class: center, middle, inverse, title-slide # Describing Data I --- ## Last time... Research design and validity - Construct - Statistical conclusion - Internal - External --- ## Today... Describing data! --- ## Why do we describe data? - Understand your data - There's a lot to learn from descriptive statistics - Find errors in data entry or collection --- ## Happiness Examples today are based on data from the [2015 World Happiness Report](https://worldhappiness.report/ed/2015/), which is an annual survey part of the [Gallup World Poll](https://www.gallup.com/178667/gallup-world-poll-work.aspx). The dataset is available on [GitHub](https://raw.githubusercontent.com/uopsych/psy611/master/data/world_happiness_2015.csv) for those interested in trying at home. .footnote[ To save this file to your computer, copy all the text on the linked webpage, then paste the text into a new text file (which you can create inside RStudio). Save this file as .csv type file, like `world_happiness_2015.csv` and load using `rio::import`.] --- ```r library(rio) world = import(here("data/world_happiness_2015.csv")) world ``` ``` ## Country Happiness GDP Support Life Freedom ## 1 Albania 4.606651 9.251464 0.6393561 68.43517 0.7038507 ## 2 Argentina 6.697131 NA 0.9264923 67.28722 0.8812237 ## 3 Armenia 4.348320 8.968936 0.7225510 65.30076 0.5510266 ## 4 Australia 7.309061 10.680326 0.9518616 72.56024 0.9218710 ## 5 Austria 7.076447 10.691354 0.9281103 70.82256 0.9003052 ## 6 Azerbaijan 5.146775 9.730904 0.7857028 61.97585 0.7642895 ## 7 Bahrain 6.007375 NA 0.8525507 65.84793 0.8495212 ## 8 Bangladesh 4.633474 8.050836 0.6014683 61.72731 0.8147963 ## 9 Belarus 5.718908 9.725568 0.9240726 65.31599 0.6227534 ## 10 Belgium 6.904219 10.626178 0.8852088 71.34201 0.8694749 ## 11 Benin 3.624664 7.598665 0.4343885 50.58654 0.7333836 ## 12 Bhutan 5.082129 8.969653 0.8475744 60.61641 0.8301015 ## 13 Bolivia 5.834329 8.778191 0.8287058 59.73697 0.8836251 ## 14 Bosnia and Herzegovina 5.117178 9.178364 0.6557236 67.63831 0.6306980 ## 15 Botswana 3.761965 9.654463 0.8156561 55.25417 0.8571689 ## 16 Brazil 6.546897 9.582796 0.9066931 64.59515 0.7989353 ## 17 Burkina Faso 4.418930 7.357180 0.7053935 50.83040 0.6591027 ## 18 Cambodia 4.162165 8.094646 0.7286103 58.16891 0.9563198 ## 19 Cameroon 5.037965 7.986924 0.6463125 47.95748 0.7914286 ## 20 Canada 7.412773 10.664708 0.9390671 71.76053 0.9314690 ## 21 Chad 4.322675 7.695847 0.7512522 44.87283 0.4743609 ## 22 Chile 6.532750 10.009483 0.8271419 71.57857 0.7688814 ## 23 China 5.303878 9.501941 0.7937337 68.59845 NA ## 24 Colombia 6.387572 9.471478 0.8899000 63.84050 0.7908980 ## 25 Congo (Brazzaville) 4.690830 8.685216 0.6421362 53.51811 0.8501725 ## 26 Congo (Kinshasa) 3.902742 6.613966 0.7672356 50.01415 0.5737638 ## 27 Costa Rica 6.854004 9.580832 0.8782730 69.49661 0.9069257 ## 28 Croatia 5.205438 9.919107 0.7683634 67.59174 0.6935230 ## 29 Cyprus 5.439161 NA 0.7695561 72.48824 0.6280348 ## 30 Czech Republic 6.608017 10.308098 0.9113626 69.60413 0.8084842 ## 31 Denmark 7.514425 10.676427 0.9597013 70.70427 0.9414364 ## 32 Dominican Republic 5.061862 9.488247 0.8931978 63.16206 0.8560253 ## 33 Ecuador 5.964075 9.270621 0.8558892 66.94999 0.8008705 ## 34 Egypt 4.762538 9.234282 0.7297443 61.27411 0.6592615 ## 35 El Salvador 6.018496 9.001607 0.7907554 63.90189 0.7333559 ## 36 Estonia 5.628909 10.210577 0.9179296 66.66893 0.8146924 ## 37 Ethiopia 4.573155 7.333114 0.6255968 55.63552 0.8026426 ## 38 Finland 7.447926 10.553578 