For example, if the mean height in a population of 21-year-old men is 1.75 meters, and one randomly chosen man is 1.80 meters tall, then the "error" is 0.05 meters; if the randomly chosen man is 1.70 meters tall, then the "error" is 0.05 meters. default is to return an analysis of both the object and categorical To do this, we use the ggplot2 package. The table above displays the (linear) predicted values we would get if we regressed our a series of binary logistic regressions with varying cutpoints on the dependent variable and checking the equality of coefficients across cutpoints. the outcome variable. This will generate the output.. Stata Output of a Pearson's correlation in Stata. Count number of non-NA/null observations. slopes assumption. The output states that, as we requested, 5 imputed datasets were created. It does not cover all aspects of the research process which researchers are expected to do. We can look at the various diagnostics (and in real life, we probably would For mixed data types provided via a DataFrame, the default is to Version info: Code for this page was tested in R version 3.0.1 (2013-05-16) The statistical errors, on the other hand, are independent, and their sum within the random sample is almost surely not zero. For instance, we store a cookie when you log in to our shopping cart so that we can maintain your shopping cart should you not complete checkout. Hence, our outcome variable has three categories. It is similar to the regression method except that for each missing value, pared equals yes is equal to the intercept plus the coefficient for We plot the scott, silverman, a scalar constant or a callable. The return type depends on the return_type parameter: axes : object of class matplotlib.axes.Axes, dict : dict of matplotlib.lines.Line2D objects, both : a namedtuple with structure (ax, lines). Since this is a biased estimate of the variance of the unobserved errors, the bias is removed by dividing the sum of the squared residuals by df = np1, instead of n, where df is the number of degrees of freedom (n minus the number of parameters (excluding the intercept) p being estimated - 1). For data in the long format there is one observation for each time period for each subject. We can inspect the distributions of the original and imputed data using the stripplot function that is part of the lattice package. The sum of squares of the residuals, on the other hand, is observable. before weighting, differences were large. The default is The marginplot function below will plot both the complete and incomplete observations for the variables specified. fontsize=15): The parameter return_type can be used to select the type of element assumption that missingness is based on other observed variable(s) but not on the values of the missing variable(s) itself. This is particularly important in the case of detecting outliers, where the case in question is somehow different than the other's in a dataset. visually to verify that they are approximately equal. Column labels to use for resulting frame when data does not have them, defaulting to RangeIndex(0, 1, 2, , n). The blue boxes located on the left and bottom margins are box plots of the None (default) : The result will exclude nothing. x-axis, and main=' ' which sets the main label for the graph to blank. For each element in the calling DataFrame, if cond is True the element is used; otherwise the corresponding element from the DataFrame other is used. these are not used in the interpretation of the results. For example, {'a': 'b', 'y': 'z'} replaces the value a with b and y with z. or changing the fontsize (i.e. Proceedings, Register Stata online [5] If the data exhibit a trend, the regression model is likely incorrect; for example, the true function may be a quadratic or higher order polynomial. For instance: A list of strings (i.e. We do this by creating a new Refer to the notes In the above graph, the boxplots appear to mostly overlap once again providing support for the assumption of MCAR. outcome and y4 and x1 as predictors. further apart on the second line than on the first), suggesting that the proportional A black list of data types to omit from the result. two sets of coefficients is similar. Looking at the intercept for this model (-0.3783), we see that it matches the Institute for Digital Research and Education. StataCorp LLC (StataCorp) strives to provide our users with exceptional products and services. This function uses Gaussian kernels and includes automatic ratio of variances between the treated and untreated for each covariate: Ignore the raw columns, at least to begin, and focus on the weighted 1000 equally spaced points are used. n Which Stata is right for me? Pos=2 or position 2 in the anscombe file refers to the fact that x2 is in the second column of the data file. By default the lower percentile is 25 and the upper percentile is 75.The 50 percentile is the same as the median.. For object data (e.g. In such cases, we say that the $$. three is about 2.14 (-0.204 -2.345 = 2.141). This page shows how to perform a number of statistical tests using Stata. This page uses the following packages. object of class matplotlib.axes.Axes, optional, {axes, dict, both} or None, default axes, . Statistical Methods for Categorical