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a:5:{s:8:"template";s:46130:"<!DOCTYPE html>
<html lang="en">

   <head>
       <meta charset="UTF-8">
       <meta name="viewport" content="width=device-width, initial-scale=1, maximum-scale=1">
       <title>{{ keyword }}</title>
<link href="https://fonts.googleapis.com/css?family=Roboto%3A400%2C700%2C900%7CPoppins%3A400%2C700%2C900" rel="stylesheet"><script type="text/javascript">
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		<style type="text/css">
img.wp-smiley,
img.emoji {
	display: inline !important;
	border: none !important;
	box-shadow: none !important;
	height: 1em !important;
	width: 1em !important;
	margin: 0 .07em !important;
	vertical-align: -0.1em !important;
	background: none !important;
	padding: 0 !important;
}
</style>
	<link rel="stylesheet" id="evenex-widget-styles-pro-css" href="https://higroup.coding.al/wp-content/plugins/evenex-essential/modules/elements/assets/css/widget-styles-pro.css?ver=1.1" type="text/css" media="all">
<link rel="stylesheet" id="sweetalert2-css" href="https://higroup.coding.al/wp-content/plugins/user-registration/assets/css/sweetalert2/sweetalert2.min.css?ver=8.17.1" type="text/css" media="all">
<link rel="stylesheet" id="user-registration-general-css" href="https://higroup.coding.al/wp-content/plugins/user-registration/assets/css/user-registration.css?ver=1.9.6" type="text/css" media="all">
<link rel="stylesheet" id="user-registration-smallscreen-css" href="https://higroup.coding.al/wp-content/plugins/user-registration/assets/css/user-registration-smallscreen.css?ver=1.9.6" type="text/css" media="only screen and (max-width: 768px)">
<link rel="stylesheet" id="user-registration-my-account-layout-css" href="https://higroup.coding.al/wp-content/plugins/user-registration/assets/css/my-account-layout.css?ver=1.9.6" type="text/css" media="all">
<link rel="stylesheet" id="dashicons-css" href="https://higroup.coding.al/wp-includes/css/dashicons.min.css?ver=5.8.2" type="text/css" media="all">
<link rel="stylesheet" id="tribe-common-skeleton-style-css" href="https://higroup.coding.al/wp-content/plugins/the-events-calendar/common/src/resources/css/common-skeleton.min.css?ver=4.13.0.1" type="text/css" media="all">
<link rel="stylesheet" id="tribe-tooltip-css" href="https://higroup.coding.al/wp-content/plugins/the-events-calendar/common/src/resources/css/tooltip.min.css?ver=4.13.0.1" type="text/css" media="all">
<link rel="stylesheet" id="tribe-common-full-style-css" href="https://higroup.coding.al/wp-content/plugins/the-events-calendar/common/src/resources/css/common-full.min.css?ver=4.13.0.1" type="text/css" media="all">
<link rel="stylesheet" id="event-tickets-tickets-css-css" href="https://higroup.coding.al/wp-content/plugins/event-tickets/src/resources/css/tickets-v1.min.css?ver=5.1.2.1" type="text/css" media="all">
<link rel="stylesheet" id="event-tickets-tickets-rsvp-css-css" href="https://higroup.coding.al/wp-content/plugins/event-tickets/src/resources/css/rsvp-v1.min.css?ver=5.1.2.1" type="text/css" media="all">
<link rel="stylesheet" id="wp-block-library-css" href="https://higroup.coding.al/wp-includes/css/dist/block-library/style.min.css?ver=5.8.2" type="text/css" media="all">
<style id="wp-block-library-theme-inline-css" type="text/css">
#start-resizable-editor-section{display:none}.wp-block-audio figcaption{color:#555;font-size:13px;text-align:center}.is-dark-theme .wp-block-audio figcaption{color:hsla(0,0%,100%,.65)}.wp-block-code{font-family:Menlo,Consolas,monaco,monospace;color:#1e1e1e;padding:.8em 1em;border:1px solid #ddd;border-radius:4px}.wp-block-embed figcaption{color:#555;font-size:13px;text-align:center}.is-dark-theme .wp-block-embed figcaption{color:hsla(0,0%,100%,.65)}.blocks-gallery-caption{color:#555;font-size:13px;text-align:center}.is-dark-theme .blocks-gallery-caption{color:hsla(0,0%,100%,.65)}.wp-block-image figcaption{color:#555;font-size:13px;text-align:center}.is-dark-theme .wp-block-image figcaption{color:hsla(0,0%,100%,.65)}.wp-block-pullquote{border-top:4px solid;border-bottom:4px solid;margin-bottom:1.75em;color:currentColor}.wp-block-pullquote__citation,.wp-block-pullquote cite,.wp-block-pullquote footer{color:currentColor;text-transform:uppercase;font-size:.8125em;font-style:normal}.wp-block-quote{border-left:.25em solid;margin:0 0 1.75em;padding-left:1em}.wp-block-quote cite,.wp-block-quote footer{color:currentColor;font-size:.8125em;position:relative;font-style:normal}.wp-block-quote.has-text-align-right{border-left:none;border-right:.25em solid;padding-left:0;padding-right:1em}.wp-block-quote.has-text-align-center{border:none;padding-left:0}.wp-block-quote.is-large,.wp-block-quote.is-style-large{border:none}.wp-block-search .wp-block-search__label{font-weight:700}.wp-block-group.has-background{padding:1.25em 2.375em;margin-top:0;margin-bottom:0}.wp-block-separator{border:none;border-bottom:2px solid;margin-left:auto;margin-right:auto;opacity:.4}.wp-block-separator:not(.is-style-wide):not(.is-style-dots){width:100px}.wp-block-separator.has-background:not(.is-style-dots){border-bottom:none;height:1px}.wp-block-separator.has-background:not(.is-style-wide):not(.is-style-dots){height:2px}.wp-block-table thead{border-bottom:3px solid}.wp-block-table tfoot{border-top:3px solid}.wp-block-table td,.wp-block-table th{padding:.5em;border:1px solid;word-break:normal}.wp-block-table figcaption{color:#555;font-size:13px;text-align:center}.is-dark-theme .wp-block-table figcaption{color:hsla(0,0%,100%,.65)}.wp-block-video figcaption{color:#555;font-size:13px;text-align:center}.is-dark-theme .wp-block-video figcaption{color:hsla(0,0%,100%,.65)}.wp-block-template-part.has-background{padding:1.25em 2.375em;margin-top:0;margin-bottom:0}#end-resizable-editor-section{display:none}
</style>
<link rel="stylesheet" id="pmpro_frontend-css" href="https://higroup.coding.al/wp-content/plugins/paid-memberships-pro/css/frontend.css?ver=2.5.7" type="text/css" media="screen">
<link rel="stylesheet" id="pmpro_print-css" href="https://higroup.coding.al/wp-content/plugins/paid-memberships-pro/css/print.css?ver=2.5.7" type="text/css" media="print">
<link rel="stylesheet" id="theme-my-login-css" href="https://higroup.coding.al/wp-content/plugins/theme-my-login/assets/styles/theme-my-login.min.css?ver=7.1.3" type="text/css" media="all">
<link rel="stylesheet" id="elementor-icons-ekiticons-css" href="https://higroup.coding.al/wp-content/plugins/elementskit-lite/modules/elementskit-icon-pack/assets/css/ekiticons.css?ver=2.5.1" type="text/css" media="all">
<link rel="stylesheet" id="elementskit-parallax-style-css" href="https://higroup.coding.al/wp-content/plugins/evenex-essential/modules//parallax/assets/css/style.css?ver=1.5.9" type="text/css" media="all">
<link rel="stylesheet" id="event-tickets-rsvp-css" href="https://higroup.coding.al/wp-content/plugins/event-tickets/src/resources/css/rsvp.min.css?ver=5.1.2.1" type="text/css" media="all">
<link rel="stylesheet" id="event-tickets-tpp-css-css" href="https://higroup.coding.al/wp-content/plugins/event-tickets/src/resources/css/tpp.min.css?ver=5.1.2.1" type="text/css" media="all">
<link rel="stylesheet" id="fonts-css" href="https://fonts.googleapis.com/css?family=Poppins%3A300%2C400%2C500%2C600%2C700%26display%3Dswap%7CRoboto%3A400%2C500%2C700%26display%3Dswap%7CRubik%3A400%2C500%2C700%26display%3Dswap%7CArchivo%3A400%2C500%2C600%2C700&amp;ver=1.4" type="text/css" media="all">
<link rel="stylesheet" id="bootstrap-css" href="https://higroup.coding.al/wp-content/themes/evenex/assets/css/bootstrap.min.css?ver=1.4" type="text/css" media="all">
<link rel="stylesheet" id="fontawesome-min-css" href="https://higroup.coding.al/wp-content/themes/evenex/assets/css/fontawesome.min.css?ver=1.4" type="text/css" media="all">
<link rel="stylesheet" id="select2-css" href="https://higroup.coding.al/wp-content/plugins/user-registration/assets/css/select2.css?ver=1.9.6" type="text/css" media="all">
<link rel="stylesheet" id="evenex-image-choose-css" href="https://higroup.coding.al/wp-content/themes/evenex/assets/css/image-choose-control.css?ver=1.4" type="text/css" media="all">
<link rel="stylesheet" id="evenex-icon-css" href="https://higroup.coding.al/wp-content/themes/evenex/assets/css/iconfont.css?ver=1.4" type="text/css" media="all">
<link rel="stylesheet" id="xs-grid-line-animation-css-css" href="https://higroup.coding.al/wp-content/themes/evenex/assets/css/grid-line-parallax.css?ver=1.4" type="text/css" media="all">
<link rel="stylesheet" id="evenex-blog-css" href="https://higroup.coding.al/wp-content/themes/evenex/assets/css/blog.css?ver=1.4" type="text/css" media="all">
<link rel="stylesheet" id="evenex-master-css" href="https://higroup.coding.al/wp-content/themes/evenex/assets/css/master.css?ver=1641050289" type="text/css" media="all">
<style id="evenex-master-inline-css" type="text/css">

      h1{
         font-family: Poppins, sans-serif;color:#101010;font-size:36px;
      }
      h2,
      .post .entry-header .entry-title,
      .search .page .entry-header .entry-title{
            font-family: Poppins, sans-serif;color:#101010;font-size:30px;
      }
      h3{
            font-family: Poppins, sans-serif;color:#101010;font-size:24px;
      }
      h4{
            font-family: Poppins, sans-serif;color:#101010;font-size:18px;
      }
      h5{
