{"id":8232,"date":"2021-09-28T15:31:04","date_gmt":"2021-09-28T19:31:04","guid":{"rendered":"http:\/\/149.4.100.129\/mqr\/?page_id=8232"},"modified":"2021-09-30T10:18:52","modified_gmt":"2021-09-30T14:18:52","slug":"mqr-descriptive-statistics-statistical-analyses","status":"publish","type":"page","link":"https:\/\/www.qc.cuny.edu\/mqr\/mqr-descriptive-statistics-statistical-analyses\/","title":{"rendered":"Statistics"},"content":{"rendered":"<p>[et_pb_section fb_built=&#8221;1&#8243; _builder_version=&#8221;4.4.5&#8243; custom_padding=&#8221;||54px|||&#8221; global_colors_info=&#8221;{}&#8221;][et_pb_row _builder_version=&#8221;4.9.1&#8243; global_colors_info=&#8221;{}&#8221;][et_pb_column type=&#8221;4_4&#8243; _builder_version=&#8221;4.4.5&#8243; global_colors_info=&#8221;{}&#8221;][dsm_text_divider header=&#8221;Descriptive Statistics &amp; Statistical Analyses&#8221; color=&#8221;#000000&#8243; divider_style=&#8221;double&#8221; divider_weight=&#8221;5px&#8221; _builder_version=&#8221;4.10.8&#8243; _module_preset=&#8221;default&#8221; header_font=&#8221;Open Sans|700|||||||&#8221; header_text_align=&#8221;center&#8221; header_font_size=&#8221;35px&#8221; module_alignment=&#8221;center&#8221; global_colors_info=&#8221;{}&#8221;][\/dsm_text_divider][et_pb_accordion open_toggle_text_color=&#8221;#E71939&#8243; open_toggle_background_color=&#8221;#ffffff&#8221; closed_toggle_background_color=&#8221;#ffffff&#8221; icon_color=&#8221;#e71939&#8243; _builder_version=&#8221;4.10.8&#8243; _module_preset=&#8221;default&#8221; toggle_text_color=&#8221;#E71939&#8243; toggle_font=&#8221;Open Sans|600|||||||&#8221; body_font=&#8221;Open Sans||||||||&#8221; body_text_color=&#8221;#000000&#8243; body_font_size=&#8221;16px&#8221; border_color_all=&#8221;#000000&#8243; global_colors_info=&#8221;{}&#8221;][et_pb_accordion_item title=&#8221;Descriptive Statistics&#8221; open=&#8221;on&#8221; _builder_version=&#8221;4.10.8&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221;]<\/p>\n<p><span style=\"text-decoration: underline\"><strong>Mean\/Median\/Mode<br \/><\/strong><\/span><a href=\"https:\/\/www.khanacademy.org\/math\/ap-statistics\/summarizing-quantitative-data-ap\/measuring-center-quantitative\/v\/statistics-intro-mean-median-and-mode\" target=\"_blank\" rel=\"noopener\">Click here to visit link.<\/a><span style=\"text-decoration: underline\"><strong><br \/><\/strong><\/span><\/p>\n<ul>\n<li>This <strong>video<\/strong> defines the the <strong>mean, median,<\/strong> and <strong>mode.<\/strong> The <strong>mean (average)<\/strong> of a data set is found by adding all numbers in the data set and then dividing by the number of values in the set. The <strong>median<\/strong> is the middle value when a data set is ordered from least to greatest. The <strong>mode<\/strong> is the <strong>number that occurs most frequently in a data set.<\/strong><\/li>\n<\/ul>\n<p><span style=\"text-decoration: underline\"><strong>Mean\/Median\/Mode, Example<br \/><\/strong><\/span><a href=\"https:\/\/www.khanacademy.org\/math\/ap-statistics\/summarizing-quantitative-data-ap\/measuring-center-quantitative\/v\/mean-median-and-mode\" target=\"_blank\" rel=\"noopener\">Click here to visit link.<\/a><span style=\"text-decoration: underline\"><strong><br \/><\/strong><\/span><\/p>\n<ul>\n<li>This <strong>video<\/strong> illustrates how to find the <strong>mean, median,<\/strong> and <strong>mode<\/strong> of a sample data set.<\/li>\n<\/ul>\n<p><span style=\"text-decoration: underline\"><strong>Standard Deviation<br \/><\/strong><\/span><a href=\"https:\/\/www.khanacademy.org\/math\/ap-statistics\/summarizing-quantitative-data-ap\/measuring-spread-quantitative\/v\/sample-standard-deviation-and-bias\" target=\"_blank\" rel=\"noopener\">Click here to visit link.