Data Analysis. This website uses cookies to provide you with a better user experience. It tests whether the model-adjusted means of the It will be applied to each column in by independently. R will estimate our regression model separately for each imputed dataset, 1 though 5. However, Harrell does recommend a graphical method for assessing the parallel We collect and use this information only where we may legally do so. Strings | Further Information. points are not equal. it fills in a value randomly from among the a observed donor values from an observation whose regression-predicted values are closest to the regression-predicted value for the missing value from the simulated regression model (Heitjan and Little 1991; Schenker and Taylor 1996). To help demonstrate this, we normalized all the first To get the OR and confidence intervals, we just exponentiate the estimates and confidence intervals. pared (i.e. To exclude object columns submit the data non-missing values for each variable. predicted probilities, connected with a line, colored by level of the outcome, In this case a dict containing the Lines The pbox function below wil plot the marginal distribution of a variable within levels or categories of another variable. between the estimates for public are different (i.e., the markers are much drop the cases so that the model can run. This is similar to the key argument in the builtin sorted() function, with the notable difference that this key function should be vectorized.It should expect a Series and return a Series with the same shape as the input. We will demonstrate how do this, by running a linear regression model with y1 as the The red dots represent individuals that have missing values for either y1 but observed for y4 (left margin) or missing values for y4 but observed for y1 (bottom margin). The second column represents the 2 observations that are missing information only on the variable y1 . If a cell has very few cases, the If you do not have The intercepts indicate where the latent variable is cut to make the three groups that we observe in our data. Suffix to use from right frames overlapping columns. (Note, For pared equal to yes the difference in predicted values for apply greater If this 1.5 * IQR (IQR = Q3 - Q1) from the edges of the box, ending at the farthest Note that profiled CIs are not symmetric (although they are usually close to symmetric). rolling (window, min_periods = None, center = False, win_type = None, on = None, axis = 0, closed = None, step = None, method = 'single') [source] # Provide rolling window calculations. In order create this graph, you will need the Hmisc library. Some of the methods listed are quite reasonable while others have either variable, should remain similar. A t-test is any statistical hypothesis test in which the test statistic follows a Student's t-distribution under the null hypothesis.It is most commonly applied when the test statistic would follow a normal distribution if the value of a scaling term in the test statistic were known (typically, the scaling term is unknown and therefore a nuisance parameter). public or private, and current GPA is also collected. Example 2: A researcher is interested in what factors influence medaling in Olympic swimming. in hopes of achieving experimental-like We assume that treatment (smoking during pregnancy) is determined by The expected value, being the mean of the entire population, is typically unobservable, and hence the statistical error cannot be observed either. would indicate that the effect of attending a public versus private school is different for available values for y1 and y4 . A box plot is a method for graphically depicting groups of numerical data through their quartiles. That fact, and the normal and chi-squared distributions given above form the basis of calculations involving the t-statistic: where pandas.DataFrame.rolling# DataFrame. datasets distribution, excluding NaN values. If include='all' is provided as an option, the result The first row represents the 6 One can then also calculate the mean square of the model by dividing the sum of squares of the model minus the degrees of freedom, which is just the number of parameters. Wikipedias entry for boxplot. making up the boxes, caps, fliers, medians, and whiskers is returned. by some other columns. Analyzes both numeric and object series, as well The predictor matrix tells us which variables in the dataset were used to produce predicted values for matching. variables value (i.e. Note that this latent variable is continuous. The plot above allows you to examine the pattern and distribution of complete and incomplete observations. If the axis of other does not align with axis of cond Series/DataFrame, the misaligned index positions will be filled with False.. The include and exclude parameters can be used to limit Here we will plot the That is fortunate because it means that even though we do not know, we know the probability distribution of this quotient: it has a Student's t-distribution with n1 degrees of freedom. [.25, .5, .75], which returns the 25th, 50th, and Make a box-and-whisker plot from DataFrame columns, optionally grouped The option selected here will apply only to the device you are currently using. ordinal variable is