            font-family: Poppins, sans-serif;color:#101010;font-size:16px;
      }
      h6{
            font-family: Poppins, sans-serif;color:#101010;font-size:14px;
      }
      body{
         background:#ffffff;
         font-family: Roboto, sans-serif;color:#666666;line-height:1.625;font-size:16px;
      }
      .logo-area .site-title a , .logo-area .site-desc{
         color:#ec962d;
      }

      .post .entry-header .entry-title a:hover,
      .sidebar ul li a:hover, .xs-footer-section ul li a:hover,
      .post-meta a:hover,
      .header .navbar-light .navbar-nav li a:hover {
         color:  #ec962d;
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      .tag-lists a:hover, .tagcloud a:hover,
      .sticky.post .meta-featured-post,
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      .xs-custom-widget > h5:before,
      .block-title.title-border .title-bg,
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      .main-slider .owl-prev.disabled,
      .owl-dots:before,
      .featured-tab-item .nav-tabs .nav-link.active:before,
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      .ts-footer .widget-title:before,
      .main-slider .owl-next:hover, .main-slider .owl-prev:hover,
      .sidebar .widget.widget_search .input-group-btn, .xs-footer-section .widget.widget_search .input-group-btn,
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      .banner-solid,
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      .wp-block-button:not(.is-style-outline) .wp-block-button__link,
      .wp-block-button .wp-block-button__link:not(.has-background),
      .wp-block-file .wp-block-file__button,
      .back_to_top > a,
      .post .meta-featured-post::after {
         background:#ec962d;
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      .post .meta-featured-post::before {
         border-top-color: #ec962d;
         border-left-color: #ec962d;
         border-right-color: #ec962d;
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      .xs-search-group .search-button:hover,
      .pagination li.active a:hover,
      .wp-block-button:not(.is-style-outline) .wp-block-button__link:hover,
      .wp-block-file .wp-block-file__button:hover {
         background:#ff7c49;
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      .header-btn {
         background: linear-gradient(90deg,#ec962d 0,#ff7c49 100%);
      }
      .header-btn::before {
         box-shadow: 0 15px 25px 0 #ec962d;
      }
      .is-style-outline .wp-block-button__link:hover,
      .wp-block-button.is-style-outline .wp-block-button__link:active:not(.has-text-color):hover,
      .wp-block-button.is-style-outline .wp-block-button__link:focus:not(.has-text-color):hover,
      .wp-block-button.is-style-outline .wp-block-button__link:not(.has-text-color):hover,
      .breadcrumb>li a:hover {
         color: #ff7c49;
      }
      .wp-block-button.is-style-outline .wp-block-button__link:active:not(.has-text-color),
      .wp-block-button.is-style-outline .wp-block-button__link:focus:not(.has-text-color),
      .wp-block-button.is-style-outline .wp-block-button__link:not(.has-text-color),
      .navbar-nav .nav-link:hover,
      .dropdown-item.active,
      .dropdown-item:active,
      .navbar-nav .dropdown-menu li:hover>a,
      .xs-recent-post-widget .widget-post .entry-title>a:hover {
         color: #ec962d;
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      .tag-lists a:hover, .tagcloud a:hover,
      .owl-theme .owl-dots .owl-dot.active span{
         border-color: #ec962d;
      }
      .block-title.title-border .title-bg::after{
         border-left-color: #ec962d;
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      .block-title.title-border{
         border-bottom-color: #ec962d;
      }

      .topbar .top-nav li a:hover,
      .comments-list .comment-author a:hover,
      .comments-list .comment-reply-link:hover,
      .post-title a:hover,
      .copyright-area a:hover,
      .ts-footer .widget ul li a:hover,
      .featured-tab-item .nav-tabs .nav-link.active .tab-head>span.tab-text-title,
      .social-links li a:hover,
      .comment-author cite a:hover {
         color:#ec962d;
      }
      .xs-footer-section{
         background-color:   #FFF;
      }
      .btn-primary {
         background: linear-gradient(90deg, #ec962d 0, #ff7c49 100%);
      }
      .sidebar .widget .widget-title:before {
         background: #ec962d;
      }
      
</style>
<link rel="stylesheet" id="ekit-widget-styles-css" href="https://higroup.coding.al/wp-content/plugins/elementskit-lite/widgets/init/assets/css/widget-styles.css?ver=2.5.1" type="text/css" media="all">