0.9478006 71.21165 0.9298619 ## 39 France 6.357625 10.530862 0.8957194 71.97216 0.8170362 ## 40 Gabon 4.661013 9.845919 0.7558620 55.68797 0.6713007 ## 41 Georgia 4.121941 8.902565 0.5173716 65.30637 0.6399450 ## 42 Germany 7.037138 10.694968 0.9259232 71.30358 0.8894289 ## 43 Ghana 3.985916 8.277353 0.6874486 53.54028 0.8520162 ## 44 Greece 5.622519 10.093509 0.8348247 70.67931 0.5317363 ## 45 Guatemala 6.464987 8.886600 0.8228375 61.96589 0.8686398 ## 46 Guinea 3.504694 7.037234 0.5788596 50.16096 0.6659530 ## 47 Haiti 3.569762 7.413352 0.5643197 52.95332 0.3982955 ## 48 Honduras 4.845437 8.470057 0.7723755 63.41061 0.5340577 ## 49 Hungary 5.344383 10.107333 0.8587338 66.59668 0.5577214 ## 50 India 4.342079 8.659320 0.6101333 59.07401 0.7772253 ## 51 Indonesia 5.042800 9.247716 0.8094781 60.31876 0.7794183 ## 52 Iran 4.749956 9.717675 0.5724069 65.53881 NA ## 53 Iraq 4.493377 9.546689 0.6844348 60.94004 0.5994599 ## 54 Ireland 6.830125 10.839026 0.9529426 71.29931 0.8922769 ## 55 Israel 7.079411 10.363305 0.8641302 72.66603 0.7527840 ## 56 Italy 5.847684 10.394681 0.9089865 72.46586 0.5747657 ## 57 Ivory Coast 4.445039 8.095674 0.7039917 45.04416 0.7997455 ## 58 Japan 5.879684 10.488579 0.9226572 74.82469 0.8316942 ## 59 Jordan 5.404593 9.352019 0.8304439 64.18116 0.7665170 ## 60 Kazakhstan 5.949995 10.042273 0.9313493 63.64412 0.7401328 ## 61 Kenya 4.357618 7.970297 0.7769231 54.14322 0.7929903 ## 62 Kosovo 5.077461 NA 0.8052708 62.00486 0.5610483 ## 63 Kuwait 6.146032 NA 0.8230178 65.09716 0.8216624 ## 64 Kyrgyzstan 4.905376 8.061284 0.8565845 62.41665 0.8131759 ## 65 Latvia 5.880598 10.037901 0.8793724 65.33850 0.6563932 ## 66 Lebanon 5.171971 9.728823 0.7417077 69.59850 0.5967498 ## 67 Liberia 2.701591 6.739805 0.6376660 51.28914 0.6714309 ## 68 Libya 5.615405 9.555550 0.8679877 61.16175 0.7745450 ## 69 Lithuania 5.711378 10.185368 0.9285235 65.67057 0.6414702 ## 70 Luxembourg 6.701571 11.429970 0.9336046 72.53326 0.9322564 ## 71 Macedonia 4.975590 9.446383 0.7663682 65.56458 0.6603189 ## 72 Madagascar 3.592514 7.228697 0.6467165 56.31346 0.5447536 ## 73 Malawi 3.867638 6.660712 0.4943816 54.48933 0.8013907 ## 74 Malaysia 6.322121 10.136704 0.8176163 64.74024 0.6745945 ## 75 Mali 4.582098 7.350600 0.8301892 49.19207 0.6337535 ## 76 Malta 6.613394 NA 0.9187649 70.77766 0.9121780 ## 77 Mauritania 3.922664 8.231690 0.8749459 53.24210 0.4470866 ## 78 Mexico 6.236287 9.707403 0.7606143 67.78441 0.7194660 ## 79 Moldova 6.017472 8.447127 0.8399055 61.26768 0.5952414 ## 80 Mongolia 4.982720 9.346310 0.9055244 62.64931 0.6855108 ## 81 Montenegro 5.124921 9.616770 0.7396305 65.11017 0.5833173 ## 82 Morocco 5.160294 8.906810 0.6537850 63.91926 0.6934186 ## 83 Myanmar 4.223846 NA 0.7520643 57.09218 0.8079711 ## 84 Nepal 4.812437 7.746914 0.7476119 60.75210 0.7634472 ## 85 Netherlands 7.324437 10.748624 0.8790104 71.09193 0.9039788 ## 86 New Zealand 7.418121 10.431994 0.9873435 71.92076 0.9417843 ## 87 Nicaragua 5.924113 8.480531 0.8269085 65.85806 0.8092592 ## 88 Niger 3.671454 6.803244 0.7130196 52.82997 0.7281283 ## 89 Nigeria 4.932915 8.644704 0.8116477 45.24734 0.6804703 ## 90 North Cyprus 5.842550 NA 0.7913827 NA 0.7853528 ## 91 Norway 7.603434 11.068009 0.9468340 70.52483 0.9476205 ## 92 Pakistan 4.823195 8.464853 0.5617201 57.25552 0.5865462 ## 93 Palestinian Territories 4.695239 8.365737 0.7661012 62.83750 0.5560409 ## 94 Panama 6.605550 9.942081 0.8826150 67.68180 0.8466692 ## 95 Paraguay 5.559724 9.065535 0.9141991 63.34425 