{"id":1495,"date":"2023-10-30T11:50:10","date_gmt":"2023-10-30T11:50:10","guid":{"rendered":"https:\/\/myknowledgehub.org\/?p=1495"},"modified":"2023-11-26T11:29:59","modified_gmt":"2023-11-26T11:29:59","slug":"research-methodology-chapter-6-5","status":"publish","type":"post","link":"https:\/\/myknowledgehub.org\/index.php\/2023\/10\/30\/research-methodology-chapter-6-5\/","title":{"rendered":"Research Methodology Chapter 8.1"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"1495\" class=\"elementor elementor-1495\">\n\t\t\t\t<div class=\"elementor-element elementor-element-52db2cb e-flex e-con-boxed e-con e-parent\" data-id=\"52db2cb\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div class=\"elementor-element elementor-element-7d25bd5 e-con-full e-flex e-con e-child\" data-id=\"7d25bd5\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-1fe6d70 elementor-widget elementor-widget-image\" data-id=\"1fe6d70\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"150\" height=\"150\" src=\"https:\/\/myknowledgehub.org\/wp-content\/uploads\/2023\/11\/bayesian-statistics-bell-curve-2889576-150x150.png\" class=\"attachment-thumbnail size-thumbnail wp-image-1806\" alt=\"bayesian, statistics, bell curve-2889576.jpg\" srcset=\"https:\/\/myknowledgehub.org\/wp-content\/uploads\/2023\/11\/bayesian-statistics-bell-curve-2889576-150x150.png 150w, https:\/\/myknowledgehub.org\/wp-content\/uploads\/2023\/11\/bayesian-statistics-bell-curve-2889576-300x300.png 300w, https:\/\/myknowledgehub.org\/wp-content\/uploads\/2023\/11\/bayesian-statistics-bell-curve-2889576-1024x1024.png 1024w, https:\/\/myknowledgehub.org\/wp-content\/uploads\/2023\/11\/bayesian-statistics-bell-curve-2889576-768x768.png 768w, https:\/\/myknowledgehub.org\/wp-content\/uploads\/2023\/11\/bayesian-statistics-bell-curve-2889576.png 1280w\" sizes=\"(max-width: 150px) 100vw, 150px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-8e9fd94 e-con-full e-flex e-con e-child\" data-id=\"8e9fd94\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-610e480 elementor-widget elementor-widget-heading\" data-id=\"610e480\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Probability Distribution<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-a5fb99c e-flex e-con-boxed e-con e-parent\" data-id=\"a5fb99c\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-10cc332 elementor-widget elementor-widget-heading\" data-id=\"10cc332\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\"><h2 data-elementor-setting-key=\"title\" data-pen-placeholder=\"Type Here...\" style=\"font-size: 2.26667rem;font-style: normal\">Definition and Types <\/h2><\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-c43d5ae e-flex e-con-boxed e-con e-parent\" data-id=\"c43d5ae\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-02d415a elementor-widget elementor-widget-text-editor\" data-id=\"02d415a\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p style=\"margin: 0in; background-image: initial; background-position: initial; background-size: initial; background-repeat: initial; background-attachment: initial; background-origin: initial; background-clip: initial;\"><span style=\"font-family: Roboto;\">In the field of statistics, probability&nbsp;distribution refers to the mathematical function that describes the likelihood&nbsp;of different outcomes occurring in a particular event or experiment.&nbsp;<\/span><span style=\"text-align: var(--text-align); background-color: var(--ast-global-color-5);\"><span style=\"font-family: Roboto;\"><b>A probability distribution is a function that describes&nbsp;the likelihood of different outcomes in a random event<\/b>. It provides a way to&nbsp;assign probabilities to each possible outcome of a random event, based on the&nbsp;likelihood of that outcome occurring<\/span><\/span><span style=\"font-style: inherit; font-weight: inherit; text-align: var(--text-align); background-color: var(--ast-global-color-5); color: var(--ast-global-color-3);\"><span style=\"font-family: Roboto; color: black;\">.&nbsp;<\/span><\/span><\/p>\n<p style=\"margin: 0in; background-image: initial; background-position: initial; background-size: initial; background-repeat: initial; background-attachment: initial; background-origin: initial; background-clip: initial;\"><span style=\"font-family: Roboto;\">&nbsp;<\/span><\/p>\n<p style=\"margin: 0in; background-image: initial; background-position: initial; background-size: initial; background-repeat: initial; background-attachment: initial; background-origin: initial; background-clip: initial;\"><span style=\"font-family: Roboto;\">There are&nbsp;several types of probability distributions, each with its own characteristics&nbsp;and applications.