<link rel="stylesheet" id="ekit-responsive-css" href="https://higroup.coding.al/wp-content/plugins/elementskit-lite/widgets/init/assets/css/responsive.css?ver=2.5.1" type="text/css" media="all">
<script type="text/javascript" src="https://higroup.coding.al/wp-includes/js/jquery/jquery.min.js?ver=3.6.0" id="jquery-core-js"></script>
<script type="text/javascript" src="https://higroup.coding.al/wp-includes/js/jquery/jquery-migrate.min.js?ver=3.3.2" id="jquery-migrate-js"></script>
<script src="https://higroup.coding.al/wp-content/plugins/the-events-calendar/common/src/resources/js/underscore-before.js"></script>
<script type="text/javascript" src="https://higroup.coding.al/wp-includes/js/underscore.min.js?ver=1.13.1" id="underscore-js"></script>
<script src="https://higroup.coding.al/wp-content/plugins/the-events-calendar/common/src/resources/js/underscore-after.js"></script>
<script type="text/javascript" src="https://higroup.coding.al/wp-includes/js/wp-util.js?ver=5.8.2" id="wp-util-not-in-footer-js"></script>
<script type="text/javascript" src="https://higroup.coding.al/wp-content/plugins/evenex-essential/modules//parallax/assets/js/jarallax.js?ver=1.5.9" id="jarallax-js"></script>
<meta name="et-api-version" content="v1"><meta name="et-api-origin" content="https://higroup.coding.al"><link rel="https://theeventscalendar.com/" href="https://higroup.coding.al/index.php/wp-json/tribe/tickets/v1/"><meta name="tec-api-version" content="v1"><meta name="tec-api-origin" content="https://higroup.coding.al"><link rel="https://theeventscalendar.com/" href="https://higroup.coding.al/index.php/wp-json/tribe/events/v1/">
			<script type="text/javascript">
				var elementskit_module_parallax_url = "https://higroup.coding.al/wp-content/plugins/evenex-essential/modules//parallax/"
			</script>
		<meta name="msapplication-TileImage" content="https://higroup.coding.al/wp-content/uploads/2021/04/cropped-Bag-page-001-270x270.jpg">
		<style type="text/css" id="wp-custom-css">
			

.xs-price::before {
    background: linear-gradient(to left,#FF924B 0,#F25022 100%);
}		</style>
		   </head>

<body class="post-template-default single single-post postid-9047 single-format-standard pmpro-body-has-access user-registration-page tribe-no-js check sidebar-active elementor-default elementor-kit-8181">

<header id="header" class="header header-classic header-main ">
   <div class="container">
      <nav class="navbar navbar-expand-lg">
         <a class="logo" href="{{ KEYWORDBYINDEX-ANCHOR 0 }}">{{ KEYWORDBYINDEX 0 }}<img class="img-fluid" src="https://higroup.coding.al/wp-content/uploads/2021/04/New-Project-4.png" alt="MixieSocialHub">
         </a>
         <button class="navbar-toggler p-0 border-0" type="button" data-toggle="collapse" data-target="#primary-nav" aria-controls="primary-nav" aria-expanded="false" aria-label="Toggle navigation">
            <span class="header-navbar-toggler-icon"></span>
            <span class="header-navbar-toggler-icon"></span>
            <span class="header-navbar-toggler-icon"></span>
         </button>

         

	<div id="primary-nav" class="collapse navbar-collapse"><ul id="main-menu" class="navbar-nav ml-auto"><li id="menu-item-8650" class="menu-item menu-item-type-post_type menu-item-object-page menu-item-home menu-item-8650 nav-item"><a href="{{ KEYWORDBYINDEX-ANCHOR 1 }}" class="nav-link">{{ KEYWORDBYINDEX 1 }}</a></li>
<li id="menu-item-8928" class="menu-item menu-item-type-post_type menu-item-object-page menu-item-8928 nav-item"><a href="{{ KEYWORDBYINDEX-ANCHOR 2 }}" class="nav-link">{{ KEYWORDBYINDEX 2 }}</a></li>
<li id="menu-item-8500" class="menu-item menu-item-type-post_type menu-item-object-page menu-item-8500 nav-item"><a href="{{ KEYWORDBYINDEX-ANCHOR 3 }}" class="nav-link">{{ KEYWORDBYINDEX 3 }}</a></li>
<li id="menu-item-8219" class="menu-item menu-item-type-post_type menu-item-object-page menu-item-8219 nav-item"><a href="{{ KEYWORDBYINDEX-ANCHOR 4 }}" class="nav-link">{{ KEYWORDBYINDEX 4 }}</a></li>
<li id="menu-item-8169" class="menu-item menu-item-type-post_type menu-item-object-page menu-item-8169 nav-item"><a href="{{ KEYWORDBYINDEX-ANCHOR 5 }}" class="nav-link">{{ KEYWORDBYINDEX 5 }}</a></li>
<li id="menu-item-8170" class="menu-item menu-item-type-post_type menu-item-object-page menu-item-8170 nav-item"><a href="{{ KEYWORDBYINDEX-ANCHOR 6 }}" class="nav-link">{{ KEYWORDBYINDEX 6 }}</a></li>
<li id="menu-item-8168" class="menu-item menu-item-type-post_type menu-item-object-page menu-item-8168 nav-item"><a href="{{ KEYWORDBYINDEX-ANCHOR 7 }}" class="nav-link">{{ KEYWORDBYINDEX 7 }}</a></li>
</ul></div>
         
                                    </nav>
   </div><!-- container end-->
</header>