0.8061247 ## 96 Peru 5.577263 9.358279 0.7984183 65.03024 0.8022690 ## 97 Philippines 5.547489 8.843670 0.8535886 59.46133 0.9115336 ## 98 Poland 6.007022 10.120240 0.8930904 66.95756 0.7934622 ## 99 Portugal 5.080866 10.195284 0.8662139 70.45056 0.8004403 ## 100 Qatar 6.374529 NA NA 67.82797 NA ## 101 Romania 5.777491 9.896219 0.7869673 66.41331 0.7958477 ## 102 Russia 5.995539 10.012393 0.9243633 64.08343 0.6854547 ## 103 Rwanda 3.483109 7.416408 0.6781436 54.64949 0.9078923 ## 104 Saudi Arabia 6.345492 10.815763 0.8197497 63.71784 0.8202072 ## 105 Senegal 4.617001 7.725880 0.7015345 57.57685 0.7195333 ## 106 Serbia 5.317685 9.462955 0.8162510 65.63837 0.5458920 ## 107 Sierra Leone 4.908618 7.374071 0.6105937 43.74034 0.6242961 ## 108 Singapore 6.619525 NA 0.8664367 76.04466 0.8868909 ## 109 Slovakia 6.162004 10.214083 0.9434537 67.49669 0.5871577 ## 110 Slovenia 5.740642 10.269225 0.9011638 70.51219 0.8960073 ## 111 Somalia 5.353645 NA 0.5992811 47.28276 0.9678693 ## 112 South Africa 4.887326 9.428298 0.8980963 50.14693 0.8624494 ## 113 South Korea 5.780211 10.446025 0.7683506 73.85837 0.6158488 ## 114 Spain 6.380663 10.402864 0.9564719 73.37998 0.7320005 ## 115 Sri Lanka 4.611607 9.319309 0.8625001 64.64014 0.9020748 ## 116 Sweden 7.288922 10.712334 0.9294600 71.74087 0.9350721 ## 117 Switzerland 7.572137 10.914726 0.9383337 72.86915 0.9278024 ## 118 Syria 3.461913 NA 0.4639129 64.83573 0.4482709 ## 119 Taiwan 6.450088 NA 0.8853889 70.75000 0.7008105 ## 120 Tajikistan 5.124211 7.869648 0.8439325 61.64697 0.8465421 ## 121 Tanzania 3.660597 7.831087 0.7902626 56.12052 0.7586847 ## 122 Thailand 6.201763 9.637293 0.8663245 65.64534 0.8849165 ## 123 Togo 3.768302 7.241591 0.4785934 51.97361 0.7715772 ## 124 Tunisia 5.131612 9.292294 0.6094700 63.35026 0.7113734 ## 125 Turkey 5.514465 9.864202 0.8512246 65.69592 0.6531968 ## 126 Turkmenistan 5.791460 9.669529 0.9601585 58.44135 0.7013584 ## 127 Ukraine 3.964543 8.895362 0.9094397 63.52374 0.4305920 ## 128 United Arab Emirates 6.568398 NA 0.8241367 68.35641 0.9150362 ## 129 United Kingdom 6.515445 10.567661 0.9359857 71.05131 0.8329261 ## 130 United States 6.863947 10.877965 0.9035711 70.03674 0.8487535 ## 131 Uruguay 6.628080 9.917072 0.8914935 68.11640 0.9168797 ## 132 Uzbekistan 5.972364 8.630272 0.9682252 60.53566 0.9799371 ## 133 Venezuela 5.568800 NA 0.9110869 64.58602 0.5121593 ## 134 Vietnam 5.076315 8.637988 0.8486767 66.04872 NA ## 135 Yemen 2.982674 7.843260 0.6686835 54.08096 0.6099808 ## 136 Zimbabwe 3.703191 7.430315 0.7358003 50.36258 0.6671933 ## Generosity Corruption ## 1 -0.082337685 0.88479304 ## 2 NA 0.85090619 ## 3 -0.186696529 0.90146220 ## 4 0.315701962 0.35655439 ## 5 0.089088559 0.55747962 ## 6 -0.222635135 0.61555255 ## 7 NA NA ## 8 -0.059777591 0.72060090 ## 9 -0.100902960 0.66867816 ## 10 0.052451991 0.46878463 ## 11 0.001502594 0.85009819 ## 12 0.285040438 0.63395578 ## 13 -0.023433717 0.86237395 ## 14 -0.046468392 0.95985365 ## 15 -0.126697809 0.86029297 ## 16 -0.027783971 0.77133906 ## 17 0.020786475 0.69272399 ## 18 0.212208703 0.82513022 ## 19 0.058221977 0.86804903 ## 20 0.237486422 0.42715225 ## 21 -0.041133381 0.88863939 ## 22 0.026815979 0.81151134 ## 23 -0.262474209 NA ## 24 -0.107555106 0.84289932 ## 25 -0.143441707 0.84135950 ## 26 -0.014224946 0.86637801 ## 27 -0.058310181 0.76141941 ## 28 -0.100691713 0.84854555 ## 29 NA 0.89279515 ## 30 -0.152868271 0.88646746 ## 31 0.213263184 0.19101639 ## 32 -0.068872340 0.75528818 ## 33 -0.118297137 