greater than or equal to a (note, this is what the ordinal frequency. This suggests that the parallel slopes assumption is reasonable (these differences are what graph below are plotting). However, a terminological difference arises in the expression mean squared error (MSE). In this cases as in the margins plot, the box plots are blue for observed values and red for missing values. A box plot is a method for graphically depicting groups of numerical data through their quartiles. We then need to summarize or pool those estimates to get one overall set of parameter estimates. The whiskers extend from the edges Given a Series of points randomly sampled from an unknown The levels in the pivot table will be stored in MultiIndex objects (hierarchical indexes) on the The final command A residual (or fitting deviation), on the other hand, is an observable estimate of the unobservable statistical error. Suppose there is a series of observations from a univariate distribution and we want to estimate the mean of that distribution (the so-called location model).In this case, the errors are the deviations of the observations from the population mean, while the residuals are the deviations of the observations from the sample mean. While the outcome variable, size of soda, is obviously ordered, the difference between the various sizes is not consistent. mark_right bool, default True When using a secondary_y axis, automatically mark the column labels with (right) in the legend. variable, even if it is numbered 0, 1, 2, 3). This is the function used internally to estimate the PDF. Then the F value can be calculated by dividing the mean square of the model by the mean square of the error, and we can then determine significance (which is why you want the mean squares to begin with.).[8]. Sample size: Both ordered logistic and ordered probit, using left: use only keys from left frame, similar to a SQL left outer join; preserve key order. Ignored Version info: Code for this page was tested in Stata 12. Have we done an adequate job of balancing the covariates so that we can trust Members of the The San Diego Union-Tribune Editorial Board and some local writers share their thoughts on 2022. If your dependent variable had more than three levels you would need n across treatment groups. To find out more about checking for balance after teffects or stteffects, see [TE] tebalance. is the most common value. n The mean squared error of a regression is a number computed from the sum of squares of the computed residuals, and not of the unobservable errors. Turning our attention to the predictions with public columns Index or array-like. In the original Stephen King novel, Tad Trenton dies of dehydration while Donna contracts rabies from her fight with Cujo. lead to over-fitting, while using a large bandwidth value may result If you would like to change the default number First we store the coefficient table, then calculate the p-values and combine back with the table. Timestamps also include the first and last items. Make sure that you can load These factors may include what type of sandwich is ordered (burger or chicken), whether or not fries are also ordered, and age of the consumer. Our two variables with missing values were imputed using pmm. This affects statistics data point within that interval. for Series. In regression analysis, the distinction between errors and residuals is subtle and important, and leads to the concept of studentized residuals. This approach is used in other software packages such as Stata and is trivial to do. Say that we estimate the effect of smoking during pregnancy on infant represents the sample standard deviation for a sample of size n, and unknown , and the denominator term the expected value of apply on the log odds scale, given all of the other variables in the model are held constant. In particular, it does not cover data The mean error (ME) is the bias. Apply the key function to the values before sorting. Version info: Code for this page was tested in R version 3.1.1 (2014-07-10) To understand how to interpret the coefficients, first lets establish some notation and review the concepts involved in ordinal logistic regression. Likewise, the sum of absolute errors (SAE) is the sum of the absolute values of the residuals, which is minimized in the least absolute deviations approach to regression. For our data analysis below, we are going to expand on Example 3 about applying to graduate school. unlikely, somewhat likely, or very likely to apply to graduate school. For each element in the calling DataFrame, if cond is True the element is used; otherwise the corresponding element from the DataFrame other is used. the numpy.object data type. and perform a statistical test. DataFrame.plot(). The signature for DataFrame.where() differs If all of the residuals are equal, or do not fan out, they exhibit homoscedasticity. Finally, in addition to the cells, we plot all of the marginal relationships. The matrix (array) rr represents the number of observations where both pairs of values are observed. Backend to use instead of the backend specified in the option The plot command below tells R that the object we wish to plot is s. The command which=1:3 is a list of values indicating levels of y should be included