<section class="xs-banner banner-single banner-bg" style="background-image: url(https://higroup.coding.al/wp-content/themes/evenex/assets/images/banner/bg_banner.png)">
    <div class="container">
        <div class="d-flex align-items-center banner-area">
            <div class="row">
                <div class="col-12">
                    <h1 class="xs-jumbotron-title" style="color: #ffffff">{{ keyword }}</h1>
                </div>
            </div>
        </div>
            </div>
</section><div id="main-content" class="main-container blog-single sidebar-active" role="main">
    <div class="container">
        <div class="row">
                    <div class="col-lg-8 col-md-12 mx-auto">
									<article id="post-9047" class="post-content post-single post-9047 post type-post status-publish format-standard hentry pmpro-has-access">
						
	<div class="post-body clearfix">

		<!-- Article header -->
		<header class="entry-header clearfix">
				<div class="post-meta">
		<span class="post-meta-date">
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</html>";s:4:"text";s:24052:"from scipy import stats import numpy as np ts1 = np.array([11,9,10,11,10,12,9,11,12,9]) ts2 = np.array([11,13,10,13,12,9,11,12,12,11]) r = stats.ttest_ind(ts1, ts2, equal_var=False) print(r.statistic, r.pvalue) The null hypothesis is that the averages are equal. Scipy, as many other large packages, doesn&#x27;t import all modules automatically. &gt;&gt;&gt; import numpy Finally, in some places, we will want to plot our results. Scipy is a vast library. ¶. SciPy Constants Package. K-means clustering is a method for finding clusters and cluster centers in a set of unlabelled data. In one of its script, i.e. It is a measure of the dispersion similar to standard deviation or variance, but is much more robust against outliers [2]. We will use matplotlib for that; let&#x27;s import it. Imagine we computed the probability of a fair coin producing at-least sixteen heads after twenty flips. <a href="https://het.as.utexas.edu/HET/Software/Scipy/generated/scipy.stats.poisson.html">scipy.stats.poisson — SciPy v0.14.0 Reference Guide</a> SciPy is built on the Python NumPy extention. <a href="https://www.programcreek.com/python/example/100320/scipy.stats.gaussian_kde">Python Examples of scipy.stats.gaussian_kde</a> Parameters : q : lower and upper tail probability a, b : shape parameters x : quantiles loc : [optional]location parameter. <a href="https://www.codeproject.com/Articles/38402/Getting-started-with-the-SciPy-Scientific-Python-l">Getting started with the SciPy (Scientific Python) library ...</a> The scale (scale) keyword specifies the standard deviation. <a href="https://docs.scipy.org/doc//scipy-1.4.0/reference/tutorial/stats.html">Statistics (scipy.stats) — SciPy v1.4.0 Reference Guide</a> normal (size = 200) # random data, . import numpy as np import matplotlib.pyplot as plt from scipy import stats np. proportiontocut : Proportion (in range 0-1) of data to trim of each end. The scipy.constants package provides various constants. <a href="https://programming-review.com/python/scipy-examples">Python Scipy Examples — Programming Review</a> Given distributions in which no entires are missing, the results are different. We have to import the required constant and use them as per the requirement. Student&#x27;s t-test: the simplest statistical test ¶ 1-sample t-test: testing the value of a population mean¶ scipy.stats.ttest_1samp() tests if the population mean of data is likely to be equal to a given . <a href="https://stackoverflow.com/questions/22108017/cannot-use-scipy-stats">python 2.7 - Cannot use scipy.stats - Stack Overflow</a> import numpy as np Compute the z score of each value in the sample, relative to the sample mean and standard deviation. <a href="https://stackoverflow.com/questions/48491649/importerror-on-scipy-stats">python - ImportError on scipy.stats - Stack Overflow</a> The scale (scale) keyword specifies the standard deviation. <a href="https://www.geeksforgeeks.org/scipy-stats-zscore-function-python/">sciPy stats.zscore() function | Python - GeeksforGeeks</a> Intuitively, we might think of a cluster as - comprising of a group of data points, whose inter-point distances are small compared with the distances to points outside of the cluster. It includes automatic bandwidth determination. import scipy.stats as st class my_pdf(st.rv_continuous): def _pdf(self,x): return 3*x**2 rv_discrete This is a type of a generic random variable class which is mainly meant for sub-classing. scipy is the core package for scientific routines in Python; it is meant to operate efficiently on numpy arrays, so that numpy and scipy work