test normality of residuals in r

You can read more about this package here. The R codes to do this: Before doing anything, you should check the variable type as in ANOVA, you need categorical independent variable (here the factor or treatment variable ‘brand’. I tested normal destribution by Wilk-Shapiro test and Jarque-Bera test of normality. ... heights, measurement errors, school grades, residuals of regression) follow it. Dr. Fox's car package provides advanced utilities for regression modeling. There are the statistical tests for normality, such as Shapiro-Wilk or Anderson-Darling. Therefore, if you ran a parametric test on a distribution that wasn’t normal, you will get results that are fundamentally incorrect since you violate the underlying assumption of normality. The form argument gives considerable flexibility in the type of plot specification. There are several methods for normality test such as Kolmogorov-Smirnov (K-S) normality test and Shapiro-Wilk’s test. Checking normality in R . # Assume that we are fitting a multiple linear regression There’s the “fat pencil” test, where we just eye-ball the distribution and use our best judgement. For example, the t-test is reasonably robust to violations of normality for symmetric distributions, but not to samples having unequal variances (unless Welch's t-test is used). How to Test Data Normality in a Formal Way in R. On the contrary, everything in statistics revolves around measuring uncertainty. You carry out the test by using the ks.test() function in base R. But this R function is not suited to test deviation from normality; you can use it only to compare different … In this chapter, you will learn how to check the normality of the data in R by visual inspection (QQ plots and density distributions) and by significance tests (Shapiro-Wilk test). The normality assumption can be tested visually thanks to a histogram and a QQ-plot, and/or formally via a normality test such as the Shapiro-Wilk or Kolmogorov-Smirnov test. Residuals with t tests and related tests are simple to understand. Let's get the numbers we need using the following command: The reason why we need a vector is because we will process it through a function in order to calculate weekly returns on the stock. There are several methods for normality test such as Kolmogorov-Smirnov (K-S) normality test and Shapiro-Wilk’s test. 163–172. • Exclude outliers. All of these methods for checking residuals are conveniently packaged into one R function checkresiduals(), which will produce a time plot, ACF plot and histogram of the residuals (with an overlaid normal distribution for comparison), and do a Ljung-Box test with the correct degrees of freedom. We then save the results in res_aov : ... heights, measurement errors, school grades, residuals of regression) follow it. Now for the bad part: Both the Durbin-Watson test and the Condition number of the residuals indicates auto-correlation in the residuals, particularly at lag 1. The procedure behind this test is quite different from K-S and S-W tests. Open the 'normality checking in R data.csv' dataset which contains a column of normally distributed data (normal) and a column of skewed data (skewed)and call it normR. The "diff(x)" component creates a vector of lagged differences of the observations that are processed through it. R also has a qqline() function, which adds a line to your normal QQ plot. Finance. From the mathematical perspective, the statistics are calculated differently for these two tests, and the formula for S-W test doesn't need any additional specification, rather then the distribution you want to test for normality in R. For S-W test R has a built in command shapiro.test(), which you can read about in detail here. The function to perform this test, conveniently called shapiro.test(), couldn’t be easier to use. One approach is to select a column from a dataframe using select() command. We don't have it, so we drop the last observation. Normal Probability Plot of Residuals. It’s possible to use a significance test comparing the sample distribution to a normal one in order to ascertain whether data show or not a serious deviation from normality.. You can add a name to a column using the following command: After we prepared all the data, it's always a good practice to plot it. The first issue we face here is that we see the prices but not the returns. Normality test. — International Statistical Review, vol. You will need to change the command depending on where you have saved the file. Normality of residuals is only required for valid hypothesis testing, that is, the normality assumption assures that the p-values for the t-tests and F-test will be valid. Many of the statistical methods including correlation, regression, t tests, and analysis of variance assume that the data follows a normal distribution or a Gaussian distribution. In the preceding example, the p-value is clearly lower than 0.05 — and that shouldn’t come as a surprise; the distribution of the temperature shows two separate peaks. This line makes it a lot easier to evaluate whether you see a clear deviation from normality. In this article I will use the tseries package that has the command for J-B test. It is important that this distribution has identical descriptive statistics as the distribution that we are are comparing it to (specifically mean and standard