<\/a><span style=\"text-decoration: underline\"><strong><br \/><\/strong><\/span><\/p>\n<ul>\n<li>This <strong>video<\/strong> provides an example of how to calculate <strong>standard deviation<\/strong> and <strong>bias.<\/strong> The <strong>standard deviation<\/strong> is a measure of the amount of <strong>variation<\/strong> or <strong>dispersion<\/strong> of a set of values.<\/li>\n<\/ul>\n<p><span style=\"text-decoration: underline\"><strong>Mean, Median, Mode, Range, and Standard Deviation<br \/><\/strong><\/span><a href=\"https:\/\/www.youtube.com\/watch?v=mk8tOD0t8M0\" target=\"_blank\" rel=\"noopener\">Click here to visit link.<\/a><span style=\"text-decoration: underline\"><strong><br \/><\/strong><\/span><\/p>\n<ul>\n<li>This <strong>video<\/strong> defines and provides examples to find the <strong>mean, median, mode, range,<\/strong> and <strong>standard deviation<\/strong> of a data set.<\/li>\n<\/ul>\n<p><span style=\"text-decoration: underline\"><strong>Confidence Intervals and Margin of Error<br \/><\/strong><\/span><a href=\"https:\/\/www.khanacademy.org\/math\/ap-statistics\/xfb5d8e68:inference-categorical-proportions\/introduction-confidence-intervals\/v\/confidence-intervals-and-margin-of-error\" target=\"_blank\" rel=\"noopener\">Click here to visit link.<\/a><span style=\"text-decoration: underline\"><strong><br \/><\/strong><\/span><\/p>\n<ul>\n<li>This <strong>video<\/strong> defines the <strong>margin of error<\/strong> as <strong>how far from the estimate the true value might be, in either direction.<\/strong> The <strong>confidence interval<\/strong> is the <strong>estimate \u00b1<\/strong> the <strong>margin of error.<\/strong> It also applies these terms to a practical <strong>QR example: a runoff in an election.<\/strong><\/li>\n<\/ul>\n<p><span style=\"text-decoration: underline\"><strong>p-Values and Hypothesis Testing<br \/><\/strong><\/span><a href=\"https:\/\/www.youtube.com\/watch?v=8Aw45HN5lnA\" target=\"_blank\" rel=\"noopener\">Click here to visit link.<\/a><span style=\"text-decoration: underline\"><strong><br \/><\/strong><\/span><\/p>\n<ul>\n<li>This <strong>video<\/strong> explains how to use the <strong>p-value<\/strong> to solve problems with <strong>hypothesis testing.<\/strong> When the <strong>p-value is less than alpha,<\/strong> the <strong>null hypothesis is rejected<\/strong> and <strong>vice versa.<\/strong> A simple way to remember this is: &#8216;&#8221;<strong>If the p is low, the null must go!<\/strong>&#8221; It also discusses when to use a <strong>one tailed test<\/strong> compared to a <strong>two tailed test.<\/strong><\/li>\n<\/ul>\n<p><span style=\"text-decoration: underline\"><strong>Interpreting Confidence Levels, Example<br \/><\/strong><\/span><a href=\"https:\/\/www.khanacademy.org\/math\/ap-statistics\/estimating-confidence-ap\/introduction-confidence-intervals\/v\/interpreting-confidence-intervals-example\" target=\"_blank\" rel=\"noopener\">Click here to visit link.<\/a><span style=\"text-decoration: underline\"><strong><br \/><\/strong><\/span><\/p>\n<ul>\n<li>This <strong>video<\/strong> provides a <strong>numerical example<\/strong> using a <strong>sample mean and standard deviation<\/strong> and a <strong>90% confidence interval.<\/strong><\/li>\n<\/ul>\n<p><span style=\"text-decoration: underline\"><strong>p-Values and Significance Tests<br \/><\/strong><\/span><a href=\"https:\/\/www.youtube.com\/watch?v=KS6KEWaoOOE\" target=\"_blank\" rel=\"noopener\">Click here to visit link.<\/a><\/p>\n<ul>\n<li>This <strong>video<\/strong> explains significance testing, using a <strong>p value = 0.05.<\/strong> It explicates the following:<\/li>\n<li><strong>p &gt; 0.05<\/strong> is the probability that the <strong>null hypothesis<\/strong> is <strong>true.