&nbsp;<\/span><\/p>\n<p style=\"margin: 0in; text-align: justify; background-image: initial; background-position: initial; background-size: initial; background-repeat: initial; background-attachment: initial; background-origin: initial; background-clip: initial;\"><span style=\"font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-alternates: normal; font-variant-position: normal; white-space-collapse: preserve;\"><b><i><span style=\"font-family: Roboto;\">&nbsp;<\/span><\/i><\/b><\/span><\/p>\n<p style=\"margin: 0in; text-align: justify; background-image: initial; background-position: initial; background-size: initial; background-repeat: initial; background-attachment: initial; background-origin: initial; background-clip: initial;\"><span style=\"font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-alternates: normal; font-variant-position: normal; white-space-collapse: preserve;\"><b><i><span style=\"font-family: Roboto;\">Discrete probability distributions<\/span><\/i><\/b><\/span><span style=\"font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-alternates: normal; font-variant-position: normal; white-space-collapse: preserve;\"><span style=\"font-family: Roboto;\">: These are used when the possible outcomes of a random event are discrete, or separate and distinct, rather than continuous. Examples include the roll of a die, the flip of a coin, and the outcome of a lottery draw. Common discrete probability distributions include the <b>Binomial distribution<\/b>, the <b>Geometric distribution<\/b>, and the <b>Poisson distribution<\/b>.<\/span><\/span><\/p>\n<p style=\"margin: 0in; text-align: justify; background-image: initial; background-position: initial; background-size: initial; background-repeat: initial; background-attachment: initial; background-origin: initial; background-clip: initial;\"><span style=\"font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-alternates: normal; font-variant-position: normal; white-space-collapse: preserve;\"><b><i><span style=\"font-family: Roboto;\">&nbsp;<\/span><\/i><\/b><\/span><\/p>\n<p style=\"margin: 0in; text-align: justify; background-image: initial; background-position: initial; background-size: initial; background-repeat: initial; background-attachment: initial; background-origin: initial; background-clip: initial;\"><span style=\"font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-alternates: normal; font-variant-position: normal; white-space-collapse: preserve;\"><b><i><span style=\"font-family: Roboto;\">Continuous probability distributions<\/span><\/i><\/b><\/span><span style=\"font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-alternates: normal; font-variant-position: normal; white-space-collapse: preserve;\"><span style=\"font-family: Roboto;\">: These are used when the possible outcomes of a random event are continuous, or can take on any value within a certain range. Examples include the height of a person, the weight of an object, and the time it takes for an event to occur. Common continuous probability distributions include the <b>Normal distribution<\/b>, the <b>Uniform distribution<\/b>, and the <b>Exponential distribution<\/b>.<\/span><\/span><\/p>\n<p style=\"margin: 0in; text-align: justify; background-image: initial; background-position: initial; background-size: initial; background-repeat: initial; background-attachment: initial; background-origin: initial; background-clip: initial;\"><span style=\"font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-alternates: normal; font-variant-position: normal; white-space-collapse: preserve;\"><span style=\"font-family: Roboto;\">&nbsp;<\/span><\/span><\/p>\n<p style=\"margin: 0in; text-align: justify; background-image: initial; background-position: initial; background-size: initial; background-repeat: initial; background-attachment: initial; background-origin: initial; background-clip: initial;\"><span style=\"font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-alternates: normal; font-variant-position: normal; white-space-collapse: preserve;\"><b><i><span style=\"font-family: Roboto;\">Multivariate probability distributions<\/span><\/i><\/b><\/span><span style=\"font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-alternates: normal; font-variant-position: normal; white-space-collapse: preserve;\"><span style=\"font-family: Roboto;\">: These are used when there are multiple random variables involved in a single event. Examples include the distribution of height and weight for a group of people, or the distribution of temperatures in different locations. Common multivariate probability distributions include the <b>Multivariate Normal distribution <\/b>and the <b>Multivariate Uniform distribution<\/b>.