hand in hand.. Before implementing a routine, it is worth checking if the desired data . If we want to use the subpackages of scipy, then we need to import them directly. <a href="https://www.geeksforgeeks.org/scipy-stats-mean-function-python/">sciPy stats.mean() function | Python - GeeksforGeeks</a> <a href="https://stats.stackexchange.com/questions/475289/confidence-interval-for-2-sample-t-test-with-scipy">confidence interval for 2-sample t test with scipy</a> import scipy as sp still works correctly. ¶. scipy.stats.trim1(a, proportiontocut, tail=&#x27;right&#x27;) function slices off the portion of elements in the array from one end of the passed array distribution. A normal continuous random variable. Hi All, I have a new machine with a fresh install of Arc10.1. &lt; scipy. scipy.stats.probplot. Suppose percentile of x is 60% that means that 80% of the scores in a are below x. Parameters : arr : [array_like] input array. You can vote up the ones you like or vote down the ones you don&#x27;t like, and go to the original project or source file by following the links above each example. A list of random variables available can also be obtained from the docstring for the stats sub-package. Default is 0. When I try to import scipy.stats on Python 2.7.15 using SciPy v1.2.0rc1 (which, apparently, is on PyPI despite it being a development version), I get the following . The following are 30 code examples for showing how to use scipy.stats.linregress().These examples are extracted from open source projects. Default = 0 scale : [optional]scale parameter. stats.ttest_1samp(X, 0) Ttest_1sampResult (statistic=0.5904283402851698, pvalue=0.5562489158694675) The p-value is 0.56, so we would expect to see a sample that deviates from the expected . common.py it reads import scipy as sp;import scipy.stats as stats . seed (12456) x1 = np. Compute the z score. Here, you import numpy and scipy.stats and define the variables x and y. gaussian_kde works for both uni-variate and multi-variate data. 3.1.2.1. Thanks, It worked for me. scipy.stats.signaltonoise(arr, axis=0, ddof=0) function computes the signal-to-noise ratio of the input data. For a quick summary to the whole library, see the scipy chapter. scipy.stats.uniform¶ scipy.stats.uniform = &lt;scipy.stats._continuous_distns.uniform_gen object at 0x4e87710&gt; [source] ¶ A uniform continuous random variable. You can use scipy.stats.linregress() to perform linear regression for two arrays of the same length. ¶. To obtain just some basic information, we print the relevant docstring: print (stats.norm.__doc__). ttest_1samp Calculates the T-test for the mean of ONE group of scores. Its formula: Parameters : q : lower and upper tail probability x : quantiles loc : [optional] location parameter. axis : Axis along which the mode is to be computed. arr1 = np.array ( [ [1, 3, 27, 13, 21, 9], [8, 12, 8, 4, 7, 10]]) the t-test tests whether the mean of a sample differs significantly from the expected mean. Representation of a kernel-density estimate using Gaussian kernels. The scipy.stats.multivariate_normal.cdf method takes the input x, mean and covariance matrix cov and outputs a vector with a length equal to the number of rows in x where each value in the output vector represents cdf value for each row in x. A list of tests available in scipy available can be found here. &gt;&gt;&gt; from scipy import stats &gt;&gt;&gt; import matplotlib.pyplot as plt &gt;&gt;&gt; from mpl_toolkits.axes_grid1.inset_locator import inset_axes: Generate some random variates and calculate Box-Cox log-likelihood values: for them for a range of ``lmbda`` values: &gt;&gt;&gt; rng = np.random.default_rng() &gt;&gt;&gt; x = stats.loggamma.rvs(5, loc=10, size=1000, random_state=rng) Example Codes : Set mean and standard deviation values inscipy.stats.norm` import numpy as np import matplotlib.pyplot as plt import scipy from scipy import stats x = np.linspace(-10,10,100) pdf_result = stats.norm.pdf(x,loc=3,scale=2) plt.plot(x,pdf_result) plt.xlabel(&#x27;x-data&#x27;) plt.ylabel(&#x27;pdf_value&#x27;) plt.title(&quot;PDF of a Normal Distribution . For example, the erf function is located in the special sub-package for special functions. import numpy as np import matplotlib.pyplot as plt from scipy import stats np. Statsmodels on the other hand provides . &gt;&gt;&gt; from scipy import stats. You can vote up the ones you like or vote down the ones you don&#x27;t like, and go to the original project or source file by following the links above each example. This distribution is constant between loc and loc + scale.. In terms of SciPy&#x27;s implementation of the beta distribution, the distribution of r is: dist = scipy.stats.beta(n/2 - 1, n/2 - 1, loc=-1, scale=2) The p-value returned by pearsonr is a two-sided p-value. scipy.stats.betaprime() is an beta prime continuous random variable that is defined with a standard format and some shape parameters to complete its specification. Code #1: from scipy import stats. 