class: center, middle, inverse, title-slide .title[ # A Very Incomplete Survey of Basic Statistical Tests in R ] .subtitle[ ## EDP 619 Week 5 ] .author[ ### Dr. Abhik Roy ] --- <script src="https://ajax.googleapis.com/ajax/libs/jquery/3.6.0/jquery.min.js"></script> <script type="text/x-mathjax-config"> MathJax.Hub.Register.StartupHook("TeX Jax Ready",function () { MathJax.Hub.Insert(MathJax.InputJax.TeX.Definitions.macros,{ cancel: ["Extension","cancel"], bcancel: ["Extension","cancel"], xcancel: ["Extension","cancel"], cancelto: ["Extension","cancel"] }); }); </script> <style> section { display: flex; display: -webkit-flex; } section { height: 600px; width: 60%; margin: auto; border-radius: 21px; background-color: #212121; } .remark-slide-container { background: #212121; } .hljs-github .hljs { background: transparent; color: #b2dfdb; } .hljs-github .hljs-keyword { color: #64b5f6; } .hljs-github .hljs-literal { color: #64b5f6; } .hljs-github .hljs-number { color: #64b5f6; } .hljs-github .hljs-string { color: #b7b3ef; } .hljs-github .hljs { background: transparent; color: #b2dfdb; } .hljs-github .hljs-keyword { color: #64b5f6; } .hljs-github .hljs-literal { color: #64b5f6; } .hljs-github .hljs-number { color: #64b5f6; } .hljs-github .hljs-string { color: #b7b3ef; } section p { text-align: center; font-size: 30px; background-color: #212121; border-radius: 21px; font-family: Roboto Condensed; font-style: bold; padding: 12px; color: #bff4ee; margin: auto; } #center { text-align: center; } #right { text-align: right; } .center p { margin: 0; position: absolute; top: 50%; left: 50%; -ms-transform: translate(-50%, -50%); transform: translate(-50%, -50%); } .center2 { margin: 0; position: absolute; top: 50%; left: 50%; -ms-transform: translate(-50%, -50%); transform: translate(-50%, -50%); } .tab { display: inline-block; margin-left: 40px; } .obr { display:block; margin-top:-15px; } </style> <style type="text/css"> .highlight-last-item > ul > li, .highlight-last-item > ol > li { opacity: 0.5; } .highlight-last-item > ul > li:last-of-type, .highlight-last-item > ol > li:last-of-type { opacity: 1; } </style> <div> <style type="text/css">.xaringan-extra-logo { width: 110px; height: 128px; z-index: 0; background-image: url(/Users/skynet/Documents/WVU/Teaching/GitHub.nosync/edp619/static/img/course_hex.png); background-size: contain; background-repeat: no-repeat; position: absolute; top:1em;right:1em; } </style> <script>(function () { let tries = 0 function addLogo () { if (typeof slideshow === 'undefined') { tries += 1 if (tries < 10) { setTimeout(addLogo, 100) } } else { document.querySelectorAll('.remark-slide-content:not(.title-slide):not(.inverse):not(.hide_logo)') .forEach(function (slide) { const logo = document.createElement('a') logo.classList = 'xaringan-extra-logo' logo.href = 'https://edp619.asocialdatascientist.com' slide.appendChild(logo) }) } } document.addEventListener('DOMContentLoaded', addLogo) })()</script> </div> --- class: highlight-last-item layout: true --- # Getting Ready To follow along, please make sure to do the following -- 1. Run `Basic Statistical Tests install.R` -- 2. Open up a blank `.R` script -- 3. Load up the following packages ```r library(tidyverse) library(car) library(foreign) library(lme4) library(MASS) library(CCA) library(psych) ``` --- # Purpose This is not a traditional presentation per say, rather it is a collection of common statistical tests used for survey analyses that you may need to use at some point. Review the content simply to see what is included and then come back as needed. > The examples are of course presented with R in mind, but obviously not restricted to the platform. -- > The content is intended to cast a wide net so some may look familiar while others will not -- > You can think of this as a toolkit --- # Note Here are some things to consider as you go through this + Items in here will apply depending on your focus and strengths -- + Never memorize formulas - that's what the Internet is for. Rather it is important to know what test applies to which scenario -- + Since this is not a statistics course, only test names and examples are provided -- + Some of the syntax may be structured in an odd way. This is only done so so that they be fit within the slide. Remember in general spacing doesn't mean much in R -- + These are presented in alphabetical order according to the standard name of the test --- # Decisions Decisions Decisions When deciding which test is appropriate to use, it is important to consider the type of variables that you have. Please load in the following data sets (and look at them by using `View()` or `head()` ```r some_ed_data <- read_csv("some_ed_data.csv") ``` ```r some_exercise_data <- read_csv("some_exercise_data.csv") ``` ```r some_survey_data <- read_csv("some_survey_data.csv") ``` --- # The Datas (1/3) ```r some_ed_data ``` ``` ## # A tibble: 200 × 11 ## id female race ses schtyp prog read write math science socst ## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> ## 1 70 0 4 1 1 1 57 52 41 47 57 ## 2 121 1 4 2 1 3 68 59 53 63 61 ## 3 86 0 4 3 1 1 44 33 54 58 31 ## 4 141 0 4 3 1 