[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-1-en-105":3,"doc-seo-572080-105":53,"doc-detail-572080-en":127},{"code":4,"msg":5,"data":6},0,"success",[7,14,19,24,29,34,39,44,49],{"id":8,"doc_module":9,"doc_module_name":10,"category_name":11,"show_sort_weight":12,"slug":13},11,1,"Template","Presentations",90,"presentations",{"id":15,"doc_module":9,"doc_module_name":10,"category_name":16,"show_sort_weight":17,"slug":18},12,"Resumes",80,"resumes",{"id":20,"doc_module":9,"doc_module_name":10,"category_name":21,"show_sort_weight":22,"slug":23},14,"Invoices",70,"invoices",{"id":25,"doc_module":9,"doc_module_name":10,"category_name":26,"show_sort_weight":27,"slug":28},15,"Posters",60,"posters",{"id":30,"doc_module":9,"doc_module_name":10,"category_name":31,"show_sort_weight":32,"slug":33},16,"Social Media",50,"social-media",{"id":35,"doc_module":9,"doc_module_name":10,"category_name":36,"show_sort_weight":37,"slug":38},17,"Forms",40,"forms",{"id":40,"doc_module":9,"doc_module_name":10,"category_name":41,"show_sort_weight":42,"slug":43},18,"Letters",30,"letters",{"id":45,"doc_module":9,"doc_module_name":10,"category_name":46,"show_sort_weight":47,"slug":48},21,"Paper Templates",5,"papers-templates",{"id":50,"doc_module":9,"doc_module_name":10,"category_name":51,"show_sort_weight":4,"slug":52},158,"General","general-158",{"code":4,"msg":54,"data":55},"ok",{"site_id":56,"language":57,"slug":58,"title":59,"keywords":60,"description":61,"schema_data":62,"social_meta":120,"head_meta":122,"extra_data":124,"updated_unix":126},105,"en","important-concepts-not-on-the-ap-statistics-formula-sheet-part-i-part-ii-summary-notes","Important Concepts not on the AP Statistics Formula Sheet - Part I - Part II - Summary Notes","","Reference-style notes covering AP Statistics concepts missing from the formula sheet. Part I summarizes key data-analysis tools and descriptions, including outlier testing with IQR, effects of linear transformations on center and spread, and how to report center/spread/shape using histograms, skewness, boxplots, stem-and-leaf, Ogive, and normal probability plots. It also reviews standard deviation from the mean, correlation (r, r²), residuals and least-squares line, exponential and power models, and variable roles (EV/RV) plus lurking and confounding variables. Part II explains sampling methods and experimental design terms.",{"@graph":63,"@context":119},[64,80,102],{"@type":65,"itemListElement":66},"BreadcrumbList",[67,71,74,77],{"item":68,"name":69,"@type":70,"position":9},"https://docshare.wps.com","Home","ListItem",{"item":72,"name":10,"@type":70,"position":73},"https://docshare.wps.com/template/",2,{"item":75,"name":51,"@type":70,"position":76},"https://docshare.wps.com/template/general/",3,{"item":78,"name":59,"@type":70,"position":79},"https://docshare.wps.com/template/important-concepts-not-on-the-ap-statistics-formula-sheet-part-i-part-ii-summary-notes/572080/",4,{"url":78,"name":59,"@type":81,"image":82,"author":87,"headline":59,"publisher":90,"fileFormat":93,"inLanguage":57,"description":61,"dateModified":94,"datePublished":95,"encodingFormat":93,"isAccessibleForFree":96,"interactionStatistic":97},"DigitalDocument",{"url":83,"@type":84,"width":85,"height":86},"https://docshare.wps.com/thumbnails/important-concepts-not-on-the-ap-statistics-formula-sheet-part-i-part-ii-summary-notes/572080.png","ImageObject",442,249,{"name":88,"@type":89},"nayy☆","Person",{"url":68,"name":91,"@type":92},"DocShare","Organization","application/pdf","2026-10-22","2026-10-13",true,{"@type":98,"interactionType":99,"userInteractionCount":101},"InteractionCounter",{"@type":100},"ViewAction",6,{"@type":103,"mainEntity":104},"FAQPage",[105,111,115],{"name":106,"@type":107,"acceptedAnswer":108},"How do you test for an outlier using the IQR method?","Question",{"text":109,"@type":110},"Compute IQR = Q3 - Q1, then flag values more than 1.5(IQR) above Q3 or below Q1 as outliers.","Answer",{"name":112,"@type":107,"acceptedAnswer":113},"What changes in a data distribution when you apply linear transformations?",{"text":114,"@type":110},"Addition affects the center but not the spread, while multiplication affects both the center and the spread.",{"name":116,"@type":107,"acceptedAnswer":117},"What is the difference between stratified sampling and cluster sampling?",{"text":118,"@type":110},"Stratified sampling includes subjects from each stratum, whereas cluster sampling surveys only individuals within selected clusters.","https://schema.org",{"og:url":78,"og:type":121,"og:title":59,"og:site_name":91,"og:description":61},"article",{"robots":123,"canonical":78},"index,follow",{"doc_id":125,"site_id":56},572080,1792099248,{"code":4,"msg":5,"data":128},{"doc_id":125,"user_id":129,"nickname":88,"user_avatar":130,"doc_module":9,"category_id":50,"category_name":51,"doc_title":59,"doc_description":61,"doc_content":131,"file_id":132,"file_url":133,"file_type":134,"file_size":135,"view_count":101,"is_deleted":4,"is_public":9,"is_downloadable":9,"audit_status":9,"page_count":20,"language":136,"language_code":57,"site_id":56,"html_lang":57,"table_of_contents":137,"faqs":138,"seo_title":139,"seo_description":61,"update_tm":140,"read_time":47},962090893153,"https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc","Important Concepts not on the AP Statistics Formula Sheet  \nPart I:  \n\n| IQR = Q3 – Q1\u003Cbr>Test for an outlier:\u003Cbr>1.5(IQR) above Q3 or below Q1\u003Cbr>The calculator will run the test for you as long as you choose the boxplot with theoulier on it in STATPLOT | Linear transformation: Addition: affects center NOT spread\u003Cbr>adds to , M, Q1 , Q3, IQR not σ\u003Cbr>Multiplication: affects both center and spread\u003Cbr>multiplies , M, Q1 , Q3, IQR, σ | When describing data: describe\u003Cbr>center, spread, and shape.