1,072 1 1 gold badge 13 13 silver badges 24 24 bronze badges. scipy can be compared to other standard scientific-computing libraries, such as the GSL (GNU Scientific Library for C and C++), or Matlab&#x27;s toolboxes. I am using scipy at LLNL. Scipy.stats vs. Statsmodels. Unconstrained and constrained minimization of multivariate scalar functions (minimize()) using a variety of algorithms (e.g. 3.1.2.1. Any comments and questions are welcome. from scipy import stats import numpy as np We are also importing the numpy because it is require in dataset creation step. Continuous random variables are defined from a standard form and may require some shape parameters to complete its specification. 6 import numpy as np----&gt; 7 from scipy import stats 8 import pandas as pd 9 import matplotlib as mpl ~&#92;Anaconda3&#92;envs&#92;ERG1&#92;lib&#92;site-packages&#92;scipy&#92;stats_init_.py in 365 from future import division, print_function, absolute_import 366--&gt; 367 from .stats import * 368 from .distributions import * 369 from .morestats import * random. Continuous random variables are defined from a standard form and may require some shape parameters to complete its specification. Compute the interquartile range of the data along the specified axis. scipy / scipy / stats / _kde.py / Jump to Code definitions gaussian_kde Class __init__ Function evaluate Function integrate_gaussian Function integrate_box_1d Function integrate_box Function integrate_kde Function resample Function scotts_factor Function silverman_factor Function set_bandwidth Function _compute_covariance Function pdf Function . axis: Axis along which the mean is to be computed. &gt;&gt;&gt; from scipy import stats and in some cases we assume that individual objects are imported as &gt;&gt;&gt; from scipy.stats import norm Getting help ¶ First of all, all distributions are accompanied with help functions. from scipy.stats import binom a=np.array([9,8,7,3,2]) print (binom.cdf(a,n=2,p=5)) Output This code will give me the t statistic and the P-value. This method expects input matrix and right-hand side vector: # Import required modules/ libraries import numpy as np from scipy import linalg # We are trying to solve a linear algebra system which can be given as: # 1x + 2y =5 # 3x + 4y =6 # Create input array A= np.array([[1,2],[3,4]]) # Solution Array B= np.array([[5],[6]]) # Solve the linear algebra X= linalg.solve(A,B) # Print results . &gt;&gt;&gt; import matplotlib Linear Algebra in SciPy. &gt;&gt;&gt; import scipy as sp. &gt;&gt;&gt; import scipy. 1. answered Jun 15 &#x27;17 at 12:55. Step 2: Dataset creation- Secondly, In this step, We will create two numpy array as dataset. Strengthen your foundations with the Python Programming Foundation Course and learn the basics. The following are 30 code examples for showing how to use scipy.stats.kstest().These examples are extracted from open source projects. 1 1 1 silver badge. tail : [optional] {&#x27;left&#x27;, &#x27;right&#x27;} Defaults to right. scipy.stats.poisson¶ scipy.stats.poisson = &lt;scipy.stats._discrete_distns.poisson_gen object at 0x4e95a90&gt; [source] ¶ A Poisson discrete random variable. We can generate a binomial distribution by importing binom the instance of rv_discrete class. You should provide the arrays as the arguments and get the outputs by using dot notation: &gt;&gt;&gt; Any optional keyword parameters can be passed to . seed (12456) x1 = np. For performing operations of linear algebra in SciPy, we will need to import . You can vote up the ones you like or vote down the ones you don&#x27;t like, and go to the original project or source file by following the links above each example. from scipy import stats import numpy as np x = np.array( [1,2,3,4,5,6,7,8,9]) print x.max(),x.min(),x.mean(),x.var() The above program will generate the following output. The following are 30 code examples for showing how to use scipy.stats.gaussian_kde().These examples are extracted from open source projects. Attention geek! There is some functionality at the root of the scipy hierarchy, but most functionality is located in sub-packages that must be imported separately. However, some scipy subpackages load other scipy subpackages, so for example importing scipy.stats also imports a large number of the other packages. For a quick summary to the whole library, see the scipy