3 63 44 47 53 56 ## 5 172 0 4 2 1 2 47 52 57 53 61 ## 6 113 0 4 2 1 2 44 52 51 63 61 ## 7 50 0 3 2 1 1 50 59 42 53 61 ## 8 11 0 1 2 1 2 34 46 45 39 36 ## 9 84 0 4 2 1 1 63 57 54 58 51 ## 10 48 0 3 2 1 2 57 55 52 50 51 ## # … with 190 more rows ``` --- # The Datas (2/3) ```r some_exercise_data ``` ``` ## # A tibble: 90 × 6 ## id diet exertype pulse time highpulse ## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> ## 1 1 1 1 85 1 0 ## 2 1 1 1 85 2 0 ## 3 1 1 1 88 3 0 ## 4 2 1 1 90 1 0 ## 5 2 1 1 92 2 0 ## 6 2 1 1 93 3 0 ## 7 3 1 1 97 1 0 ## 8 3 1 1 97 2 0 ## 9 3 1 1 94 3 0 ## 10 4 1 1 80 1 0 ## # … with 80 more rows ``` --- # The Datas (3/3) .pull-left[ ```r some_survey_data ``` ] .pull-right[ ``` ## # A tibble: 20 × 4 ## respondent gender item_1 item_2 ## <dbl> <dbl> <dbl> <dbl> ## 1 1 1 5 1 ## 2 2 2 4 3 ## 3 3 1 2 5 ## 4 4 2 4 4 ## 5 5 2 2 3 ## 6 6 1 3 2 ## 7 7 1 4 1 ## 8 8 2 5 4 ## 9 9 2 3 3 ## 10 10 2 3 5 ## 11 11 1 2 3 ## 12 12 2 3 3 ## 13 13 2 1 5 ## 14 14 1 4 4 ## 15 15 2 2 5 ## 16 16 1 2 5 ## 17 17 1 3 5 ## 18 18 1 5 2 ## 19 19 1 3 2 ## 20 20 2 5 5 ``` ] --- # An Incomplete Table of Approaches <div id="htmlwidget-ab611fcc3cdc0c463b08" style="width:100%;height:auto;" class="datatables html-widget"></div> <script type="application/json" data-for="htmlwidget-ab611fcc3cdc0c463b08">{"x":{"filter":"none","vertical":false,"extensions":["Buttons"],"data":[["1","1","1","1","1","1","1","1","1","1","1","1","1","1","1","1","1","1","1","1","1","1","1","1","1","1","1","1","2+","2+","2+","2 sets of 2+"],["0 IVs (1 population)","0 IVs (1 population)","0 IVs (1 population)","0 IVs (1 population)","1 IV with 2 levels (independent groups)","1 IV with 2 levels (independent groups)","1 IV with 2 levels (independent groups)","1 IV with 2 levels (independent groups)","1 IV with 2 or more levels (independent groups)","1 IV with 2 or more levels (independent groups)","1 IV with 2 or more levels (independent groups)","1 IV with 2 levels (dependent/matched groups)","1 IV with 2 levels (dependent/matched groups)","1 IV with 2 levels (dependent/matched groups)","1 IV with 2 or more levels (dependent/matched groups)","1 IV with 2 or more levels (dependent/matched groups)","1 IV with 2 or more levels (dependent/matched groups)","2 or more IVs (independent groups)","2 or more IVs (independent groups)","2 or more IVs (independent groups)","1 interval IV","1 interval IV","1 interval IV","1 interval IV","1 or more interval IVs and/or 1 or more categorical IVs","1 or more interval IVs and/or 1 or more categorical IVs","1 or more interval IVs and/or 1 or more categorical IVs","1 or more interval IVs and/or 1 or more categorical IVs","1 IV with 2 or more levels (independent groups)","2+","0","0"],["interval & normal","ordinal or interval","categorical (2 categories)","categorical","interval & normal","ordinal or interval","categorical","categorical","interval & normal","ordinal or interval","categorical","interval & normal","ordinal or interval","categorical","interval & normal","ordinal or interval","categorical (2 categories)","interval & normal","ordinal or interval","categorical (2 categories)","interval & normal","interval & normal","ordinal or interval","categorical","interval & normal","interval & normal","categorical","categorical","interval & normal","interval & normal","interval & normal","interval & normal"],["one-sample t-test","one-sample median","binomial test","Chi-square goodness-of-fit","2 independent sample t-test","Wilcoxon-Mann Whitney test","Chi-square test","Fisher’s exact test","one-way ANOVA","Kruskal Wallis","Chi-square test","paired t-test","Wilcoxon signed ranks test","McNemar","one-way repeated measures ANOVA","Friedman test","repeated measures logistic regression","factorial ANOVA","ordered logistic regression","factorial logistic regression","correlation","simple linear regression","non-parametric correlation","simple logistic regression","multiple regression","analysis of covariance","multiple logistic regression","discriminant analysis","one-way MANOVA","multivariate multiple linear regression","factor analysis","canonical correlation"]],"container":"<table class=\"FALSE\">\n <thead>\n <tr>\n <th>Number of Dependent Variables<\/th>\n <th>Number and Type of Independent Variables<\/th>\n <th>Type of Dependent Variables<\/th>\n <th>Test(s)<\/th>\n <\/tr>\n <\/thead>\n<\/table>","options":{"pageLength":5,"searching":false,"autoWidth":false,"dom":"Brtip","buttons":["copy","csv","excel","pdf"],"lengthMenu":false,"columnDefs":[{"className":"dt-center","targets":0}],"initComplete":"function(settings, json) {\n$(this.api().table().header()).css({'color': '#fff'});\n}","order":[],"orderClasses":false,"rowCallback":"function(row, data, displayNum, displayIndex, dataIndex) {\nvar value=data[0]; $(this.api().cell(row, 0).node()).css({'color':'white','background-color':'#212121'});\nvar value=data[1]; $(this.api().cell(row, 1).node()).css({'color':'white','background-color':'#212121'});\nvar value=data[2]; $(this.api().cell(row, 2).node()).css({'color':'white','background-color':'#212121'});\nvar value=data[3]; $(this.api().cell(row, 