0.66582751 ## 34 -0.098499060 0.68449807 ## 35 -0.166342810 0.80454427 ## 36 -0.173507467 0.56873447 ## 37 0.125852734 0.56702733 ## 38 0.100564413 0.22336966 ## 39 -0.150623634 0.64060205 ## 40 -0.224478483 0.86677748 ## 41 -0.175952777 0.50241679 ## 42 0.164857537 0.41216829 ## 43 -0.030955523 0.94543612 ## 44 -0.278941423 0.82395965 ## 45 0.050059937 0.82165492 ## 46 0.054546587 0.76215202 ## 47 0.312127113 0.77740395 ## 48 -0.095861293 0.84808272 ## 49 -0.208412573 0.90753031 ## 50 -0.016868994 0.77643496 ## 51 0.457429528 0.94596726 ## 52 0.141305760 NA ## 53 -0.016951477 0.76216716 ## 54 0.232017383 0.40875691 ## 55 0.099938810 0.78942990 ## 56 -0.070082054 0.91275305 ## 57 -0.035634797 0.74424964 ## 58 -0.169799194 0.65444309 ## 59 -0.069207847 NA ## 60 -0.051406134 0.71384430 ## 61 0.238054082 0.85254985 ## 62 NA 0.85064709 ## 63 NA NA ## 64 0.226265088 0.85772502 ## 65 -0.085272886 0.80840039 ## 66 0.054535788 0.88895327 ## 67 -0.009916847 0.90267265 ## 68 -0.076456018 NA ## 69 -0.262404770 0.92417407 ## 70 0.036430217 0.37539047 ## 71 -0.048491806 0.82417899 ## 72 -0.029426256 0.86095339 ## 73 0.085140154 0.83482540 ## 74 0.201898143 0.83789223 ## 75 -0.042629778 0.80004674 ## 76 NA 0.66388631 ## 77 0.073142350 0.71535844 ## 78 -0.156158805 0.70797193 ## 79 -0.038925178 0.94311881 ## 80 0.157298073 0.90021819 ## 81 -0.145327449 0.78123259 ## 82 -0.248565361 0.86777443 ## 83 NA 0.63330519 ## 84 0.242998511 0.82350838 ## 85 0.247195244 0.41182211 ## 86 0.320652515 0.18588871 ## 87 0.081847742 0.72799838 ## 88 -0.009490362 0.70254970 ## 89 -0.045139253 0.92610925 ## 90 NA 0.65918028 ## 91 0.228181615 0.29881436 ## 92 0.071716130 0.71664119 ## 93 -0.150193453 0.77430135 ## 94 -0.002205028 0.80994290 ## 95 0.003749649 0.86288828 ## 96 -0.100029737 0.88373041 ## 97 -0.056648508 0.75519156 ## 98 -0.103777371 0.81009632 ## 99 -0.171841606 0.94105077 ## 100 NA NA ## 101 -0.145152822 0.96165097 ## 102 -0.179458767 0.91341829 ## 103 0.035836529 0.09460447 ## 104 -0.070376605 NA ## 105 -0.092522651 0.76549017 ## 106 -0.062717922 0.85935801 ## 107 0.050585665 0.82482803 ## 108 NA 0.09894388 ## 109 -0.142087966 0.92754513 ## 110 -0.000629284 0.89219791 ## 111 NA 0.41023576 ## 112 -0.138439283 0.85269475 ## 113 -0.048341293 0.84072161 ## 114 -0.084349990 0.82166493 ## 115 0.310886711 0.85947096 ## 116 0.197725981 0.23196414 ## 117 0.097075745 0.20953351 ## 118 NA 0.68523693 ## 119 NA 0.85719484 ## 120 0.025703574 0.74168962 ## 121 0.142459080 0.90642261 ## 122 0.305076480 0.91365111 ## 123 -0.063052811 0.73326176 ## 124 -0.240898952 0.81482500 ## 125 NA 0.80607623 ## 126 0.063167505 NA ## 127 -0.012190276 0.95247275 ## 128 NA NA ## 129 0.288037807 0.45613372 ## 130 0.201775953 0.69754261 ## 131 -0.048586730 0.67347568 ## 132 0.373070538 0.47091693 ## 133 NA 0.81309682 ## 134 0.087306730 NA ## 135 -0.139901683 0.82909757 ## 136 -0.081429422 0.81045735 ``` ??? Data measured at country level, one row per country. Draw attention to `NA`s. --- ```r names(world) ``` ``` ## [1] "Country" "Happiness" "GDP" "Support" "Life" ## [6] "Freedom" "Generosity" "Corruption" ``` .small[ **Happiness**: “Please imagine a ladder, with steps numbered from 0 at the bottom to 10 at the top. The top of the ladder represents the best possible life for you and the bottom of the ladder represents the worst possible life for you. On which step of the ladder would you say you personally feel you stand at this time?” **GDP**: Log gross domestic product per capita **Support**: “If you were in trouble, do you have relatives or friends you can count on to help you whenever you need them, or not?” **Life**: Healthy life expectancy at birth **Freedom**: “Are you satisfied or dissatisfied with your freedom to choose what you do with your life?” **Corruption**: “Is corruption widespread throughout the government or not” and “Is corruption widespread within businesses or not?” (average of 2 questions) **Generosity**: “Have you donated money to a charity in the past month?” (residual, adjusting for GDP) ] --- ```r dim(world) ``` ``` ## [1] 136 8 ``` ```r glimpse(world) #need tidyverse ``` ``` ## Rows: 136 ## Columns: 8 ## $ Country <chr> "Albania", "Argentina", "Armenia", "Australia", "Austria", … ## $ Happiness <dbl> 4.606651, 6.697131, 4.348320, 7.309061, 7.076447, 5.146775,… ## $ GDP <dbl> 9.251464, NA, 8.968936, 10.680326, 10.691354, 9.730904, NA,… ## $ Support <dbl> 0.6393561, 0.9264923, 0.7225510, 0.9518616, 0.9281103, 0.78… ## $ Life <dbl> 68.43517, 67.28722, 65.30076, 72.56024, 70.82256, 61.97585,… ## $ Freedom <dbl> 0.7038507, 0.8812237, 0.5510266, 0.9218710, 0.9003052, 0.76… ## $ Generosity <dbl> -0.082337685, NA, -0.186696529, 0.315701962, 0.089088559, -… ## $ Corruption <dbl> 0.8847930, 0.8509062, 0.9014622, 0.3565544, 0.5574796, 0.61… ``` --- ## Distributions A **distribution** often refers to a description of the [relative] number of times a variable X will take each of its unique values. ```r hist(world$Happiness, breaks = 30, main = "Distribution of happiness scores", xlab = "Happiness") ``` <!-- --> ??? One common assumption is that the data are normally distributed. --- ## Moments of a distribution 1. Mean 2. Variance 3. Skew 4. Kurtosis ??? The first two moments (mean and variance) will be central to many inferential procedures (e.g., ANOVA). We will make assumptions about the other two moments (skew and kurtosis) in order to use some statistical procedures. --- ## Mean, `\(\mu\)` - The **mean** is the average. The population mean is represented by the Greek symbol `\(\mu\)`. - Example: a set of numbers is: `7, 7, 8, 3, 9, 2`. For a vector `\(x\)` with length `\(N\)`, the mean `\((\mu)\)` of `\(x\)` is: `$$\mu = \frac{\Sigma_{i=1}^N(x_i)}{N} = \frac{\Sigma(x_i)}{N}=\frac{7+7+8+3+9+2}{6}=\frac{36}{6}=6$$` --- ## Properties of the mean - The mean can take a value not found in the dataset. -- - Fulcrum of the data -- - Deviations from the mean sum to 0 -- - The mean is strongly influenced by outliers. -- - Can only be used with interval- and ratio-level variables. -- - The expected value of a (continuous) random variable, `\(E(X)\)`, is the mean. ??? Balancing point of the data. The farther a data point is from the mean, the greater 'weight' it has. Draw this for students:  We'll get to expected values when we talk about probability distributions, but for now, treat the expected value as the mean of a continuous variable. --- It's important to remember that the mean of a population (or group) may not represent well some (or any) members of the population. - Example: [André-François Raffray](https://www.nytimes.com/1995/12/29/world/a-120-year-lease-on-life-outlasts-apartment-heir.html) and the French apartment  ??? lawyer André-François Raffray in 1965 agreed to pay a 90-year-old woman $2500 francs each month and when she died, he would take possession of the apartment. Average life expectancy of French women was 74.5. Andre was 45 years old the woman lived another 32 years to become the