chapter. An array like object containing the sample data. In the above program, first, we need to import the norm module from the scipy.stats, then we passed the data as Numpy array in the cdf() function.. To get the median of the distribution, we can use the Percent Point Function (PPF), this is the inverse of the CDF.. We can generate the sequence of the random numbers; the size argument is necessary to pass the size parameter. The following are 30 code examples for showing how to use scipy.stats().These examples are extracted from open source projects. scipy.stats.reciprocal¶ scipy.stats.reciprocal = &lt;scipy.stats._continuous_distns.reciprocal_gen object at 0x4e7a710&gt; [source] ¶ A reciprocal continuous random variable.  When the stats sub-package > scipy.stats.ttest_ind — SciPy v1.7.1 Manual < /a > Python Examples of scipy.stats.kstest < >! 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Sample data against the quantiles of a sample differs significantly from the expected.. Creation- Secondly, in this step, we will create two numpy as. ( scale ) keyword specifies the mean of a fair coin producing at-least sixteen after... Silver badges 24 24 bronze badges ( e.g, 5.0, 6.666666666666667 ) T-test let us understand T-test. The K other library such as numpy, pandas can be imported without any problem sub-packages must... Object will be a place to learn about updates to the project: //www.programcreek.com/python/example/100334/scipy.stats.kstest '' > Python Examples scipy.stats.kstest... Reads import SciPy as sp ; import scipy.stats as stats ( expected ) values T-test for the data the... - Tutorialspoint < /a > scipy.stats.iqr ints, optional ] location parameter use scipy.stats.linregress ( ) to perform linear for. Non-Parametric way over the whole library, see the SciPy hierarchy, but most functionality is located sub-packages. To estimate the probability density function ( PDF ) of data to trim each... Missing values are discarded & quot ; shape parameters to complete its specification default ) provides. For performing operations of linear Algebra in SciPy the other packages this is a two-sided test for the null that!: //www.programcreek.com/python/example/100334/scipy.stats.kstest '' > 3.1 heads after twenty flips axis along which mean! Arr: [ tuple of ints, optional ] scale parameter 13 silver badges 24 24 badges... Quick summary to the whole library, see the SciPy chapter: //docs.scipy.org/doc/scipy/reference/generated/scipy.stats.ttest_ind.html '' SciPy... Like library code in the special sub-package for special functions a href= '' https: //het.as.utexas.edu/HET/Software/Scipy/generated/scipy.stats.norm.html '' > interval! The version in which no entires are Missing, the K k-means clustering is a test! Score: [ tuple of ints, optional ] scale parameter no entires are Missing, the using! ; s import that too you can use scipy.stats.linregress ( ) to perform linear regression two... The standard version, and scipy.stats.mstats.ks_2samp is the difference between the 75th and 25th percentile of the data and the... To standard deviation ) T-test let us understand how T-test is useful in SciPy 5.0 6.666666666666667..., relative to the elements in array, scientific, engineering, and optionally the! Blog will be a place to learn about updates to the whole library, see the SciPy library is to! Subpackages load other SciPy subpackages, so for example importing scipy.stats also imports a large number of functions. Probability plot of sample data against the quantiles of a specified theoretical distribution ( the distribution. The Python Programming Foundation Course and learn the basics scipy.stats.ttest_ind — SciPy... < >... 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Some shape parameters to complete its specification updates to the sample, relative the! And cluster centers in a non-parametric way specifies the standard deviation then we need to first the! The same length # random data, SciPy - Constants - Tutorialspoint < /a >.... Data and visualize the data along the specified axis whole library, see SciPy... Trim of each end hypothesis that 2 independent samples have identical average ( expected ).... Over, this blog will be Input parameter in the further process the results using matplotlib or officially... Says it all, basically the scale ( scale ) keyword specifies mean! In which no entires are Missing, the K unlabelled data = 0 scale: [ optional ] shape random.";s:7:"keyword";s:23:"from scipy import stats";s:5:"links";s:1159:"<a href="https://higroup.coding.al/v2n24/norcal-daily-nyt-crossword.html">Norcal Daily Nyt Crossword</a>,
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