<\/strong><\/li>\n<li><strong>(1 &#8211; p value)<\/strong> is the probability that the<strong> alternative hypothesis<\/strong> is <strong>true.<\/strong><\/li>\n<li>A<strong> statistically significant<\/strong> test result (<strong>p \u2264 0.05<\/strong>) means that the <strong>test hypothesis is false<\/strong> or should be <strong>rejected.<\/strong><\/li>\n<li><strong>A p value greater<\/strong> than <strong>0.05<\/strong> means that <strong>no effect was observed.<\/strong><\/li>\n<\/ul>\n<p><span style=\"text-decoration: underline\"><strong>General Statistics Resources<br \/><\/strong><\/span><a href=\"https:\/\/onlinestatbook.com\/2\/index.html\" target=\"_blank\" rel=\"noopener\">Click here to visit link.<\/a><span style=\"text-decoration: underline\"><strong><br \/><\/strong><\/span><\/p>\n<ul>\n<li>This link provides various <strong>study guides<\/strong> and <strong>video tutorials<\/strong> for a wide range of topics in <strong>Statistics.<\/strong><\/li>\n<\/ul>\n<p>[\/et_pb_accordion_item][et_pb_accordion_item title=&#8221;Statistical Analyses&#8221; _builder_version=&#8221;4.10.8&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221; open=&#8221;off&#8221;]<\/p>\n<p><span style=\"text-decoration: underline\"><strong>Correlation vs. Causation<br \/><\/strong><\/span><a href=\"https:\/\/www.khanacademy.org\/math\/probability\/scatterplots-a1\/creating-interpreting-scatterplots\/v\/correlation-and-causality\" target=\"_blank\" rel=\"noopener\">Click here to visit link.<\/a><span style=\"text-decoration: underline\"><strong><br \/><\/strong><\/span><\/p>\n<ul>\n<li>This <strong>video<\/strong> highlights the difference between <strong>correlation<\/strong> and <strong>causation,<\/strong> and explains why <strong>correlation does not imply causality.<\/strong><\/li>\n<\/ul>\n<p><span style=\"text-decoration: underline\"><strong>Calculating r, the Correlation Coefficient<br \/><\/strong><\/span><a href=\"https:\/\/www.khanacademy.org\/math\/ap-statistics\/bivariate-data-ap\/correlation-coefficient-r\/v\/calculating-correlation-coefficient-r\" target=\"_blank\" rel=\"noopener\">Click here to visit link.<\/a><span style=\"text-decoration: underline\"><strong><br \/><\/strong><\/span><\/p>\n<ul>\n<li>This <strong>video<\/strong> explains how to calculate the <strong>correlation coefficient, r,<\/strong> which measures the <strong>strength<\/strong> and <strong>direction<\/strong> of a <strong>linear relationship<\/strong> between <strong>two variables<\/strong> on a <strong>scatterplot.<\/strong><\/li>\n<\/ul>\n<p><span style=\"text-decoration: underline\"><strong>Chi-Square Distribution<br \/><\/strong><\/span><a href=\"https:\/\/www.khanacademy.org\/math\/statistics-probability\/inference-categorical-data-chi-square-tests\/chi-square-goodness-of-fit-tests\/v\/chi-square-distribution-introduction\" target=\"_blank\" rel=\"noopener\">Click here to visit link.<\/a><span style=\"text-decoration: underline\"><strong><br \/><\/strong><\/span><\/p>\n<ul>\n<li>This <strong>video<\/strong> provides a comprehensive explanation to the <strong>chi-square distribution,<\/strong> which is used to examine the <strong>differences between categorical variables in the same population.<\/strong><\/li>\n<\/ul>\n<p><span style=\"text-decoration: underline\"><strong>Chi-Square Statistic for Hypothesis Testing<br \/><\/strong><\/span><a href=\"https:\/\/www.khanacademy.org\/math\/statistics-probability\/inference-categorical-data-chi-square-tests\/chi-square-goodness-of-fit-tests\/v\/chi-square-statistic\" target=\"_blank\" rel=\"noopener\">Click here to visit link.<\/a><span style=\"text-decoration: underline\"><strong><br \/><\/strong><\/span><\/p>\n<ul>\n<li>This <strong>video<\/strong> defines the<strong> chi-square statistic<\/strong> as the <strong>square of the difference between the observed (o) and expected (e) values divided by the expected value.