3).node()).css({'color':'white','background-color':'#212121'});\n}"},"callback":"function(table) {\n$(\"button.buttons-copy\").css(\"color\",\"#bb86fc\"); \n $(\"button.buttons-csv\").css(\"color\",\"#bb86fc\"); \n $(\"button.buttons-excel\").css(\"color\",\"#bb86fc\"); \n $(\"button.buttons-pdf\").css(\"color\",\"#bb86fc\"); \n return table;\n}"},"evals":["options.initComplete","options.rowCallback","callback"],"jsHooks":[]}</script> --- # Tests --- ## ANOVA ```r summary(aov(some_ed_data$write ~ some_ed_data$prog + some_ed_data$read)) ``` ``` ## Df Sum Sq Mean Sq F value Pr(>F) ## some_ed_data$prog 1 586 586 10.2 0.00164 ** ## some_ed_data$read 1 5965 5965 103.7 < 2e-16 *** ## Residuals 197 11327 57 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ``` --- ## Binomial Test ```r prop.test(sum(some_ed_data$female), length(some_ed_data$female), p = 0.5) ``` ``` ## ## 1-sample proportions test with continuity correction ## ## data: sum(some_ed_data$female) out of length(some_ed_data$female), null probability 0.5 ## X-squared = 1.445, df = 1, p-value = 0.2293 ## alternative hypothesis: true p is not equal to 0.5 ## 95 percent confidence interval: ## 0.4733037 0.6149394 ## sample estimates: ## p ## 0.545 ``` --- ## Canonical Correlation .pull-left[ ```r cc(cbind(some_ed_data$read, some_ed_data$write), cbind(some_ed_data$math, some_ed_data$science)) ``` ] .pull-right[ ``` ## $cor ## [1] 0.7728409 0.0234784 ## ## $names ## $names$Xnames ## NULL ## ## $names$Ynames ## NULL ## ## $names$ind.names ## NULL ## ## ## $xcoef ## [,1] [,2] ## [1,] -0.06326131 -0.1037908 ## [2,] -0.04924918 0.1219084 ## ## $ycoef ## [,1] [,2] ## [1,] -0.06698268 0.1201425 ## [2,] -0.04824063 -0.1208860 ## ## $scores ## $scores$xscores ## [,1] [,2] ## [1,] -0.26358835 -0.589561062 ## [2,] -1.30420707 -0.877901269 ## [3,] 1.49454321 -1.556539586 ## [4,] -0.24916276 -2.187572699 ## [5,] 0.36902478 0.448346869 ## [6,] 0.55880872 0.759719249 ## [7,] -0.16550344 0.990333008 ## [8,] 1.48691695 1.066177022 ## [9,] -0.88940214 -0.602764022 ## [10,] -0.41133590 -0.223835983 ## [11,] -0.15787718 -1.632383600 ## [12,] -0.90382773 0.995247615 ## [13,] -1.66976282 -1.274946875 ## [14,] -0.61554542 1.062803275 ## [15,] 0.24930149 1.265470254 ## [16,] 0.83307890 0.601575756 ## [17,] 0.36902478 0.448346869 ## [18,] -0.50983426 0.019980737 ## [19,] -1.59970217 -0.146451110 ## [20,] 0.50317366 -1.966788153 ## [21,] -0.49540867 -1.578030900 ## [22,] -1.18489724 0.128686136 ## [23,] 0.77023105 -1.325925827 ## [24,] -0.45337228 -0.900933442 ## [25,] 1.39563138 0.510987923 ## [26,] 1.93015961 -0.030998216 ## [27,] -1.40991823 0.164921269 ## [28,] 0.12277887 1.057888668 ## [29,] 1.24829729 -0.946997788 ## [30,] 0.44671169 -1.045873974 ## [31,] 1.71956420 -1.592774720 ## [32,] -1.46555329 -2.561586132 ## [33,] -1.50841660 0.408737989 ## [34,] 1.22707237 -0.373691122 ## [35,] -0.29202606 0.782751422 ## [36,] -1.09361167 0.683875235 ## [37,] -1.05796116 -1.487443067 ## [38,] -1.26217068 -0.200803810 ## [39,] 1.40325764 -2.111728685 ## [40,] 1.90934814 -1.281402340 ## [41,] 0.61527070 -0.161194929 ## [42,] -0.48181000 0.471379042 ## [43,] -0.04578015 0.173209623 ## [44,] 1.22707237 -0.373691122 ## [45,] -1.40991823 0.164921269 ## [46,] -0.48181000 0.471379042 ## [47,] 0.77023105 -1.325925827 ## [48,] -1.11442313 -0.566528889 ## [49,] 0.02428050 1.301705388 ## [50,] -0.36208671 -0.345744343 ## [51,] -0.45378574 0.922777348 ## [52,] 1.81084977 -1.037585621 ## [53,] 0.95280219 -0.215547629 ## [54,] -0.98790051 -0.358947303 ## [55,] -1.76826119 -1.031130155 ## [56,] 1.51535467 -0.306135462 ## [57,] 1.74037567 -0.342370595 ## [58,] 1.32557073 -0.617507842 ## [59,] -0.88940214 -0.602764022 ## [60,] 0.45351102 -0.021169003 ## [61,] 0.47473595 -0.594475669 ## [62,] -0.12346705 1.667430467 ## [63,] 0.95280219 -0.215547629 ## [64,] 2.12715634 -0.518631655 ## [65,] 0.67173268 -1.082109108 ## [66,] 1.10054974 -0.581272708 ## [67,] -0.55187065 -0.657116722 ## [68,] 1.00926417 -1.136461807 ## [69,] -0.19991357 -2.309481059 ## [70,] 1.11497533 -2.179284345 ## [71,] 0.95280219 -0.215547629 ## [72,] -0.55187065 -0.657116722 ## [73,] -2.01450711 -0.421588356 ## [74,] -1.30420707 -0.877901269 ## [75,] 0.20767856 -1.235337994 ## [76,] 0.96001499 -1.014553448 ## [77,] -0.58030837 0.715195762 ## [78,] -1.30420707 -0.877901269 ## [79,] 2.16919273 0.158465804 ## [80,] 0.91076580 -0.892645088 ## [81,] -0.98790051 -0.358947303 ## [82,] 1.21264678 1.224320515 ## [83,] -1.30420707 -0.877901269 ## [84,] -1.28339561 0.372502856 ## [85,] 0.40467530 -1.722971433 ## [86,] -0.62955755 0.837104122 ## [87,] 0.59445924 -1.411599054 ## [88,] 1.33916940 1.431902101 ## [89,] 1.21347370 -2.423101065 ## [90,] 0.01068183 -0.747704555 ## [91,] -0.43977362 1.148476501 ## [92,] -0.49540867 -1.578030900 ## [93,] -1.45195462 -0.512176189 ## [94,] 1.26910876 0.303406337 ## [95,] 0.95280219 -0.215547629 ## [96,] -0.31325099 1.356058087 ## [97,] -1.78948611 -0.457823489 ## [98,] -1.28339561 0.372502856 ## [99,] 1.58541532 0.822360302 ## [100,] -1.18489724 0.128686136 ## [101,] -1.35345626 -0.755992909 ## [102,] 0.02428050 1.301705388 ## [103,] 0.66451988 -0.283103289 ## [104,] -0.64315622 -1.212305821 ## [105,] -0.29202606 0.782751422 ## [106,] -0.23556409 -0.138162756 ## [107,] -0.94586412 0.318150156 ## [108,] 1.96539666 -0.378605729 ## [109,] 0.27052642 0.692163589 ## [110,] -1.78948611 -0.457823489 ## [111,] -0.26358835 -0.589561062 ## [112,] 0.65730709 0.515902529 ## [113,] -1.11442313 -0.566528889 ## [114,] -1.59970217 -0.146451110 ## [115,] -1.71901201 -1.153038515 ## [116,] 1.45889269 0.614778716 ## [117,] 0.52357167 1.107326761 ## [118,] -2.01450711 -0.421588356 ## [119,] -0.19352770 0.538934702 ## [120,] 0.99483858 0.461549830 ## [121,] -0.55187065 -0.657116722 ## [122,] 0.17924085 0.136974490 ## [123,] 0.17924085 0.136974490 ## [124,] 0.66451988 -0.283103289 ## [125,] -0.12346705 1.667430467 ## [126,] -0.38331164 0.227562323 ## [127,] 0.72098186 -1.204017467 ## [128,] 0.82586610 1.400581574 ## [129,] 0.32698840 -0.228750589 ## [130,] 2.02865797 -0.274814935 ## [131,] -0.60833263 0.263797456 ## [132,] -0.90382773 0.995247615 ## [133,] -1.45195462 -0.512176189 ## [134,] 0.58683298 1.211117555 ## [135,] -0.86137788 -0.151365716 ## [136,] -2.00729431 -1.220594175 ## [137,] 0.02428050 1.301705388 ## [138,] 0.43228610 0.552137663 ## [139,] 1.41685631 -0.062318743 ## [140,] 0.20046577 -0.436332176 ## [141,] 1.86648483 1.688921781 ## [142,] 0.58683298 1.211117555 ## [143,] 0.86151662 -0.770736728 ## [144,] 0.12277887 1.057888668 ## [145,] -0.29202606 0.782751422 ## [146,] 0.36902478 0.448346869 ## [147,] -0.31325099 1.356058087 ## [148,] 0.55880872 0.759719249 ## [149,] 0.91076580 -0.892645088 ## [150,] 0.34779986 1.021653535 ## [151,] 1.10776254 -1.380278527 ## [152,] -0.86817722 -1.176070688 ## [153,] 0.37582412 1.473051841 ## [154,] 0.27052642 0.692163589 ## [155,] -0.75608018 0.629522535 ## [156,] -1.30420707 -0.877901269 ## [157,] -0.09502933 0.295117983 ## [158,] 0.43908543 1.576842634 ## [159,] 1.32557073 -0.617507842 ## [160,] -1.57167791 0.304947196 ## [161,] -0.12346705 1.667430467 ## [162,] -0.16508998 -0.833377782 ## [163,] -0.07421787 1.545522107 ## [164,] -0.75608018 0.629522535 ## [165,] -0.29202606 0.782751422 ## [166,] 0.95280219 -0.215547629 ## [167,] -0.16550344 0.990333008 ## [168,] 0.77661692 1.522489934 ## [169,] -0.75608018 0.629522535 ## [170,] -0.65758181 0.385705816 ## [171,] 0.43908543 1.576842634 ## [172,] 0.66451988 -0.283103289 ## [173,] 1.47331828 -0.983232921 ## [174,] -0.79811657 -0.047574923 ## [175,] 0.70655627 0.393994170 ## [176,] -1.03714969 -0.237038943 ## [177,] -1.50841660 0.408737989 ## [178,] 0.77661692 1.522489934 ## [179,] 0.17924085 0.136974490 ## [180,] -0.58752117 1.514201580 ## [181,] -0.94586412 0.318150156 ## [182,] 0.70655627 0.393994170 ## [183,] -0.68601953 1.758018300 ## [184,] -0.77730510 1.202829201 ## [185,] -0.55949691 1.965599886 ## [186,] -1.40991823 0.164921269 ## [187,] -0.04578015 0.173209623 ## [188,] 0.76301825 -0.526920009 ## [189,] -1.13564806 0.006777776 ## [190,] 0.47473595 -0.594475669 ## [191,] 0.58683298 1.211117555 ## [192,] 0.81865331 2.199587393 ## [193,] 0.17924085 0.136974490 ## [194,] 0.40384838 1.924450147 ## [195,] -0.27121460 2.033155546 ## [196,] -0.48181000 0.471379042 ## [197,] 0.98082645 0.235850677 ## [198,] 0.27815267 -1.930553019 ## [199,] -0.62955755 0.837104122 ## [200,] -1.28339561 0.372502856 ## ## $scores$yscores ## [,1] [,2] ## [1,] 1.013980334 -0.81276184 ## [2,] -0.561661891 -1.30522812 ## [3,] -0.387441410 -0.58065576 ## [4,] 0.322640484 -0.81722302 ## [5,] -0.347186284 0.38420150 ## [6,] -0.427696537 -1.54551302 ## [7,] 0.657553868 -1.41793527 ## [8,] 1.131974678 0.63489582 ## [9,] -0.387441410 -0.58065576 ## [10,] 0.132448995 0.14614718 ## [11,] 0.054709777 -0.33665321 ## [12,] -0.427696537 -1.54551302 ## [13,] -1.670868810 1.09910797 ## [14,] -0.443667546 0.14242953 ## [15,] 1.182986345 2.20269592 ## [16,] 0.735293086 -0.93513488 ## [17,] 0.199431671 0.02600473 ## [18,] -0.789337471 0.14019895 ## [19,] -0.778580931 0.74314179 ## [20,] -0.347186284 0.38420150 ## [21,] 0.499304397 -3.83045019 ## [22,] -2.469446463 0.24993198 ## [23,] 0.360124575 -1.29927988 ## [24,] -0.733111335 -0.58288635 ## [25,] 1.131974678 0.63489582 ## [26,] 1.064992001 0.75503827 ## [27,] -1.335955426 0.49839571 ## [28,] -0.588389441 -0.22022841 ## [29,] 0.864043971 1.11546562 ## [30,] 0.092193868 -0.81871008 ## [31,] -0.057742495 1.10951738 ## [32,] -1.346711967 -0.10454714 ## [33,] -1.376210553 -0.46646155 ## [34,] -1.263758281 -1.91263214 ## [35,] -0.264232597 -1.42388351 ## [36,] -0.800094012 -0.46274390 ## [37,] -2.180002675 0.97524787 ## [38,] -1.711123937 0.13425071 ## [39,] 1.343679249 0.87741131 ## [40,] 1.466888062 0.03418356 ## [41,] 1.255183491 -0.20833193 ## [42,] -0.923302825 0.38048385 ## [43,] -0.443667546 0.14242953 ## [44,] 0.735293086 -0.93513488 ## [45,] -0.226748507 -1.90594038 ## [46,] -1.520932447 -0.82911949 ## [47,] 1.051464425 -1.29481870 ## [48,] -1.700367396 0.73719355 ## [49,] -0.749082344 1.10505620 ## [50,] -0.191707849 1.34980229 ## [51,] -0.808079517 0.38122738 ## [52,] 1.214928364 -1.17318919 ## [53,] -0.470395097 1.22742925 ## [54,] 0.092193868 -0.81871008 ## [55,] -2.190759215 0.37230502 ## [56,] 1.826085563 2.08627112 ## [57,] 1.622366497 0.99978435 ## [58,] 0.713780005 -2.14102057 ## [59,] -0.454424087 -0.46051331 ## [60,] 0.421892783 0.87146307 ## [61,] 0.215402681 -1.66193782 ## [62,] -1.386967094 -1.06940440 ## [63,] 1.775073896 0.51847102 ## [64,] 1.544627280 0.51698396 ## [65,] 0.941783188 1.59826602 ## [66,] 0.936241116 -1.29556223 ## [67,] -0.644615577 0.50285689 ## [68,] 0.853287430 0.51252278 ## [69,] -1.060039214 -0.82614537 ## [70,] 0.622840814 0.51103572 ## [71,] 1.064992001 0.75503827 ## [72,] -0.655372118 -0.10008596 ## [73,] -1.767350073 0.85733600 ## [74,] -1.427222220 -2.03426166 ## [75,] 0.148420004 -1.54179537 ## [76,] 0.976496243 -0.33070497 ## [77,] 0.046724273 0.50731807 ## [78,] -0.762609921 -0.94480077 ## [79,] 1.064992001 0.75503827 ## [80,] 0.384408693 1.35351994 ## [81,] -0.443667546 0.14242953 ## [82,] -0.532163305 -0.94331371 ## [83,] -1.912071967 0.49467806 ## [84,] -0.226748507 -1.90594038 ## [85,] 1.225684905 -0.57024634 ## [86,] -1.298471336 0.01633884 ## [87,] 0.054709777 -0.33665321 ## [88,] 1.389148845 -0.44861683 ## [89,] 1.708091219 0.63861347 ## [90,] -1.001042042 -0.10231655 ## [91,] -0.660586586 2.19079945 ## [92,] -0.438453078 -2.14845587 ## [93,] -1.654897801 -0.58883459 ## [94,] 0.421892783 0.87146307 ## [95,] 0.679066950 -0.21204958 ## [96,] -1.097523305 -0.34408851 ## [97,] -1.979054644 0.61482051 ## [98,] -2.056793862 0.13202012 ## [99,] 1.466888062 0.03418356 ## [100,] -1.536903457 0.85882306 ## [101,] -1.587915124 -0.70897704 ## [102,] -0.907331815 -1.30745871 ## [103,] 1.153487759 1.84078151 ## [104,] -0.001516359 0.38643209 ## [105,] -0.465180628 -1.06345616 ## [106,] -0.419383429 2.79522935 ## [107,] -0.872291157 1.94828395 ## [108,] 0.888000485 -1.41644821 ## [109,] 0.534345056 -0.57470752 ## [110,] -1.392181562 1.22148101 ## [111,] 0.405594171 -2.62530802 ## [112,] 1.399905385 0.15432601 ## [113,] -0.009501864 1.23040337 ## [114,] -0.703612749 -0.22097194 ## [115,] -0.443667546 0.14242953 ## [116,] 1.523114198 -0.68890174 ## [117,] -0.309702193 -0.09785537 ## [118,] -0.923302825 0.38048385 ## [119,] -1.057268178 0.62076875 ## [120,] 1.466888062 0.03418356 ## [121,] 0.266414348 -0.09413772 ## [122,] 0.534345056 -0.57470752 ## [123,] 0.596113264 1.59603543 ## [124,] 0.228930258 0.38791915 ## [125,] 1.373177835 1.23932572 ## [126,] -0.521406764 -0.34037086 ## [127,] 0.890771521 0.03046591 ## [128,] -0.001516359 0.38643209 ## [129,] 0.009240182 0.98937493 ## [130,] 1.882311700 1.36318582 ## [131,] -0.001516359 0.38643209 ## [132,] -1.400167067 2.06545229 ## [133,] -0.135481713 0.62671699 ## [134,] 0.612084273 -0.09190713 ## [135,] 0.622840814 0.51103572 ## [136,] -1.223503154 -0.94777488 ## [137,] -0.387441410 -0.58065576 ## [138,] 0.220944753 1.23189042 ## [139,] 1.791044905 -1.16947154 ## [140,] 0.622840814 0.51103572 ## [141,] 1.024736875 -0.20981899 ## [142,] 0.266414348 -0.09413772 ## [143,] 0.663095940 1.47589298 ## [144,] 0.689823490 0.39089327 ## [145,] -0.414168960 0.50434395 ## [146,] 0.831774349 -0.69336292 ## [147,] 0.628055282 -1.77984969 ## [148,] 1.024736875 -0.20981899 ## [149,] 1.016751370 0.63415229 ## [150,] 1.440160512 1.11918327 ## [151,] 1.121218137 0.03195297 ## [152,] -0.856320148 0.26034140 ## [153,] 0.936241116 -1.29556223 ## [154,] 0.188675131 -0.57693811 ## [155,] -0.521406764 -0.34037086 ## [156,] -0.711598254 0.62299934 ## [157,] 0.073451823 -0.57768164 ## [158,] 0.344153566 0.38866268 ## [159,] 1.188200814 -0.08818948 ## [160,] -1.402938103 0.61853816 ## [161,] -0.079255576 -0.09636831 ## [162,] 0.218173717 -0.21502370 ## [163,] -0.427696537 -1.54551302 ## [164,] -1.737851487 1.21925042 ## [165,] 0.159176545 -0.93885253 ## [166,] 1.418647431 -0.08670242 ## [167,] -0.465180628 -1.06345616 ## [168,] 1.147945687 -1.05304674 ## [169,] -0.845563607 0.86328424 ## [170,] 0.054709777 -0.33665321 ## [171,] 0.601327732 -0.69484998 ## [172,] 1.147945687 -1.05304674 ## [173,] 1.718847760 1.24155631 ## [174,] -1.068024719 0.01782590 ## [175,] 1.391919881 0.99829729 ## [176,] -0.931288329 1.22445513 ## [177,] -0.845563607 0.86328424 ## [178,] -0.146238253 0.02377414 ## [179,] 0.215402681 -1.66193782 ## [180,] -0.692856208 0.38197091 ## [181,] 0.333397025 -0.21428017 ## [182,] 0.931026648 0.99532317 ## [183,] -0.829592598 -0.82465831 ## [184,] -0.068499036 0.50657454 ## [185,] -1.577158583 -0.10603420 ## [186,] -1.057268178 0.62076875 ## [187,] -0.213220930 0.14391659 ## [188,] 1.188200814 -0.08818948 ## [189,] -0.376684870 0.02228708 ## [190,] -0.079255576 -0.09636831 ## [191,] 1.255183491 -0.20833193 ## [192,] 0.802275763 -1.05527733 ## [193,] -0.175736839 -0.33814027 ## [194,] 1.574125866 0.87889837 ## [195,] -0.403412420 1.10728679 ## [196,] 0.518374046 1.11323503 ## [197,] 1.745575310 0.15655660 ## [198,] -0.443667546 0.14242953 ## [199,] -0.655372118 -0.10008596 ## [200,] -0.883047698 1.34534111 ## ## $scores$corr.X.xscores ## [,1] [,2] ## [1,] -0.9271970 -0.374574 ## [2,] -0.8538903 0.520453 ## ## $scores$corr.Y.xscores ## [,1] [,2] ## [1,] -0.7177974 0.008701966 ## [2,] -0.6750187 -0.011433002 ## ## $scores$corr.X.yscores ## [,1] [,2] ## [1,] -0.7165758 -0.008794398 ## [2,] -0.6599214 0.012219404 ## ## $scores$corr.Y.yscores ## [,1] [,2] ## [1,] -0.9287778 0.3706371 ## [2,] -0.8734252 -0.4869583 ``` ] --- ## Chi-square Test ```r chisq.test(table(some_ed_data$female, some_ed_data$schtyp)) ``` ``` ## ## Pearson's Chi-squared test with Yates' continuity correction ## ## data: table(some_ed_data$female, some_ed_data$schtyp) ## X-squared = 0.00054009, df = 1, p-value = 0.9815 ``` --- ## Chi-square Goodness of Fit ```r chisq.test(table(some_ed_data$race), p = c(10, 10, 10, 70)/100) ``` ``` ## ## Chi-squared test for given probabilities ## ## data: table(some_ed_data$race) ## X-squared = 5.0286, df = 3, p-value = 0.1697 ``` --- ## Correlation ```r cor(some_ed_data$read, some_ed_data$write) ``` ``` ## [1] 0.5967765 ``` ```r cor.test(some_ed_data$read, some_ed_data$write) ``` ``` ## ## Pearson's product-moment correlation ## ## data: some_ed_data$read and some_ed_data$write ## t = 10.465, df = 198, p-value < 2.2e-16 ## alternative hypothesis: true correlation is not equal to 0 ## 95 percent confidence interval: ## 0.4993831 0.6792753 ## sample estimates: ## cor ## 0.5967765 ``` --- ## Discriminant Analysis ```r lda(factor(some_ed_data$prog) ~ some_ed_data$read + some_ed_data$write + some_ed_data$math, data = some_ed_data) ``` ``` ## Call: ## lda(factor(some_ed_data$prog) ~ some_ed_data$read + some_ed_data$write + ## some_ed_data$math, data = some_ed_data) ## ## Prior probabilities of groups: ## 1 2 3 ## 0.225 0.525 0.250 ## ## Group means: ## some_ed_data$read some_ed_data$write some_ed_data$math ## 1 49.75556 51.33333 50.02222 ## 2 56.16190 56.25714 56.73333 ## 3 46.20000 46.76000 46.42000 ## ## Coefficients of linear discriminants: ## LD1 LD2 ## some_ed_data$read 0.02919876 0.04385321 ## some_ed_data$write 0.03832289 -0.13698224 ## some_ed_data$math 0.07034625 0.07931008 ## ## Proportion of trace: ## LD1 LD2 ## 0.9874 0.0126 ``` --- ## Factor Analysis ```r fa(r = cor(model.matrix(~read + write + math + science + socst - 1, data = some_ed_data)), rotate = "none", fm = "pa", 2) ``` ``` ## maximum iteration exceeded ``` ``` ## Factor Analysis using method = pa ## Call: fa(r = cor(model.matrix(~read + write + math + science + socst - ## 1, data = some_ed_data)), nfactors = 2, rotate = "none", ## fm = "pa") ## Standardized loadings (pattern matrix) based upon correlation matrix ## PA1 PA2 h2 u2 com ## read 0.81 0.06 0.66 0.34 1.0 ## write 0.76 0.00 0.58 0.42 1.0 ## math 0.80 0.17 0.67 0.33 1.1 ## science 0.75 0.26 0.62 0.38 1.2 ## socst 0.79 -0.48 0.85 0.15 1.6 ## ## PA1 PA2 ## SS loadings 3.06 0.33 ## Proportion Var 0.61 0.07 ## Cumulative Var 0.61 0.68 ## Proportion Explained 0.90 0.10 ## Cumulative Proportion 0.90 1.00 ## ## Mean item complexity = 1.2 ## Test of the hypothesis that 2 factors are sufficient. ## ## The degrees of freedom for the null model are 10 and the objective function was 2.51 ## The degrees of freedom for the model are 1 and the objective function was 0.01 ## ## The root mean square of the residuals (RMSR) is 0.01 ## The df corrected root mean square of the residuals is 0.03 ## ## Fit based upon off diagonal values = 1 ## Measures of factor score adequacy ## PA1 PA2 ## Correlation of (regression) scores with factors 0.95 0.79 ## Multiple R square of scores with factors 0.91 0.62 ## Minimum correlation of possible factor scores 0.82 0.23 ``` --- ## Factorial ANOVA (Analysis of Variance) ```r anova(lm(write ~ female * ses, data = some_ed_data)) ``` ``` ## Analysis of Variance Table ## ## Response: write ## Df Sum Sq Mean Sq F value Pr(>F) ## female 1 1176.2 1176.21 14.7212 0.0001680 *** ## ses 1 1042.3 1042.32 13.0454 0.0003862 *** ## female:ses 1 0.0 0.04 0.0005 0.9827570 ## Residuals 196 15660.3 79.90 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ``` --- ## Factorial Logistic Regression ```r summary(glm(female ~ prog * schtyp, data = some_ed_data, family = binomial)) ``` ``` ## ## Call: ## glm(formula = female ~ prog * schtyp, family = binomial, data = some_ed_data) ## ## Deviance Residuals: ## Min 1Q Median 3Q Max ## -1.698 -1.247 1.069 1.109 1.572 ## ## Coefficients: ## Estimate Std. Error z value Pr(>|z|) ## (Intercept) -2.2765 1.8857 -1.207 0.227 ## prog 1.2303 0.9398 1.309 0.191 ## schtyp 2.2405 1.7017 1.317 0.188 ## prog:schtyp -1.1313 0.8622 -1.312 0.189 ## ## (Dispersion parameter for binomial family taken to be 1) ## ## Null deviance: 275.64 on 199 degrees of freedom ## Residual deviance: 273.65 on 196 degrees of freedom ## AIC: 281.65 ## ## Number of Fisher Scoring iterations: 4 ``` --- ## Friedman Test ```r friedman.test(cbind(some_ed_data$read, some_ed_data$write, some_ed_data$math)) ``` ``` ## ## Friedman rank sum test ## ## data: cbind(some_ed_data$read, some_ed_data$write, some_ed_data$math) ## Friedman chi-squared = 0.64491, df = 2, p-value = 0.7244 ``` --- ## Kruskal Wallis Test ```r kruskal.test(some_ed_data$write, some_ed_data$prog) ``` ``` ## ## Kruskal-Wallis rank sum test ## ## data: some_ed_data$write and some_ed_data$prog ## Kruskal-Wallis chi-squared = 34.045, df = 2, p-value = 4.047e-08 ``` --- ## McNemar Test ```r # Some made up data in matrix form made_up_matrixdata <- matrix(c(150, 22, 21, 12), 2, 2) mcnemar.test(made_up_matrixdata) ``` ``` ## ## McNemar's