oldest person on record, outliving Andre by two years. He had paid more than twice the market value for the apartment --- ## Other measures of central tendency - **Median** -- the middle point of the data - e.g., in the set of numbers `7, 7, 8, 3, 9, 2`, the median number is 7. - Can be used with ordinal-, interval-, or ratio-level variables. - **Mode** -- the number that most commonly occurs in the distribution. - e.g., in the set of numbers above, the mode is 7. - Can be used with any kind of variable. --- ## Center and spread - Distributions are most often described by their first two moments, mean and **variance**. - Typically, these moments are the two used in common inferential techniques. -- - The mean represents the average score in a distribution. A good measure of spread will tell us something about how the typical score deviates from the mean. -- - Why can't we use the average deviation? --- ## Average deviation ```r x = c(7,7,8,3,9,2) mean(x) ``` ``` ## [1] 6 ``` ```r x - mean(x) ``` ``` ## [1] 1 1 2 -3 3 -4 ``` ```r sum(x - mean(x)) ``` ``` ## [1] 0 ``` ```r sum(x - mean(x))/length(x) ``` ``` ## [1] 0 ``` --- ## Sums of squares Our solution is to square deviations. ```r x = c(7,7,8,3,9,2) mean(x) ``` ``` ## [1] 6 ``` ```r deviation = x - mean(x) deviation^2 ``` ``` ## [1] 1 1 4 9 9 16 ``` ```r sum(deviation^2) ``` ``` ## [1] 40 ``` The sum of squared deviations is referred to as **the Sum of Squares (SS)**. ??? For those familiar with ANOVA (PSY 612), sums of squares may sound familiar. As we talk about total variability in a construct, this will be our measure. --- ## Variance We calculate the average squared deviation: this is our variance, `\(\sigma^2\)`: ```r sum((x - mean(x))^2)/length(x) ``` ``` ## [1] 6.666667 ``` --- ## Variance ###Good things about variance: - It's additive. - Given two variables X and Y, if I create `\(Z = X + Y\)` then `\(Var(Z) = Var(X) + Var(Y)\)` - It can be calculated using expected values. - `\(\sigma^2 = E(X^2) - (E(X))^2\)` - Represents all values in a dataset -- ###Bad things about variance: - What the heck does it mean? --- **Standard deviation `\(\sigma\)`** is the square root of the variance. ```r sqrt(sum(deviation^2)/length(deviation)) ``` ``` ## [1] 2.581989 ``` --- <!-- --> In a normal distribution, the mean, median, and mode are all relatively equal. --- <!-- --> In a skewed distribution, both the mean and median get pulled away from the mode. The mean is pulled further. --- ## Skew and Kurtosis Moments 3 and 4 of a distribution are **skew** and **kurtosis**. - Skewness = asymmetry - Kurtosis = pointyness. Most inferential statistics assume distributions are not skewed and are mesokurtic. ??? * platykurtic: too flat, negative kurtosis * leptokurtic: too pointy, positive kurtosis --- <!-- --> --- ## Moments of a distribution Where do the names come from? .small[1. First moment: Mean `$$\mu = \frac{\Sigma(x_i)}{N}$$` 2. Second moment: Variance `$$\sigma^2 = \frac{\Sigma(X_i-\mu)^2}{N}$$` 3. Third moment: Skew `$$skewness(X) = \frac{1}{N\sigma^3}\Sigma(X_i-\mu)^3$$` 4. Fourth moment: Kurtosis `$$kurtosis(X) = \frac{1}{N\sigma^4}\Sigma(X_i-\mu)^4-3$$` ] ??? If you're calculating sample statistics for skewness and kurtosis, replace the `\(\sigma\)` with `\(s\)` and `\(\mu\)` with `\(\bar{X}\)`. --- ## problems ```r psych::describe(world) ``` ``` ## vars n mean sd median trimmed mad min max range skew ## Country* 1 136 68.50 39.40 68.50 68.50 50.41 1.00 136.00 135.00 0.00 ## Happiness 