<\/strong> It also provides a <strong>numerical example<\/strong> applying the <strong>chi-square statistic<\/strong> to <strong>hypothesis testing.<\/strong><\/li>\n<\/ul>\n<p><span style=\"text-decoration: underline\"><strong>Linear Regression<br \/><\/strong><\/span><a href=\"https:\/\/www.youtube.com\/watch?v=WWqE7YHR4Jc\" target=\"_blank\" rel=\"noopener\">Click here to visit link.<\/a><span style=\"text-decoration: underline\"><strong><br \/><\/strong><\/span><\/p>\n<ul>\n<li><strong>This video<\/strong> defines <strong>linear regression<\/strong> as a <strong>linear<\/strong> approach to <strong>modeling<\/strong> the relationship between a <strong>dependent variable<\/strong> (a <strong>scalar response<\/strong>) and one or more <strong>independent variables<\/strong> (<strong>explanatory variables<\/strong>). It also defines: <strong>outliers, F-statistic, total sums of squares, sums of squares for regression,<\/strong> and <strong>sums of squares for error.<\/strong><\/li>\n<\/ul>\n<p><span style=\"text-decoration: underline\"><strong>Linear Regression, Example<br \/><\/strong><\/span><a href=\"https:\/\/www.khanacademy.org\/math\/statistics-probability\/describing-relationships-quantitative-data\/more-on-regression\/v\/regression-line-example\" target=\"_blank\" rel=\"noopener\">Click here to visit link.<\/a><span style=\"text-decoration: underline\"><strong><br \/><\/strong><\/span><\/p>\n<ul>\n<li>This <strong>video<\/strong> applies <strong>linear regression<\/strong> to a <strong>numerical example.<\/strong><\/li>\n<\/ul>\n<p><span style=\"text-decoration: underline\"><strong>Levels of Measurement (Nominal, Ordinal, Interval, Ratio)<br \/><\/strong><\/span><a href=\"https:\/\/www.youtube.com\/watch?v=LPHYPXBK_ks\" target=\"_blank\" rel=\"noopener\">Click here to visit link.<\/a><span style=\"text-decoration: underline\"><strong><br \/><\/strong><\/span><\/p>\n<ul>\n<li>This link is a <strong>video<\/strong> tutorial which distinguishes between the <strong>nominal, ordinal, interval,<\/strong> and <strong>ratio<\/strong> scales of measurement. <strong>Nominal<\/strong> data is <strong>named<\/strong> data which can be <strong>separated into discrete categories which do not overlap<\/strong> (i.e. eye color). <strong>Ordinal<\/strong> data is data which is <strong>placed into some kind of order or scale<\/strong> (i.e. rating customer satisfaction on a scale from 1-10). <strong>Interval<\/strong> data is data which comes in the form of <strong>a numerical value where the difference between points is standardized and meaningful<\/strong> (i.e. temperature). <strong>Ratio<\/strong> data is much like interval data \u2013 it must be <strong>numerical values where the difference between points is standardized and meaningful,<\/strong> but it also must have a<strong> true zero\/no negative values<\/strong> (i.e. height).<\/li>\n<\/ul>\n<p><span style=\"text-decoration: underline\"><strong>The Difference Between Levels of Measurement (Nominal, Ordinal, Interval, Ratio)<br \/><\/strong><\/span><a href=\"https:\/\/www.youtube.com\/watch?v=LuBD49SFpWs\" target=\"_blank\" rel=\"noopener\">Click here to visit link.<\/a><\/p>\n<ul>\n<li>This <strong>video<\/strong> defines and provides <strong>examples<\/strong> of the <strong>nominal, ordinal, interval,<\/strong> and <strong>ratio<\/strong> scales of measurement.<\/li>\n<\/ul>\n<p>[\/et_pb_accordion_item][et_pb_accordion_item title=&#8221;Creating, Reading &amp; Interpreting Different Types of Graphs &amp; Tables&#8221; _builder_version=&#8221;4.10.8&#8243; _module_preset=&#8221;default&#8221; global_colors_info=&#8221;{}&#8221; open=&#8221;off&#8221;]<\/p>\n<p><span style=\"text-decoration: underline\"><strong>Creating a Bar Graph<br \/><\/strong><\/span><a href=\"https:\/\/www.khanacademy.org\/math\/ap-statistics\/analyzing-categorical-ap\/analyzing-one-categorical-variable\/v\/creating-bar-charts-1\" target=\"_blank\" rel=\"noopener\">Click here to visit link.