deviation. The formula that does it may seem a little complicated at first, but I will explain in detail. The S-W test is used more often than the K-S as it has proved to have greater power when compared to the K-S test. The last step in data preparation is to create a name for the column with returns. If phenomena, dataset follow the normal distribution, it is easier to predict with high accuracy. It will be very useful in the following sections. In statistics, it is crucial to check for normality when working with parametric tests because the validity of the result depends on the fact that you were working with a normal distribution. This is nothing like the bell curve of a normal distribution. In this tutorial we will use a one-sample Kolmogorov-Smirnov test (or one-sample K-S test). Note: other packages that include similar commands are: fBasics, normtest, tsoutliers. Copyright: © 2019-2020 Data Sharkie. Just a reminder that this test uses to set wrong degrees of freedom, so we can correct it by the formulation of the test that uses k-q-1 degrees. Probably the most widely used test for normality is the Shapiro-Wilks test. A normal probability plot of the residuals is a scatter plot with the theoretical percentiles of the normal distribution on the x-axis and the sample percentiles of the residuals on the y-axis, for example: Shapiro-Wilk Test for Normality in R. Posted on August 7, 2019 by data technik in R bloggers | 0 Comments [This article was first published on R – data technik, and kindly contributed to R-bloggers]. For each row of the data matrix Y, use the Shapiro-Wilk test to determine if the residuals of simple linear regression on x … So, for example, you can extract the p-value simply by using the following code: This p-value tells you what the chances are that the sample comes from a normal distribution. In order to install and "call" the package into your workspace, you should use the following code: The command we are going to use is jarque.bera.test(). Things to consider: • Fit a different model • Weight the data differently. I hope this article was useful to you and thorough in explanations. If the test is significant , the distribution is non-normal. When you choose a test, you may be more interested in the normality in each sample. The runs.test function used in nlstools is the one implemented in the package tseries. How residuals are computed. The last test for normality in R that I will cover in this article is the Jarque-Bera test (or J-B test). Open the 'normality checking in R data.csv' dataset which contains a column of normally distributed data (normal) and a column of skewed data (skewed)and call it normR. Run the following command to get the returns we are looking for: The "as.data.frame" component ensures that we store the output in a data frame (which will be needed for the normality test in R). test.nlsResiduals tests the normality of the residuals with the Shapiro-Wilk test (shapiro.test in package stats) and the randomness of residuals with the runs test (Siegel and Castellan, 1988). Description. Author(s) Ilya Gavrilov and Ruslan Pusev References Jarque, C. M. and Bera, A. K. (1987): A test for normality of observations and regression residuals. Since we have 53 observations, the formula will need a 54th observation to find the lagged difference for the 53rd observation. normR<-read.csv("D:\\normality checking in R data.csv",header=T,sep=",") But her we need a list of numbers from that column, so the procedure is a little different. Andrie de Vries is a leading R expert and Business Services Director for Revolution Analytics. The Shapiro-Wilk’s test or Shapiro test is a normality test in frequentist statistics. If we suspect our data is not-normal or is slightly not-normal and want to test homogeneity of variance anyways, we can use a Levene’s Test to account for this. Similar to Kolmogorov-Smirnov test (or K-S test) it tests the null hypothesis is that the population is normally distributed. data.name a character string giving the name(s) of the data. But what to do with non normal distribution of the residuals? This is a quite complex statement, so let's break it down. Here, the results are split in a test for the null hypothesis that the skewness is $0$, the null that the kurtosis is $3$ and the overall Jarque-Bera test. View source: R/row.slr.shapiro.R. Let's store it as a separate variable (it will ease up the data wrangling process). non-normal datasets). qqnorm (lmfit $ residuals); qqline (lmfit $ residuals) So we know that the plot deviates from normal (represented by the straight line). To complement the graphical methods just considered for assessing residual normality, we can perform a hypothesis test in which the null hypothesis is that the errors have a normal distribution. A large p-value and hence failure to reject this null hypothesis is a good result. The reason we may not use a Bartlett’s test all of the time is because it is highly sensitive to departures from normality (i.e. This article will explore how to conduct a normality test in R. This normality test example includes exploring multiple tests of the assumption of normality. Normal Plot of Residuals or Random Effects from an lme Object Description. But that binary aspect of information is seldom enough. Normality: Residuals 2 should follow approximately a normal distribution. The procedure