![]() ![]() Extreme values are statistically and philosophically more interesting, because they are possible but unlikely responses. Her or his weight is most probably 78.6 kg (173 pounds) or 7.86 kg (17 pounds) depending on whether weights of adults or babies have been measured.įor this reason, it sometimes makes sense to formally distinguish two classes of outliers: (i) extreme values and (ii) mistakes. For instance, a human weighting 786 kg (1733 pounds) is clearly an error when encoding the weight of the subject. Outliers can also arise due to an experimental, measurement or encoding error. For example, it is often the case that there are outliers when collecting data on salaries, as some people make much more money than the rest. ![]() Indeed, someone who is 200 cm tall (6’7” in US) will most likely be considered as an outlier compared to the general population, but that same person may not be considered as an outlier if we measured the height of basketball players.Īn outlier may be due to the variability inherent in the observed phenomenon. ![]() Enderlein ( 1987) goes even further as the author considers outliers as values that deviate so much from other observations one might suppose a different underlying sampling mechanism.Īn observation must always be compared to other observations made on the same phenomenon before actually calling it an outlier. # sunflower-soybean 82.488095 19.125803 145.85039 0.An outlier is a value or an observation that is distant from other observations, that is to say, a data point that differs significantly from other data points. # Fit: aov(formula = weight ~ feed, data = chickwts) # Tukey's test tukey <- TukeyHSD(anova) print(tukey) # Tukey multiple comparisons of means We are going to start by loading the appropriate libraries, the datasets to access the data file, the ggplot2 for the plots, multcompView to obtain the compact letter display, and the dplyr for building a table with the summarized data. 1 The data file (chickwts) is available in the R datasets library. We are going to use the results of a one-factor experiment conducted to measure and compare the effectiveness of various feed supplements on the growth rate of chickens. colour the boxes according to the median value.add the compact letter display to the boxplot.obtain the compact letter display to indicate significant differences.perform analysis of variance and Tukey’s test.In this R tutorial, you are going to learn how to: Boxplots coloured according to the median.Boxplots coloured according to the factor (explanatory variable).Adding compact letter display from Tukey’s test.Creating a table with the summarised data and the compact letter display.Compact letter display to indicate significant differences.Analysis of variance for one factor – One-Way ANOVA. ![]()
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