Chi-squared test with continuity correction ## ## data: made_up_matrixdata ## McNemar's chi-squared = 0, df = 1, p-value = 1 ``` --- ## Multiple Regression ```r lm(some_ed_data$write ~ some_ed_data$female + some_ed_data$read + some_ed_data$math + some_ed_data$science + some_ed_data$socst) ``` ``` ## ## Call: ## lm(formula = some_ed_data$write ~ some_ed_data$female + some_ed_data$read + ## some_ed_data$math + some_ed_data$science + some_ed_data$socst) ## ## Coefficients: ## (Intercept) some_ed_data$female some_ed_data$read ## 6.1388 5.4925 0.1254 ## some_ed_data$math some_ed_data$science some_ed_data$socst ## 0.2381 0.2419 0.2293 ``` --- ## Multivariate Multiple Regression ```r mmrlm <- lm(cbind(write, read) ~ female + math + science + socst, data = some_ed_data) summary(Anova(mmrlm)) ``` ``` ## ## Type II MANOVA Tests: ## ## Sum of squares and products for error: ## write read ## write 7258.783 1091.057 ## read 1091.057 8699.762 ## ## ------------------------------------------ ## ## Term: female ## ## Sum of squares and products for the hypothesis: ## write read ## write 1413.5284 -133.48461 ## read -133.4846 12.60544 ## ## Multivariate Tests: female ## Df test stat approx F num Df den Df Pr(>F) ## Pillai 1 0.1698853 19.85132 2 194 1.4335e-08 *** ## Wilks 1 0.8301147 19.85132 2 194 1.4335e-08 *** ## Hotelling-Lawley 1 0.2046528 19.85132 2 194 1.4335e-08 *** ## Roy 1 0.2046528 19.85132 2 194 1.4335e-08 *** ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## ## ------------------------------------------ ## ## Term: math ## ## Sum of squares and products for the hypothesis: ## write read ## write 714.8665 856.2825 ## read 856.2825 1025.6735 ## ## Multivariate Tests: math ## Df test stat approx F num Df den Df Pr(>F) ## Pillai 1 0.1599321 18.46685 2 194 4.5551e-08 *** ## Wilks 1 0.8400679 18.46685 2 194 4.5551e-08 *** ## Hotelling-Lawley 1 0.1903800 18.46685 2 194 4.5551e-08 *** ## Roy 1 0.1903800 18.46685 2 194 4.5551e-08 *** ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## ## ------------------------------------------ ## ## Term: science ## ## Sum of squares and products for the hypothesis: ## write read ## write 857.8824 901.3191 ## read 901.3191 946.9551 ## ## Multivariate Tests: science ## Df test stat approx F num Df den Df Pr(>F) ## Pillai 1 0.1664254 19.36631 2 194 2.1459e-08 *** ## Wilks 1 0.8335746 19.36631 2 194 2.1459e-08 *** ## Hotelling-Lawley 1 0.1996526 19.36631 2 194 2.1459e-08 *** ## Roy 1 0.1996526 19.36631 2 194 2.1459e-08 *** ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ## ## ------------------------------------------ ## ## Term: socst ## ## Sum of squares and products for the hypothesis: ## write read ## write 1105.653 1277.393 ## read 1277.393 1475.810 ## ## Multivariate Tests: socst ## Df test stat approx F num Df den Df Pr(>F) ## Pillai 1 0.2206710 27.46604 2 194 3.1399e-11 *** ## Wilks 1 0.7793290 27.46604 2 194 3.1399e-11 *** ## Hotelling-Lawley 1 0.2831551 27.46604 2 194 3.1399e-11 *** ## Roy 1 0.2831551 27.46604 2 194 3.1399e-11 *** ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ``` --- ## Non-parametric Correlation ```r cor.test(some_ed_data$read, some_ed_data$write, method = "spearman") ``` ``` ## Warning in cor.test.default(some_ed_data$read, some_ed_data$write, method = ## "spearman"): Cannot compute exact p-value with ties ``` ``` ## ## Spearman's rank correlation rho ## ## data: some_ed_data$read and some_ed_data$write ## S = 510993, p-value < 2.2e-16 ## alternative hypothesis: true rho is not equal to 0 ## sample estimates: ## rho ## 0.6167455 ``` --- ## One Sample *t*-test ```r t.test(some_ed_data$read, mu = 50) ``` ``` ## ## One Sample t-test ## ## data: some_ed_data$read ## t = 3.0759, df = 199, p-value = 0.002394 ## alternative hypothesis: true mean is not equal to 50 ## 95 percent confidence interval: ## 50.80035 53.65965 ## sample estimates: ## mean of x ## 52.23 ``` --- ## One-way Analysis of Variance (ANOVA) ```r summary(aov(some_ed_data$read ~ some_ed_data$prog)) ``` ``` ## Df Sum Sq Mean Sq F value Pr(>F) ## some_ed_data$prog 1 381 381.1 3.674 0.0567 . ## Residuals 198 20538 103.7 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ``` --- ## One-way Multivariate Analysis of Variance (MANOVA) ```r summary( manova( cbind(some_ed_data$read, some_ed_data$write, some_ed_data$math) ~ some_ed_data$prog ) ) ``` ``` ## Df Pillai approx F num Df den Df Pr(>F) ## some_ed_data$prog 1 0.035319 2.392 3 196 0.06984 . ## Residuals 198 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ``` --- ## One-way Repeated Measures Analysis of Variance (ANOVA) ```r model <- lm(gender ~ item_1 + item_2, data = some_survey_data) analysis <- Anova(model, idata = factor_surveydata, idesign = ~s) print(analysis) ``` ``` ## Anova Table (Type II tests) ## ## Response: gender ## Sum Sq Df F value Pr(>F) ## item_1 0.0601 1 0.2396 0.6307 ## item_2 0.7268 1 2.8974 0.1069 ## Residuals 4.2642 17 ``` --- ## Ordered Logistic Regression .pull-left[ ```r # Create ordered variable write3 as # a factor with levels 1, 2, and 3 some_ed_data$write3 <- cut(some_ed_data$write, c(0, 48, 57, 70), right = TRUE, labels = c(1,2,3)) table(some_ed_data$write3) ``` ] .pull-right[ ``` ## ## 1 2 3 ## 61 61 78 ``` ] .pull-left[ ```r # fit ordered logit model and store results 'some_write_data' some_write_data <- polr(write3 ~ female + read + socst, data = some_ed_data, Hess=TRUE) summary(some_write_data) ``` ] .pull-right[ ``` ## Call: ## polr(formula = write3 ~ female + read + socst, data = some_ed_data, ## Hess = TRUE) ## ## Coefficients: ## Value Std. Error t value ## female 1.28543 0.32445 3.962 ## read 0.11772 0.02136 5.512 ## socst 0.08019 0.01944 4.124 ## ## Intercepts: ## Value Std. Error t value ## 1|2 9.7037 1.1968 8.1080 ## 2|3 11.8001 1.3041 9.0486 ## ## Residual Deviance: 312.5526 ## AIC: 322.5526 ``` ] --- ## Principal Components Analysis (PCA) ```r princomp(formula = ~read + write + math + science + socst, data = some_ed_data) ``` ``` ## Call: ## princomp(formula = ~read + write + math + science + socst, data = some_ed_data) ## ## Standard deviations: ## Comp.1 Comp.2 Comp.3 Comp.4 Comp.5 ## 18.252929 7.677044 6.213371 5.774331 5.429881 ## ## 5 variables and 200 observations. ``` --- ## Repeated Measures Logistic Regression ```r glmer(highpulse ~ diet + (1 | id), data = some_exercise_data, family = binomial) ``` ``` ## Generalized linear mixed model fit by maximum likelihood (Laplace ## Approximation) [glmerMod] ## Family: binomial ( logit ) ## Formula: highpulse ~ diet + (1 | id) ## Data: some_exercise_data ## AIC BIC logLik deviance df.resid ## 105.4679 112.9674 -49.7340 99.4679 87 ## Random effects: ## Groups Name Std.Dev. ## id (Intercept) 1.821 ## Number of obs: 90, groups: id, 30 ## Fixed Effects: ## (Intercept) diet ## -3.148 1.145 ``` --- ## Simple Linear Regression ```r lm(some_ed_data$write ~ some_ed_data$read) ``` ``` ## ## Call: ## lm(formula = some_ed_data$write ~ some_ed_data$read) ## ## Coefficients: ## (Intercept) some_ed_data$read ## 23.9594 0.5517 ``` --- ## Simple Logistic Regression ```r glm(some_ed_data$female ~ some_ed_data$read, family = binomial) ``` ``` ## ## Call: glm(formula = some_ed_data$female ~ some_ed_data$read, family = binomial) ## ## Coefficients: ## (Intercept) some_ed_data$read ## 0.72609 -0.01044 ## ## Degrees of Freedom: 199 Total (i.e. Null); 198 Residual ## Null Deviance: 275.6 ## Residual Deviance: 275.1 AIC: 279.1 ``` --- ## Two Independent Samples *t*-test ```r t.test(some_ed_data$read ~ some_ed_data$female) ``` ``` ## ## Welch Two Sample t-test ## ## data: some_ed_data$read by some_ed_data$female ## t = 0.74506, df = 188.46, p-value = 0.4572 ## alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0 ## 95 percent confidence interval: ## -1.796263 3.976725 ## sample estimates: ## mean in group 0 mean in group 1 ## 52.82418 51.73394 ``` --- ## Wilcoxon-Mann-Whitney Test ```r wilcox.test(some_ed_data$read ~ some_ed_data$female) ``` ``` ## ## Wilcoxon rank sum test with continuity correction ## ## data: some_ed_data$read by some_ed_data$female ## W = 5300, p-value = 0.4029 ## alternative hypothesis: true location shift is not equal to 0 ``` --- ## Wilcoxon Signed Rank Sum Test ```r wilcox.test(some_ed_data$write, some_ed_data$read, paired = TRUE) ``` ``` ## ## Wilcoxon signed rank test with continuity correction ## ## data: some_ed_data$write and some_ed_data$read ## V = 9261, p-value = 0.3666 ## alternative hypothesis: true location shift is not equal to 0 ``` --- # Other Approaches There are so many other approaches that are for specific cases or use statistical approaches, but aren't [themselves statistics](https://www.nature.com/articles/nmeth.4642). With that said, the approaches given in this overview cover the gambit of what you will likely need --- # Some Extra Things --- ## Reporting After running a statistical test successfully, it can be difficult to know how to report the results. The `report` package automatically produces reports of models and dataframes according to best practices guidelines. Click [here](https://easystats.github.io/report/) for more information. --- ## Visualizations Interested in making incredible visuals? Check out [#tidytuesday](https://twitter.com/hashtag/tidytuesday) on Twitter. You do not need an account for access. --- ## Something useless If you are a fan of the show Rick & Morty, consider downloading the most pointless package `mortyr` to do pointless statistics on pointless data. More about the package [here](https://github.com/mikejohnpage/mortyr). --- ## Source A majority of the information included in this survey of approached was scraped from the web using `R` via the [UCLA Institute for Digital Research & Education](https://stats.idre.ucla.edu/r/whatstat/what-statistical-analysis-should-i-usestatistical-analyses-using-r/) site using the `xml2` package. They also fully support SAS, SPSS (for some reason), Stata, and Mplus. --- ## Thats it! If you have any questions, please reach out -- <br> <br> <br> <br> <br> <br> <br> <br> <br> <center> <br><br> <div class="fade_rule"></div> <br><br> </center> <center> <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png" /></a><br /><br />This work is licensed under a <br /><a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a> </center>