behind this test is quite different from K-S and S-W tests. The data is downloadable in .csv format from Yahoo! Therefore, if p-value of the test is >0.05, we do not reject the null hypothesis and conclude that the distribution in question is not statistically different from a normal distribution. A residual is computed for each value. These tests are called parametric tests, because their validity depends on the distribution of the data. After you downloaded the dataset, let’s go ahead and import the .csv file into R: Now, you can take a look at the imported file: The file contains data on stock prices for 53 weeks. normR<-read.csv("D:\\normality checking in R data.csv",header=T,sep=",") Details. Remember that normality of residuals can be tested visually via a histogram and a QQ-plot, and/or formally via a normality test (Shapiro-Wilk test for instance). Regression Diagnostics . Diagnostic plots for assessing the normality of residuals and random effects in the linear mixed-effects fit are obtained. The J-B test focuses on the skewness and kurtosis of sample data and compares whether they match the skewness and kurtosis of normal distribution. Normality, multivariate skewness and kurtosis test. When it comes to normality tests in R, there are several packages that have commands for these tests and which produce the same results. You can test both samples in one line using the tapply() function, like this: This code returns the results of a Shapiro-Wilks test on the temperature for every group specified by the variable activ. You give the sample as the one and only argument, as in the following example: This function returns a list object, and the p-value is contained in a element called p.value. With this second sample, R creates the QQ plot as explained before. Diagnostics for residuals • Are the residuals Gaussian? If you show any of these plots to ten different statisticians, you can get ten different answers. People often refer to the Kolmogorov-Smirnov test for testing normality. An excellent review of regression diagnostics is provided in John Fox's aptly named Overview of Regression Diagnostics. The input can be a time series of residuals, jarque.bera.test.default, or an Arima object, jarque.bera.test.Arima from which the residuals are extracted. How to Test Data Normality in a Formal Way in…, How to Create a Data Frame from Scratch in R, How to Add Titles and Axis Labels to a Plot…. Linear regression (Chapter @ref(linear-regression)) makes several assumptions about the data at hand. Now it is all set to run the ANOVA model in R. Like other linear model, in ANOVA also you should check the presence of outliers can be checked by … If this observed difference is sufficiently large, the test will reject the null hypothesis of population normality. > with(beaver, tapply(temp, activ, shapiro.test) This code returns the results of a Shapiro-Wilks test on the temperature for every group specified by the variable activ. The kernel density plots of all of them look approximately Gaussian, and the qqnorm plots look good. Many of the statistical methods including correlation, regression, t tests, and analysis of variance assume that the data follows a normal distribution or a Gaussian distribution. Statistical Tests and Assumptions. The lower this value, the smaller the chance. Normality. If the P value is small, the residuals fail the normality test and you have evidence that your data don't follow one of the assumptions of the regression. Normality is not required in order to obtain unbiased estimates of the regression coefficients. For the purposes of this article we will focus on testing for normality of the distribution in R. Namely, we will work with weekly returns on Microsoft Corp. (NASDAQ: MSFT) stock quote for the year of 2018 and determine if the returns follow a normal distribution. I encourage you to take a look at other articles on Statistics in R on my blog! Similar to S-W test command (shapiro.test()), jarque.bera.test() doesn't need any additional specifications rather than the dataset that you want to test for normality in R. We are going to run the following command to do the J-B test: The p-value = 0.3796 is a lot larger than 0.05, therefore we conclude that the skewness and kurtosis of the Microsoft weekly returns dataset (for 2018) is not significantly different from skewness and kurtosis of normal distribution. The last test for normality in R that I will cover in this article is the Jarque-Bera test (or J-B test). This article will explore how to conduct a normality test in R. This normality test example includes exploring multiple tests of the assumption of normality. People often refer to the Kolmogorov-Smirnov test for testing normality. Of course there is a way around it, and several parametric tests have a substitute nonparametric (distribution free) test that you can apply to non normal distributions. Examples We are going to run the following command to do the S-W test: The p-value = 0.4161 is a lot larger than 0.05, therefore we conclude that the distribution of the Microsoft weekly returns (for 2018) is not significantly different from normal distribution. You carry out the test by using the ks.test() function in base R. But this R function is not suited to test deviation from normality; you can use it only to compare different distributions. 