[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-448376-105":59,"doc-detail-448376-en":131},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":124,"head_meta":126,"extra_data":128,"updated_unix":130},105,"en","ap-statistics-course-review-study-guide-unit-review","AP Statistics Course Review Study Guide - Unit Review","","AP Statistics Course Review Study Guide covering key concepts across four units: descriptive statistics and distributions, two-variable relationships with least-squares regression and model straightening, experimental designs and study bias, and probability distributions for both continuous and discrete variables. Emphasizes SOCS for describing distributions, identifying and interpreting outliers, standardizing normal data using z-scores, evaluating regression via residual patterns, and understanding how sampling methods and blinding reduce bias. Includes core formulas and settings for uniform, normal, t, chi-squared, binomial, and geometric distributions.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":20,"@type":76,"position":81},"https://docshare.wps.com/document/exam/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/ap-statistics-course-review-study-guide-unit-review/448376/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/ap-statistics-course-review-study-guide-unit-review/448376.png","ImageObject",300,407,{"name":92,"@type":93},"SANS","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-04","2026-09-30",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",12,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"How do you describe a distribution using SOCS?","Question",{"text":113,"@type":114},"Use SOCS: Shape, Outliers/unusual values, Center, and Spread. If comparing distributions, use appropriate comparison language for each SOCS component.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"When is a point considered an outlier?",{"text":118,"@type":114},"A data point is an outlier if it is less than Q1 - 1.5IQR or greater than Q3 + 1.5IQR.",{"name":120,"@type":111,"acceptedAnswer":121},"What does a random residual pattern indicate in regression?",{"text":122,"@type":114},"If residuals are in a random pattern (no pattern), the line is a good fit to the data. If residuals display a pattern, the relationship is not linear.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},448376,1790963744,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":19,"category_name":20,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":145,"read_time":146,"preview_page_count":4},962090760505,"https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d","Unit 1: One-variable data AP Statistics Course Review Study Guide  \nStatistics Facts: Descriptive Statistics  \nDescribing distributions: SOCS = Shape, Outliers/unusual, Center, Spread (use comparison language if comparing)  \nOutliers: Data point is an outlier if it is 􀀁 Q1􀀂1.5IQR or 􀀃 Q3 􀀄1.5IQR For data that follows N􀀅 􀀁 , 􀀂 􀀆 an outlier is a point more (or less) than 􀀁 􀀇 2􀀂  \nStandardizing Normal data: If N􀀅 􀀁 , 􀀂 􀀆 we can standardize to N􀀅 0,1􀀆  \nby finding z-score:  \nStandard deviation: Measures the average distance between individual data values and their mean.  \nUnit 2: Two-variable (x-y) data  \nRegression:  \nLeast-Squares Regression Line (LSRL): or  \nr: correlation coefficient (no units) 􀀂1 􀀈 r 􀀈 1  \nb 􀀉 r ~~ ~~ssy~~ ~~ (given on AP formula sheet)  \nx  \nr2 : coefficient of determination (fraction or percent of variation in y that is explained by the LSRL)  \nIf a line is a good fit to data, then residuals are in a random pattern (no pattern) .  \nIf residuals display a pattern  \n then data is not linear and follows:  \nStraightening non-linear data:  \n Exponential model 􀀅 y 􀀉 ax􀀆 a  \ny 􀀉 x  \nlog y 􀀉 a log x 􀀁 We straighten data by taking logs log of x and y  \n-or  \nPower model 􀀅 y 􀀉 xa􀀆  \nx  \ny 􀀉 a  \nlogy 􀀉 x log a  \nlog of y only  \nUnit 3: Experiments, Studies, Biases  \nObservational Study: No treatment is imposed.  \nExperimental Study: A treatment is imposed.  \nNo cause/effect relationship can be concluded from an observational study. Why not? Correlation does not imply association due to possible lurking variables.  \nSampling: Selecting a portion of a population for analysis.  \nSimple Random Sample (SRS) : Each set of n individuals has an equal chance of being selected.  \nBest way to obtain: Draw names from a hat.  \nRandom Sample: Each individual has an equal chance of being selected.  \nStratified Random Sample: Population is divided into groups or strata, take SRSs from each stratum (Stratified is used when you expect some difference between strata and want to include some from each) .  \nCluster Sample: Population is divided into non-homogeneous groups called clusters. SRS take from some of the clusters (usually clusters separated by geographic location) . (Cluster is used when you don’t expect difference between clusters, but want to subdivide for convenience.)  \nSystematic Sample: Employing an algorithm for selecting e.g. choose every 5th individual from a list.  \nConvenience Sample: A potentially biased sample which was taken in some way ‘convenient’ to the researchers (e.g. everyone who leave a particular building, researchers ask everyone in their neighborhood) .  \nBias: Anything which causes a sample to be not representative of the population from which it is sampled.  \nVoluntary response: Asking for volunteers instead of selecting the participants.  \nNon-response: Researchers choose the participants, but they may choose not to participate.  \nResponse bias: Anything in the survey design or procedures which might induce a particular response (attractive interviewer, boss will find out answers, wording of survey questions)  \nUndercoverage bias: Anything which results in some portion of the population not being included in the right proportion (landline phones for survey, surveying only North part of city)  \nExperiment Designs: (Single blind, double blind – placebos aide in blinding)  \nCompletely randomized  \nBlocked randomized  \nMatched pair  \nCompare differences before and after treatment or [pre vs. post](pre vs. post) test  \nNote: We block on differences we know about, and we randomize to take care of differences we don’t know about.  \nUnit 4: Probability and Data Analysis Distributions (Continuous variables)  \n1) Uniform:  All events equally likely  \n2) Normal:  \nstandardized:  \n3) Student t-distribution: 4) Chi-squared 􀀃 2 :  \n-More area in tails than Normal.  \n-Depends upon degrees of freedom  \n-As n (df) increases, approaches a Normal distribution.  \n-Depends upon df.  \n-Skewed right.  \n-Mode is at df-2.  \n-","cbCaia5SaTX1BsPd","https://ap.wps.com/l/cbCaia5SaTX1BsPd","pdf",2477908,14,"English","# Unit 1: One-variable data\n## Statistics Facts: Descriptive Statistics\n## Describing Distributions (SOCS)\n## Outliers and IQR\n## Standardizing Normal Data (z-score)\n## Standard Deviation\n# Unit 2: Two-variable (x-y) data\n## Regression and Least-Squares Regression Line (LSRL)\n## Correlation and r, r²\n## Residual Patterns and Linearity\n## Straightening Non-linear Data (Exponential and Power Models)\n# Unit 3: Experiments, Studies, Biases\n## Observational vs. Experimental Studies\n## Sampling Methods (SRS, Stratified, Cluster, Systematic, Convenience)\n## Bias Types (voluntary response, non-response, response bias, undercoverage)\n## Experiment Designs (single blind, double blind)\n## Randomization and Blocking (completely randomized, blocked randomized, matched pair)\n## Pre vs. Post Testing\n# Unit 4: Probability and Data Analysis Distributions\n## Continuous Distributions (Uniform, Normal, Student t, Chi-squared)\n## Discrete Distributions (Binomial, Geometric)","[{\"question\":\"How do you describe a distribution using SOCS?\",\"answer\":\"Use SOCS: Shape, Outliers/unusual values, Center, and Spread. If comparing distributions, use appropriate comparison language for each SOCS component.\"},{\"question\":\"When is a point considered an outlier?\",\"answer\":\"A data point is an outlier if it is less than Q1 - 1.5IQR or greater than Q3 + 1.5IQR.\"},{\"question\":\"What does a random residual pattern indicate in regression?\",\"answer\":\"If residuals are in a random pattern (no pattern), the line is a good fit to the data. If residuals display a pattern, the relationship is not linear.\"}]","AP Statistics Course Review Study Guide - Unit Review | PDF",1790726786,35]