<\/a><span style=\"text-decoration: underline\"><strong><br \/><\/strong><\/span><\/p>\n<ul>\n<li>This <strong>video<\/strong> explicates how to create a <strong>bar graph,<\/strong> which presents <strong>categorical data with rectangular bars,<\/strong> using <strong>data<\/strong> from a <strong>survey.<\/strong><\/li>\n<\/ul>\n<p><span style=\"text-decoration: underline\"><strong>Histograms<br \/><\/strong><\/span><a href=\"https:\/\/www.khanacademy.org\/math\/pre-algebra\/pre-algebra-math-reasoning\/pre-algebra-picture-bar-graphs\/v\/histograms\" target=\"_blank\" rel=\"noopener\">Click here to visit link.<\/a><span style=\"text-decoration: underline\"><strong><br \/><\/strong><\/span><\/p>\n<ul>\n<li>This <strong>video<\/strong> illustrates how and when to use <strong>histograms<\/strong> to visualize the <strong>frequency distribution<\/strong> of a data set.<\/li>\n<\/ul>\n<p><span style=\"text-decoration: underline\"><strong>Line Graphs<br \/><\/strong><\/span><a href=\"https:\/\/www.khanacademy.org\/math\/statistics-probability\/displaying-describing-data\/more-on-data-displays\/v\/u08-l1-t2-we2-reading-line-graphs\" target=\"_blank\" rel=\"noopener\">Click here to visit link.<\/a><span style=\"text-decoration: underline\"><strong><br \/><\/strong><\/span><\/p>\n<ul>\n<li>This <strong>video<\/strong> explains how and when to use a <strong>line graph<\/strong> to visually represent data, particularly <strong>data that changes over time.<\/strong><\/li>\n<\/ul>\n<p><span style=\"text-decoration: underline\"><strong>Pie Charts<br \/><\/strong><\/span><a href=\"https:\/\/www.khanacademy.org\/math\/statistics-probability\/analyzing-categorical-data\/one-categorical-variable\/v\/reading-pie-graphs-circle-graphs\" target=\"_blank\" rel=\"noopener\">Click here to visit link.<\/a><span style=\"text-decoration: underline\"><strong><br \/><\/strong><\/span><\/p>\n<ul>\n<li>This <strong>video<\/strong> illustrates how and when to use <strong>pie charts<\/strong> to visualize data. <strong>Pie charts<\/strong> are <strong>circular charts<\/strong> divided up into <strong>segments<\/strong> (or <strong>&#8220;slices&#8221;<\/strong>) which <strong>each represent a value.<\/strong><\/li>\n<\/ul>\n<p><span style=\"text-decoration: underline\"><strong>Scatter Plots<br \/><\/strong><\/span><a href=\"https:\/\/www.khanacademy.org\/math\/cc-eighth-grade-math\/cc-8th-data\/cc-8th-scatter-plots\/v\/constructing-scatter-plot\" target=\"_blank\" rel=\"noopener\">Click here to visit link.<\/a><span style=\"text-decoration: underline\"><strong><br \/><\/strong><\/span><\/p>\n<ul>\n<li>This <strong>video<\/strong> shows how to construct and read <strong>scatter plots,<\/strong> which are used to <strong>observe relationships between variables.<\/strong><\/li>\n<\/ul>\n<p><span style=\"text-decoration: underline\"><strong>Box and Whisker Plots<br \/><\/strong><\/span><a href=\"https:\/\/www.khanacademy.org\/math\/statistics-probability\/summarizing-quantitative-data\/box-whisker-plots\/v\/box-and-whisker-plot-exercise-example\" target=\"_blank\" rel=\"noopener\">Click here to visit link.<\/a><span style=\"text-decoration: underline\"><strong><br \/><\/strong><\/span><\/p>\n<ul>\n<li>This <strong>video<\/strong> explains how to read and construct <strong>box and whisker plots<\/strong> (a <strong>five-number summary<\/strong> of a set of data), which are used to graphically depict groups of numerical data through their <strong>quartiles.