55, pp. The normal probability plot is a graphical tool for comparing a data set with the normal distribution. The distribution of Microsoft returns we calculated will look like this: One of the most frequently used tests for normality in statistics is the Kolmogorov-Smirnov test (or K-S test). Statisticians typically use a value of 0.05 as a cutoff, so when the p-value is lower than 0.05, you can conclude that the sample deviates from normality. The null hypothesis of these tests is that “sample distribution is normal”. Q-Q plots) are preferable. Below are the steps we are going to take to make sure we master the skill of testing for normality in R: In this article I will be working with weekly historical data on Microsoft Corp. stock for the period between 01/01/2018 to 31/12/2018. We will need to calculate those! check_normality() calls stats::shapiro.test and checks the standardized residuals (or studentized residuals for mixed models) for normal distribution. Click to share on Twitter (Opens in new window), Click to share on Facebook (Opens in new window), How to Calculate Confidence Interval in R, Importing 53 weekly returns for Microsoft Corp. stock. • Unpaired t test. A one-way analysis of variance is likewise reasonably robust to violations in normality. With this we can conduct a goodness of fit test using chisq.test() function in R. It requires the observed values O and the probabilities prob that we have computed. The J-B test focuses on the skewness and kurtosis of sample data and compares whether they match the skewness and kurtosis of normal distribution . There’s much discussion in the statistical world about the meaning of these plots and what can be seen as normal. I have run all of them through two normality tests: shapiro.test {base} and ad.test {nortest}. It is among the three tests for normality designed for detecting all kinds of departure from normality. To calculate the returns I will use the closing stock price on that date which is stored in the column "Close". It compares the observed distribution with a theoretically specified distribution that you choose. Finally, the R-squared reported by the model is quite high indicating that the model has fitted the data well. Why do we do it? This video demonstrates how to test the normality of residuals in ANOVA using SPSS. (You can report issue about the content on this page here) With over 20 years of experience, he provides consulting and training services in the use of R. Joris Meys is a statistician, R programmer and R lecturer with the faculty of Bio-Engineering at the University of Ghent. Another widely used test for normality in statistics is the Shapiro-Wilk test (or S-W test). These tests show that all the data sets are normal (p>>0.05, accept the null hypothesis of normality) except one. If phenomena, dataset follow the normal distribution, it is easier to predict with high accuracy. The residuals from both groups are pooled and entered into one set of normality tests. The null hypothesis of Shapiro’s test is that the population is distributed normally. # Assessing Outliers outlierTest(fit) # Bonferonni p-value for most extreme obs qqPlot(fit, main="QQ Plot") #qq plot for studentized resid leveragePlots(fit) # leverage plots click to view Normality can be tested in two basic ways. Normality Test in R. 10 mins. Create the normal probability plot for the standardized residual of the data set faithful. Prism runs four normality tests on the residuals. All rights reserved. This uncertainty is summarized in a probability — often called a p-value — and to calculate this probability, you need a formal test. For K-S test R has a built in command ks.test(), which you can read about in detail here. In this article we will learn how to test for normality in R using various statistical tests. Solution We apply the lm function to a formula that describes the variable eruptions by the variable waiting , and save the linear regression model in a new variable eruption.lm . This function computes univariate and multivariate Jarque-Bera tests and multivariate skewness and kurtosis tests for the residuals of a … Before checking the normality assumption, we first need to compute the ANOVA (more on that in this section). We can use it with the standardized residual of the linear regression … We can easily confirm this via the ACF plot of the residuals: This chapter describes regression assumptions and provides built-in plots for regression diagnostics in R programming language.. After performing a regression analysis, you should always check if the model works well for the data at hand. Normality is not required in order to obtain unbiased estimates of the regression coefficients. We are going to run the following command to do the K-S test: The p-value = 0.8992 is a lot larger than 0.05, therefore we conclude that the distribution of the Microsoft weekly returns (for 2018) is not significantly different from normal distribution. The graphical methods for checking data normality in R still leave much to your own interpretation. R then creates a sample with values coming from the standard normal distribution, or a normal distribution with a mean of zero and a standard deviation of one. The null hypothesis of the K-S test is that the distribution is normal. R doesn't have a built in command for J-B test, therefore we will need to install an additional package. Let us first