\u003Cbr>Give a 5 number summary or mean and standard deviation when necessary. | Histogram:\u003Cbr>fairly symmetrical unimodal\u003Cbr>|\n| --- | --- | --- | --- |\n| skewed right\u003Cbr>| Skewed left\u003Cbr>| Ogive (cumulative frequency)\u003Cbr>| Boxplot (with an outlier)\u003Cbr>|\n| Stem and leaf\u003Cbr>| Normal Probability Plot\u003Cbr>\u003Cbr>The 80th percentile means that 80% of the data is below that observation. | \u003Cbr>HOW MANY STANDARD DEVIATIONS AN OBSERVATION IS FROM THE MEAN\u003Cbr>68-95-99.7 Rule for Normality N(µ,σ)\u003Cbr>N(0,1) Standard Normal | r: correlation coefficient, The strength of the linear relationship of data. Close to 1 or-1 is very close to linear\u003Cbr>r2 : coefficient of determination. How well the model fits the data. Close to 1 is a good fit.“Percent of variation in y described by the LSRL on x” |\n| residual = \u003Cbr>residual =\u003Cbr>observed – predicted\u003Cbr>y = a+bx\u003Cbr>Slope of LSRL(b): rate of change in y for every unit x\u003Cbr>y-intercept of LSRL(a): y when x = 0 | Exponential Model: y = abx take log ofy\u003Cbr>Power Model:\u003Cbr>y = axb take log ofx and y | Explanatory variables explain changes in response variables. EV: x, independent\u003Cbr>RV: y, dependent | Lurking Variable: A variable that may influence the relationship bewteen two variables. LV is not among the EV’s |\n| Confounding: two variables are confounded when the effects of an RV cannot be distinguished. | | | |\n\nPart II: Designing Experiments and Collecting Data:  \nSampling Methods:  \nThe Bad:  \nVoluntary sample. A voluntary sample is made up of people who decide for themselves to be in the survey.  \nExample: Online poll  \nConvenience sample. A convenience sample is made up of people who are easy to reach.  \nExample: interview people at the mall, or in the cafeteria because it is an easy place to reach people.  \nThe Good:  \nSimple random sampling. Simple random sampling refers to a method in which all possible samples of n objects are equally likely to occur.  \nExample: assign a number 1-100 to all members ofa population of size 100. One number is selected at a time from a list of random digits or using a random number generator. The first 10 selected are the sample.  \nStratified sampling. With stratified sampling, the population is divided into groups, based on some characteristic. Then, within each group, a SRS is taken. In stratified sampling, the groups are called strata.  \nExample: For a national survey we divide the population into groups or strata, based on geography-north, east, south, and west. Then, within each stratum, we might randomly select survey respondents.  \nCluster sampling. With cluster sampling, every member of the population is assigned to one, and only one, group. Each group is called a cluster. A sample of clusters is chosen using a SRS. Only individuals within sampled clusters are surveyed.  \nExample: Randomly choose high schools in the country and only survey people in those schools.  \nDifference between cluster sampling and stratified sampling. With stratified sampling, the sample includes subjects from each stratum. With cluster sampling the sample includes subjects only from sampled clusters.  \nMultistage sampling. With multistage sampling, we select a sample by using combinations of different sampling methods. Example: Stage 1, use cluster sampling to choose clusters from a population. Then, in Stage 2, we use simple random sampling to select a subset of subjects from each chosen cluster for the final sample.  \nSystematic random sampling. With systematic random sampling, we create a lis","cbCaitIPCblyuZ9V","https://ap.wps.com/l/cbCaitIPCblyuZ9V","pdf",1029544,"English","# Important Concepts not on the AP Statistics Formula Sheet\n## Part I\n## Part II: Designing Experiments and Collecting Data","[{\"question\":\"How do you test for an outlier using the IQR method?\",\"answer\":\"Compute IQR = Q3 - Q1, then flag values more than 1.5(IQR) above Q3 or below Q1 as outliers.\"},{\"question\":\"What changes in a data distribution when you apply linear transformations?\",\"answer\":\"Addition affects the center but not the spread, while multiplication affects both the center and the spread.\"},{\"question\":\"What is the difference between stratified sampling and cluster sampling?\",\"answer\":\"Stratified sampling includes subjects from each stratum, whereas cluster sampling surveys only individuals within selected clusters.\"}]","Important Concepts not on the AP Statistics Formula Sheet - Part I - Part II - Summary Notes | PDF",1791925357]