<\/strong><\/li>\n<\/ul>\n<p><span style=\"text-decoration: underline\"><strong>Frequency Tables &amp; Dot Plots<br \/><\/strong><\/span><a href=\"https:\/\/www.khanacademy.org\/math\/ap-statistics\/quantitative-data-ap\/frequency-tables-dot-plots\/v\/frequency-tables-and-dot-plots\" target=\"_blank\" rel=\"noopener\">Click here to visit link.<\/a><span style=\"text-decoration: underline\"><strong><br \/><\/strong><\/span><\/p>\n<ul>\n<li>This <strong>video<\/strong> explains how to organize data into <strong>frequency tables<\/strong> and<strong> dot (line) plots.<\/strong><\/li>\n<\/ul>\n<p><span style=\"text-decoration: underline\"><strong>The Difference between Linear and Logarithmic Scales<br \/><\/strong><\/span><a href=\"https:\/\/www.khanacademy.org\/math\/algebra-home\/alg-exp-and-log\/alg-logarithmic-scale\/v\/logarithmic-scale\" target=\"_blank\" rel=\"noopener\">Click here to visit link.<\/a><span style=\"text-decoration: underline\"><strong><br \/><\/strong><\/span><\/p>\n<ul>\n<li>This <strong>video<\/strong> explains the <strong>difference<\/strong> between a <strong>linear scale<\/strong> and a <strong>logarithmic scale<\/strong>. On a<strong> linear scale,<\/strong> the<strong> value between any two points will never change.<\/strong> A <strong>logarithmic scale<\/strong> is one in which the units on the <strong>axis are powers,<\/strong> or <strong>logarithms,<\/strong> of a base number. <strong>Exponential growth curves<\/strong> are displayed on a <strong>logarithmic scale.<\/strong><\/li>\n<\/ul>\n<p>[\/et_pb_accordion_item][\/et_pb_accordion][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>\n","protected":false},"excerpt":{"rendered":"<p><div class=\"et_pb_module dsm_text_divider dsm_text_divider_0\">\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t<div class=\"et_pb_module_inner\">\n\t\t\t\t\t<div class=\"dsm-text-divider-wrapper dsm-text-divider-align-center et_pb_bg_layout_light\">\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t<div class=\"dsm-text-divider-before dsm-divider\"><\/div>\n\t\t\t\t<h3 class=\"dsm-text-divider-header et_pb_module_header\"><span>Descriptive Statistics &amp; Statistical Analyses<\/span><\/h3>\n\t\t\t\t<div class=\"dsm-text-divider-after dsm-divider\"><\/div>\n\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t<\/div>Mean\/Median\/ModeClick here to visit link. This video defines the the mean, median, and mode. The mean (average) of a data set is found by adding all numbers in the data set and then dividing by the number of values [&hellip;]<\/p>\n","protected":false},"author":4,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"","_et_gb_content_width":"","inline_featured_image":false,"footnotes":""},"page_category":[],"wf_page_folders":[235],"class_list":["post-8232","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/www.qc.cuny.edu\/mqr\/wp-json\/wp\/v2\/pages\/8232","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.qc.cuny.edu\/mqr\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.qc.cuny.edu\/mqr\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.qc.cuny.edu\/mqr\/wp-json\/wp\/v2\/users\/4"}],"replies":[{"embeddable":true,"href":"https:\/\/www.qc.cuny.edu\/mqr\/wp-json\/wp\/v2\/comments?post=8232"}],"version-history":[{"count":13,"href":"https:\/\/www.qc.cuny.edu\/mqr\/wp-json\/wp\/v2\/pages\/8232\/revisions"}],"predecessor-version":[{"id":8430,"href":"https:\/\/www.qc.cuny.edu\/mqr\/wp-json\/wp\/v2\/pages\/8232\/revisions\/8430"}],"wp:attachment":[{"href":"https:\/\/www.qc.cuny.edu\/mqr\/wp-json\/wp\/v2\/media?parent=8232"}],"wp:term":[{"taxonomy":"page_category","embeddable":true,"href":"https:\/\/www.qc.cuny.edu\/mqr\/wp-json\/wp\/v2\/page_category?post=8232"},{"taxonomy":"wf_page_folders","embeddable":true,"href":"https:\/\/www.qc.cuny.edu\/mqr\/wp-json\/wp\/v2\/wf_page_folders?post=8232"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}