in the plot. If the axis of other does not align with axis of cond Series/DataFrame, the misaligned index positions will be filled with False.. Under the Missing Completely at Random (MCAR) assumption the red and blue box plots should be identical. To limit the result to numeric types submit -0.3783 + 1.1438 = 0.765). The default method of imputation in the MICE package is PMM and the default number of imputations is 5. analysis commands. For example, when pared is Subscribe to Stata News predictions for apply greater than or equal to two, versus apply greater than or equal to predicted value in the cell for pared equal to no in the column for Y>=1, the value below it, for fall between 0 and 1. © 2022 pandas via NumFOCUS, Inc. If an integer, the fixed number of observations used for each window. Object to merge with. Err. Looking Parameters right DataFrame or named Series. uses box plots rather than smoothed pdfs. Make a box-and-whisker plot from DataFrame columns, optionally grouped by some other columns. Convenience method for frequency conversion and resampling of time series. When we supply a y argument, such as apply, to function sf, y >= 2 will evaluate to a 0/1 (FALSE/TRUE) vector, and taking the mean of that vector will give you the proportion of or probability that apply >= 2. We use cookies to ensure that we give you the best experience on our websiteto enhance site navigation, to analyze site usage, and to assist in our marketing efforts. For each element in the calling DataFrame, if cond is False the element is used; otherwise the corresponding element from the DataFrame other is used. Some people are not satisfied without a p value. Order result DataFrame lexicographically by the join key. Here we obtain a plot of the distibution of the variable x2 by y1 and y4 . when grouping with by, a Series mapping columns to The grid above represents the 4 missing data patterns present in our modified anscombe file. However, these tests have been criticized for having a tendency to reject the null hypothesis (that the sets of coefficients are the same), and hence, indicate that there the parallel slopes assumption does not hold, in cases where the assumption does hold (see Harrell 2001 p. 335). The margins make the final plot a 3 x 3 grid. Here are the options: all : All columns of the input will be included in the output. One box-plot will be done per value of columns in by. Click here to report an error on this page or leave a comment, Your Email (must be a valid email for us to receive the report!). the ordinal variable and is executed by the as.numeric(apply) >= a coding below. these are the number of observations where both variables are missing values. When public is set to yes We thus relax the parallel slopes assumption to checks its tenability. meaning characteristics across groups will be approximately equal. which columns in a DataFrame are analyzed for the output. You may also want to check out our FAQ page on how R handles missing data. The command pch=1:3 selects Make a box plot from DataFrame columns. plotting.backend. marital status, the mother's age, attendance to prenatal care during The latest Lifestyle | Daily Life news, tips, opinion and advice from The Sydney Morning Herald covering life and relationships, beauty, fashion, health & wellbeing treatment groups, Kernel density plot comparing propensity scores across treatment groups. Given an unobservable function that relates the independent variable to the dependent variable say, a line the deviations of the dependent variable observations from this function are the unobservable errors. plot of the estimated PDF: © 2022 pandas via NumFOCUS, Inc. If the dataframe consists Additional keyword arguments are documented in These cookies do not directly store your personal information, but they do support the ability to uniquely identify your internet browser and device. In Figure 3.28 the names are sorted alphabetically, which isnt very useful in this graph. Including only numeric columns in a DataFrame description. list-like of dtypes or None (default), optional. variable. Hosted by OVHcloud. We have simulated some data for this If your data passed assumption #2 (i.e., there was a linear relationship between your two variables), assumption #3 (i.e., there were no significant outliers) from the result. The third graphical diagnostic is the same as the second but Coef. distribution, estimate its PDF using KDE with automatic lsuffix str, default . have missing information. Then we can fit the following ordinal logistic regression model: $$ DataFrame.plot. functions for identifying the missing data pattern(s) present in a particular dataset. So for pared, we would say that for a one unit increase in pared (i.e., going from 0 to 1), we expect a 1.05 increase in The plot above allows you to examine the pattern and distribution of complete and incomplete observations. This is similar to the key argument in the builtin sorted() function, with the notable difference that this key function should be vectorized.It should expect a Series and return a Series with the same shape as the input. understand than either the coefficients or the odds ratios. For DataFrame input, this also S The freq is the most common values cleaning and checking, verification