
'symbols': [ 'open-circle' ]
})
'<your_code_here>'
'<your_code_here>'
plt25 = stdlib.plot({
'x': [ /* insert your `x` values */ ], // e.g., 'x': [ stdlib.linspace(-10,10,n) ]
'y': [ /* insert your pseudorandom numbers */ ], // e.g., 'y': [ rvs ]
'xLabel': 'x',
'yLabel': 'y',
'width': 600
})
'<your_code_here>'
'<your_code_here>'
nutrition_data
e29_groups = stdlib.bifurcateBy( nutrition_data, ( v ) => v.diabetes === 'Yes' )
e30_g1 = stdlib.inmap( e29_groups[ 0 ].slice(), ( v ) => parseInt( v.GREENSALADFREQ ) )
e30_g2 = stdlib.inmap( e29_groups[ 1 ].slice(), ( v ) => parseInt( v.GREENSALADFREQ ) )
stdlib.ttest2( e30_g1, e30_g2 ).print()
{
var keys = Object.keys( nutrition_data[ 0 ] ).slice( 27 );
var res;
var out;
var g2;
var g1;
var k;
var i;
out = [];
for ( i = 0; i < keys.length; i++ ) {
k = keys[ i ];
g1 = stdlib.inmap( e29_groups[ 0 ].slice(), ( v ) => parseInt( v[ k ] ) );
g2 = stdlib.inmap( e29_groups[ 1 ].slice(), ( v ) => parseInt( v[ k ] ) );
res = stdlib.ttest2( g1, g2 );
if ( res.rejected ) {
if ( res.xmean < res.ymean ) {
out.push( 'those with diabetes are less likely: '+k );
} else {
out.push( 'those with diabetes are more likely: '+k );
}
}
}
return out;
}
'<your_code_here>'
'<your_code_here>'
0.05/1066
'<your_code_here>'
plt34 = stdlib.plot({
'x': [ /* insert your `x` values */ ], // e.g., 'x': [ stdlib.linspace(1,1066,1066), stdlib.linspace(1,1066,1066) ]
'y': [ /* insert your y values and p-values */ ], // e.g., 'y': [ line_with_slope_alpha_over_m, pvals ]
'xLabel': 'x',
'yLabel': 'P_k',
'width': 600
})
Purpose-built for displays of data
Observable is your go-to platform for exploring data and creating expressive data visualizations. Use reactive JavaScript notebooks for prototyping and a collaborative canvas for visual data exploration and dashboard creation.