2 136 5.43 1.11 5.42 5.44 1.20 2.70 7.60 4.90 -0.07 ## GDP 3 121 9.22 1.16 9.45 9.28 1.20 6.61 11.43 4.82 -0.43 ## Support 4 135 0.80 0.12 0.83 0.81 0.12 0.43 0.99 0.55 -0.88 ## Life 5 135 63.12 7.46 64.64 63.73 7.35 43.74 76.04 32.30 -0.67 ## Freedom 6 132 0.75 0.13 0.78 0.76 0.16 0.40 0.98 0.58 -0.45 ## Generosity 7 120 0.00 0.16 -0.03 -0.01 0.15 -0.28 0.46 0.74 0.59 ## Corruption 8 125 0.73 0.20 0.81 0.77 0.12 0.09 0.96 0.87 -1.48 ## kurtosis se ## Country* -1.23 3.38 ## Happiness -0.69 0.10 ## GDP -0.78 0.11 ## Support 0.11 0.01 ## Life -0.34 0.64 ## Freedom -0.60 0.01 ## Generosity -0.27 0.01 ## Corruption 1.53 0.02 ``` How do we know if there is a problem and what should we do? --- ## problems ```r psych::describe(world) ``` ``` ## vars n mean sd median trimmed mad min max range skew ## Country* 1 136 68.50 39.40 68.50 68.50 50.41 1.00 136.00 135.00 0.00 ## Happiness 2 136 5.43 1.11 5.42 5.44 1.20 2.70 7.60 4.90 -0.07 ## GDP 3 121 9.22 1.16 9.45 9.28 1.20 6.61 11.43 4.82 -0.43 ## Support 4 135 0.80 0.12 0.83 0.81 0.12 0.43 0.99 0.55 -0.88 ## Life 5 135 63.12 7.46 64.64 63.73 7.35 43.74 76.04 32.30 -0.67 ## Freedom 6 132 0.75 0.13 0.78 0.76 0.16 0.40 0.98 0.58 -0.45 ## Generosity 7 120 0.00 0.16 -0.03 -0.01 0.15 -0.28 0.46 0.74 0.59 *## Corruption 8 125 0.73 0.20 0.81 0.77 0.12 0.09 0.96 0.87 -1.48 ## kurtosis se ## Country* -1.23 3.38 ## Happiness -0.69 0.10 ## GDP -0.78 0.11 ## Support 0.11 0.01 ## Life -0.34 0.64 ## Freedom -0.60 0.01 ## Generosity -0.27 0.01 *## Corruption 1.53 0.02 ``` ??? Draw attention to corruption - mean and median are very different - skew and kurtosis are large (|value| > 1) --- There are several approaches that could be taken to detecting and dealing with non-normality: - Overall tests of normality (e.g., Kolmogorov-Smirnov, Shapiro-Wilk tests) - Tests of extremity for a particular moment - `$$SE_{skew} =\sqrt{\frac{6n(n-1)}{(n-1)(n+2)(n+3)}}$$` - Implication? - Determine the impact of the problem on inferences. How does it affect your data? - Use procedures that are immune to the problem. --- The mean is more affected than the median by extreme values. If the data are severely skewed or there are extreme outliers, inferential statistics might be affected. There are several remedies: - Transform the data - Exclude the outliers - Use a trimmed mean (e.g., eliminate upper and lower 10%; “robust statistics”) - Use the median (not susceptible to extreme values) What are the pros and cons of these approaches? What justifies their use? --- ## Other measures of variability * **Range** -- the difference between the maximum data point and the smallest ```r min(world$Happiness) ``` ``` ## [1] 2.701591 ``` ```r max(world$Happiness) ``` ``` ## [1] 7.603434 ``` ```r range(world$Happiness) ``` ``` ## [1] 2.701591 7.603434 ``` ```r psych::describe(world$Happiness) ``` ``` ## vars n mean sd median trimmed mad min max range skew kurtosis se ## X1 1 136 5.43 1.11 5.42 5.44 1.2 2.7 7.6 4.9 -0.07 -0.69 0.1 ``` --- ## Other measures of variability * **Interquartile range (IQR)** -- the middle 50% ```r median(world$Corruption, na.rm = T) ``` ``` ## [1] 0.8099429 ``` ```r quantile(world$Corruption, probs = .50, na.rm = T) ``` ``` ## 50% ## 0.8099429 ``` ```r quantile(world$Corruption, probs = c(.25, .75), na.rm = T) ``` ``` ## 25% 75% ## 0.6734757 0.8609534 ``` ```r IQR(world$Corruption, na.rm = T) ``` ``` ## [1] 0.1874777 ``` --- ``` ## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`. ``` ``` ## Warning: Removed 11 rows containing non-finite values (stat_bin). ``` <!