of assumptions, model diagnostics or Please note: The purpose of this page is to show how to use various data analysis commands associated with imputation using PMM. If we collect data for monthly ice cream 1000 equally spaced points (default): A scalar bandwidth can be specified. odds assumption may not hold. the variable in the row) is observed and second (or column) variable is missing. Upcoming meetings Supported platforms, Stata Press books extra large) that people order at a fast-food chain. Please note: Clearing your browser cookies at any time will undo preferences saved here. Relevant predictors include at training hours, diet, age, and popularity of swimming in the athletes home country. The downside of this approach is that the information contained in the ordering is lost. the difference between the coefficients is about 1.37 (-0.175 -1.547 = 1.372). By continuing to use our site, you consent to the storing of cookies on your device. Basu's theorem. If the proportional odds assumption holds, for each predictor variable, Background Information | The Original Taxonomy | The Revised Taxonomy | Why Use Blooms Taxonomy? count and top results will be arbitrarily chosen from in under-fitting: Finally, the ind parameter determines the evaluation points for the Finally, we see the residual deviance, -2 * Log Likelihood of the model as well The researcher believes that the distance between gold and silver is larger than the distance between silver and bronze. The parameters are ignored when analyzing a Series. For further details see Treatment-effects estimators reweight the observational data Std. Let $Y$ be an ordinal outcome with $J$ categories. with a line at the median (Q2). Representation of a kernel-density estimate using Gaussian kernels. Please note: The purpose of this page is to show how to use various data provided data types. Change address The method used to calculate the estimator bandwidth. are returned. Introduction. The default is axes. tendency, dispersion and shape of a array: Use return_type='dict' when you want to tweak the appearance S Books on Stata Ignored strings or timestamps), the results index will include count, unique, top, and freq.The top is the most common value. If the linear model is applicable, a scatterplot of residuals plotted against the independent variable should be random about zero with no trend to the residuals. When R sees a call to summary with a formula argument, it will calculate descriptive statistics for the variable on the left side of the formula by groups on the right side of the formula and will return the results in a nice table. To better see the data, we also add the raw data points on top of the box plots, with a small amount of noise (often called jitter) and 50% transparency so they do not overwhelm the boxplots. The mean residual (MR) is always zero for least-squares estimators. One way to calculate a p-value in this case is by comparing the t-value against the standard normal distribution, like a z test. The Stata Blog Statistical tests to do this are available in some software packages. Column in the DataFrame to pandas.DataFrame.groupby(). Below the function is configured for a y variable with three levels, 1, 2, 3. same as the median. Long and Freese 2005 for more details and explanations of various The main difference is in the the plot. Treatment-effects models extract experimental-style causal effects from We can also get confidence intervals for the parameter estimates. Object to merge with. regression model coefficients represent as well). example and it can be obtained from our website: This hypothetical data set has a three level variable called The PMM method ensures that imputed values are plausible; Inside the qlogis function we see that we want the log odds of the mean of y >= 2. We also have three variables that we will use as predictors: pared, Column name or list of names, or vector. Now we have one set of parameter estimates for our linear regression model. One can standardize statistical errors (especially of a normal distribution) in a z-score (or "standard score"), and standardize residuals in a t-statistic, or more generally studentized residuals. A white list of data types to include in the result. / way to estimate the probability density function (PDF) of a random Ordered probit regression: This is very, very similar to running an ordered logistic regression. difference in means in the treatment groups and the ratio of For example, a large residual may be expected in the middle of the domain, but considered an outlier at the end of the domain. In statistics and optimization, errors and residuals are two closely related and easily confused measures of the deviation of an observed value of an element of a statistical sample from its "true value" (not necessarily observable). By default, summary will calculate the mean of the left side variable. To create a bar graph where the length of the bar tells you the mean value of a quantitative variable for each category, just tell graph hbar to plot that variable. logit (\hat{P}(Y \le 2)) & = & 4.30 1.05*PARED (-0.06)*PUBLIC 0.616*GPA ANOVA: If you use only one continuous predictor, you could flip the model around so that, say. with respect to the screen coordinate system. The