<\/span><\/span><\/p><p style=\"margin: 0in; text-align: justify; background-image: initial; background-position: initial; background-size: initial; background-repeat: initial; background-attachment: initial; background-origin: initial; background-clip: initial;\"><span style=\"font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-alternates: normal; font-variant-position: normal; white-space-collapse: preserve;\"><span style=\"font-family: Roboto;\"><br><\/span><\/span><\/p><p style=\"margin: 0in; text-align: justify; background-image: initial; background-position: initial; background-size: initial; background-repeat: initial; background-attachment: initial; background-origin: initial; background-clip: initial;\"><span style=\"font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-alternates: normal; font-variant-position: normal; white-space-collapse: preserve;\"><span style=\"font-family: Roboto;\"><br><\/span><\/span><\/p><p style=\"margin: 0in; text-align: justify; background-image: initial; background-position: initial; background-size: initial; background-repeat: initial; background-attachment: initial; background-origin: initial; background-clip: initial;\"><span style=\"font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-alternates: normal; font-variant-position: normal; white-space-collapse: preserve;\"><span style=\"font-family: Roboto;\">In the next three chapters we will learn in detail about 1) Normal distribution, 2) Binomial distribution, and 3) Poisson Distribution. <\/span><\/span><\/p>\n<p style=\"margin: 0in; text-align: justify; background-image: initial; background-position: initial; background-size: initial; background-repeat: initial; background-attachment: initial; background-origin: initial; background-clip: initial;\"><span style=\"font-variant-numeric: normal; font-variant-east-asian: normal; font-variant-alternates: normal; font-variant-position: normal; white-space-collapse: preserve;\"><span style=\"font-family: Roboto;\"><br><\/span><\/span><\/p>\n<p style=\"font-size: 15px; font-style: normal; font-weight: 400; margin-bottom: 0in; text-align: justify; line-height: normal;\"><br><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-1462ec5 e-flex e-con-boxed e-con e-parent\" data-id=\"1462ec5\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div class=\"elementor-element elementor-element-50a7581 e-flex e-con-boxed e-con e-child\" data-id=\"50a7581\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div class=\"elementor-element elementor-element-7d5b22c e-flex e-con-boxed e-con e-child\" data-id=\"7d5b22c\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div class=\"elementor-element elementor-element-00b0e85 e-flex e-con-boxed e-con e-child\" data-id=\"00b0e85\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-4d1f966 elementor-widget elementor-widget-button\" data-id=\"4d1f966\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"https:\/\/myknowledgehub.org\/index.php\/research-methodolgy\/\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-button-text\">Back to the Content<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Probability Distribution Definition and Types In the field of statistics, probability&nbsp;distribution refers to the mathematical function that describes the likelihood&nbsp;of different outcomes occurring in a particular event or experiment.&nbsp;A probability distribution is a function that describes&nbsp;the likelihood of different outcomes in a random event. It provides a way to&nbsp;assign probabilities to each possible outcome of &hellip;<\/p>\n<p class=\"read-more\"> <a class=\"\" href=\"https:\/\/myknowledgehub.org\/index.php\/2023\/10\/30\/research-methodology-chapter-6-5\/\"> <span class=\"screen-reader-text\">Research Methodology Chapter 8.1<\/span> Read More &raquo;<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"set","footnotes":""},"categories":[1],"tags":[],"class_list":["post-1495","post","type-post","status-publish","format-standard","hentry","category-blog"],"aioseo_notices":[],"aioseo_head":"\n\t\t<!-- All in One SEO 4.9.8 - aioseo.com -->\n\t<meta name=\"description\" content=\"Probability Distribution Definition and Types In the field of statistics, probability distribution refers to the mathematical function that describes the likelihood of different outcomes occurring in a particular event or experiment. 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