-- --> --- class: inverse #Bias and efficiency --- ## Population versus sample For those following along at home: ```r sum((x - mean(x))^2)/length(x) ``` ``` ## [1] 6.666667 ``` ```r var(x) ``` ``` ## [1] 8 ``` --- ## Population versus sample - The value that represents the entire population is called a **parameter**. - We collect samples to estimate the properties of populations; the statistic that represents a sample is called a **statistic**. - Population parameters are represented with Greek letters ( `\(\mu\)` , `\(\sigma\)`). - Sample statistics are represented with Latin letters ( `\(M\)` , `\(\bar{X}\)` , `\(s\)`). --- ## Bias and efficiency - In deciding about different ways to estimate a parameter (e.g., central tendency), it is important to consider bias and efficiency (and sometimes consistency). - **Bias**: An estimator is biased if its expected value and the true value of the parameter are different. - **Efficiency**: Of two alternative estimators, the more efficient one will estimate the parameter with less error for the same sample size. --- ## Bias and efficiency Robust statistics sacrifice efficiency to control possible bias. Variance (and standard deviation) are *biased* estimators when applied to samples. - Using the formulas we've described, these statistics will underestimate variability in the population. --- ## Population versus sample .pull-left[ ###Variance *Population* `$$\sigma^2 = \frac{\Sigma(X_i-\mu)^2}{N}$$` *Sample* `$$s^2 = \hat{\sigma}^2 = \frac{\Sigma(X_i-\bar{X})^2}{N-1}$$`] .pull-right[ ###Standard Deviation *Population* `$$\sigma = \sqrt{\frac{\Sigma(X_i-\mu)^2}{N}}$$` *Sample* `$$s = \hat{\sigma} = \sqrt{\frac{\Sigma(X_i-\bar{X})^2}{N-1}}$$`] --- <!-- --> --- class: inverse # Standardized scores --- ## Standardized scores (z-scores) $$ z = \frac{x_i - M}{s} $$ Scores interpreted as distance from the mean, in standard deviations. ### Properties of z-scores - `\(\Large \Sigma z = 0\)` - `\(\Large \Sigma z^2 = N\)` - `\(\Large s_z = \frac{\Sigma z^2}{N}\)` --- ## Standardized scores (z-scores) $$ z = \frac{x_i - M}{s} $$ **Why is this useful?** -- - Compare across scales and unit of measures -- - More easily identify extreme data ??? Note for the standard deviation property `$$s_z = \frac{\Sigma z^2}{n} = \frac{n}{n} = 1$$` --- # Which variable has outliers? ```r psych::describe(world, fast =T) ``` ``` ## vars n mean sd min max range se ## Country 1 136 NaN NA Inf -Inf -Inf NA ## Happiness 2 136 5.43 1.11 2.70 7.60 4.90 0.10 ## GDP 3 121 9.22 1.16 6.61 11.43 4.82 0.11 ## Support 4 135 0.80 0.12 0.43 0.99 0.55 0.01 ## Life 5 135 63.12 7.46 43.74 76.04 32.30 0.64 ## Freedom 6 132 0.75 0.13 0.40 0.98 0.58 0.01 ## Generosity 7 120 0.00 0.16 -0.28 0.46 0.74 0.01 ## Corruption 8 125 0.73 0.20 0.09 0.96 0.87 0.02 ``` --- # Which variable has outliers? ```r world %>% mutate_if(is.numeric, scale) %>% psych::describe(., fast =T) ``` ``` ## vars n mean sd min max range se ## Country 1 136 NaN NA Inf -Inf -Inf NA ## Happiness 2 136 0 1 -2.46 1.96 4.42 0.09 ## GDP 3 121 0 1 -2.26 1.91 4.17 0.09 ## Support 4 135 0 1 -2.99 1.51 4.50 0.09 ## Life 5 135 0 1 -2.60 1.73 4.33 0.09 ## Freedom 6 132 0 1 -2.64 1.71 4.34 0.09 ## Generosity 7 120 0 1 -1.80 2.90 4.70 0.09 *## Corruption 8 125 0 1 -3.20 1.14 4.34 0.09 ``` --- class: inverse ## Next time... describing relationships ## Please take Quiz 1 now on [Canvas](https://canvas.uoregon.edu/courses/165689).