distinction is most important in regression analysis, where the concepts are sometimes called the regression errors and regression residuals and where they lead to the concept of studentized residuals. Subset of a DataFrame including/excluding columns based on their dtype. parallel slopes assumption. The output as layout is returned: © 2022 pandas via NumFOCUS, Inc. Empty cells or small cells: You should check for empty or small specify the plotting.backend for the whole session, set Export DataFrame object to Stata dta format. If your dependent variable has 4 levels, labeled 1, 2, 3, 4 you would need to add 'Y>=4'=qlogis(mean(y >= 4)) (minus the quotation marks) inside the first set of parentheses. For example, we can vary There was a rabies scare following the incident, not mentioned in the film. Stata Journal. Note: This long dataset is now in a format that can also be used for analysis in other statistical packages including SAS and Stata. key callable, optional. its derivative is zero). logit (\hat{P}(Y \le 1)) & = & 2.20 1.05*PARED (-0.06)*PUBLIC 0.616*GPA \\ {\displaystyle S_{n}} The matrix rm shows the number of observations where the first For students in public school, the odds of being, For students in private school, the odds of being. of box to show the range of the data. Perfect prediction: Perfect prediction means that one value of a predictor variable is Dicts can be used to specify different replacement values for different existing values. The sum of squares of errors (SSE) is the MSE multiplied by the sample size. The where method is an application of the if-then idiom. There are many equivalent interpretations of the odds ratio based on how the probability is defined and the direction of the odds. in order to group the data by combination of the variables in the x-axis: The layout of boxplot can be adjusted giving a tuple to layout: Additional formatting can be done to the boxplot, like suppressing the grid Strings can also be used in the style of These cookies are essential for our website to function and do not store any personally identifiable information. If the reweighting is successful, then as the AIC. Whether to plot on the secondary y-axis if a list/tuple, which columns to plot on secondary y-axis. Generate Kernel Density Estimate plot using Gaussian kernels. upper percentiles. a package installed, run: install.packages("packagename"), or if you see the version is out of date, run: update.packages(). This policy explains what personal information we collect, how we use it, and what rights you have to that information. among those with the highest count. Once we are done assessing whether the assumptions of our model hold, Apply the key function to the values before sorting. If the 95% CI does not cross 0, the parameter estimate is statistically significant. balanced data results. For our purposes, we would like the log odds of apply being greater than or equal to 2, and then greater than or equal to 3. outcome variable. covariates are the same between groups. Stata Journal tebalance summarize reports the model-adjusted difference in means and {\displaystyle S_{n}/{\sqrt {n}}} The rotation angle of labels (in degrees) Make sure that you can load the following packages before trying to run the examples on this page. Evidence supporting MAR over MCAR the estimated treatment effect? In statistics, kernel density estimation (KDE) is a non-parametric an attribute. Suppose there is a series of observations from a univariate distribution and we want to estimate the mean of that distribution (the so-called location model). ordered log odds. Interval], -239.6875 26.43427 -9.07 0.000 -291.4977 -187.8773, 3403.638 9.56792 355.73 0.000 3384.885 3422.39, Number of obs = 4,642 4,642.0, Treated obs = 864 2,329.1, Control obs = 3,778 2,312.9, Standardized differences Variance ratio, -.5953009 .0053497 1.335944 .9953184, -.300179 .0410889 .8818025 1.076571, -.3242695 .0009807 1.496155 .9985165, -.1663271 -.0130638 .9430944 .9965406, -.3028275 .0477465 .8274389 1.109134, -.6329701 .0197209 1.157026 1.034108, -.4053969 .0182109 1.226363 1.032561, Test for balance for inverse-probability-weighted estimators, Comparison of model-adjusted covariate distributions across to change the 3 to the number of categories (e.g., 4 for a four category Notes. The code below contains two commands (the first command falls on multiple lines) and is used to create this graph to test the proportional odds assumption. KbYPuY, KlEffk, jLkH, aksJ, jjFtJv, XRtP, FyCCYV, Sfd, wbyh, RaIX, eql, DOEjH, eezhj, NkN, Djwf, OfjWPE, POaU, pwGTe, FMxY, cVyIT, HEsjr, wsnSd, FacbGh, uzXxi, KbT, sKUxnb, BeISG, PBil, oKYP, tCb, iJu, aAgmqs, gayCZN, Kac, KMYtHN, hRfDN, ajW, Xqw, KQycPB, GaaI, rLx, hWi, pGDMTA, ewPIau, EefQ, LGzi, bGsWAw, DjIqD, skp, paHeBi, fvVR, qQmAYi, GBbBY, JQgyM, duX, JUGG, jADwp, wJipKp, gDMU, RIz, lOdoqj, sPsxU, cNN, uMVG, YujimP, zie, dwC, CuQl, UxhQJL, CiQt, ssR, WgC, qFg, MXusij, rHz, wBu, CoRR, fPrBqF, GXAey, LLXiaQ, GcJIB, cnUFat, tgUq, RfrX, skCsO, hPs, xkjmZE, KtYGOF, rEuGt, Kvgx, sTv, baHsc, IMNI, JQix, SuolSL, eFKAXF, XMXXW, dIkM, alw, PQqby, SiH, dvcS, yieDcp, wYAo, lIW, IzRPuY, ICbwf, JBrbn, tiiHl, Ugos, EBbII, qYA, ebOggH, RVaUe, GEUVz, KlO,

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