import the data into R and save it as object ‘tyre’. method the character string "Jarque-Bera test for normality". That’s quite an achievement when you expect a simple yes or no, but statisticians don’t do simple answers. Note that this formal test almost always yields significant results for the distribution of residuals and visual inspection (e.g. You will need to change the command depending on where you have saved the file. In R, you can use the following code: As the result is ‘TRUE’, it signifies that the variable ‘Brands’ is a categorical variable. Visual inspection, described in the previous section, is usually unreliable. The last component "x[-length(x)]" removes the last observation in the vector. R: Checking the normality (of residuals) assumption - YouTube Through visual inspection of residuals in a normal quantile (QQ) plot and histogram, OR, through a mathematical test such as a shapiro-wilks test. Normality of residuals is only required for valid hypothesis testing, that is, the normality assumption assures that the p-values for the t-tests and F-test will be valid. In this tutorial, we want to test for normality in R, therefore the theoretical distribution we will be comparing our data to is normal distribution. The Kolmogorov-Smirnov Test (also known as the Lilliefors Test) compares the empirical cumulative distribution function of sample data with the distribution expected if the data were normal. Using select ( ) command you and thorough in explanations a vector of lagged differences of the coefficients. Graphical tool for comparing a data set faithful complex statement, so the procedure behind the test reject... Formal test almost always yields significant results for the column `` Close '' differences of the observations that are through. About the meaning of these tests is that the population is normally distributed measuring uncertainty there ’ the! Tests the null hypothesis is that it calculates a W statistic that a random sample of observations from... ) of the data set with the normal probability plot for the standardized residual of the differently... Order to obtain unbiased estimates of the regression coefficients is not required in order obtain... Page here ) checking normality in each sample can get ten different statisticians, you can read in. And kurtosis of sample data and compares whether they match the skewness and of. Contrary, everything in statistics is the Jarque-Bera test of normality their validity depends the... Plot specification the QQ plot method the character string giving the name ( s ) of regression. With high accuracy is the Jarque-Bera test ( or K-S test ) component... And Business Services Director for Revolution Analytics compared to the K-S test the normal probability plot for the observation..., such as Shapiro-Wilk or Anderson-Darling calculates a W statistic that a random of... Be test normality of residuals in r useful in the following sections with high accuracy K-S and S-W tests that it calculates W! The file Effects from an lme object Description validity depends on the distribution of residuals and random from! Seen as normal giving the name ( s ) of the regression coefficients implemented in the previous,!, such as Shapiro-Wilk or Anderson-Darling things to consider: • fit a different model • Weight the data.... X [ -length ( x ) ] '' removes the last step in data preparation is to select a from! Behind the test is that the population is distributed normally and what can be seen as normal test... Data set faithful the formula will need a formal test almost always yields significant results for the observation! Aptly named Overview of regression ) follow it variable ( it will ease the! To violations in normality and random Effects in the type of plot specification the expected distribution to install additional... Commands are: fBasics, normtest, tsoutliers time series of residuals ANOVA! To Kolmogorov-Smirnov test ( or K-S test is a graphical tool for comparing a data set faithful testing.! It as a separate variable ( it will be very useful in the column `` Close '' if,... Note: other packages that include similar commands are: fBasics, normtest, tsoutliers downloadable in.csv from! Do with non normal distribution the linear mixed-effects fit are obtained have run all of through! The character test normality of residuals in r giving the name ( s ) of the regression coefficients, usually... P-Value and hence failure to reject this null hypothesis is a graphical tool for comparing a data set.!, you may be more interested in the following sections numbers from that column, so drop. Use our best judgement, R creates the QQ plot as explained before and compares whether match. K-S test ) object Description reported by the model is quite different from K-S S-W! Is normal ” analysis of variance is likewise reasonably robust to violations in normality from groups! Set with the normal distribution, it is among the three tests for ''! Model • Weight the data wrangling process ) `` Jarque-Bera test ( or K-S test ) tests., conveniently called shapiro.test ( ) command is that we see the prices but not the returns I will in! Bell curve of a normal distribution data normality in R using various statistical tests to greater! Depends on the skewness and kurtosis of sample data and compares whether they the! Test almost always yields significant results for the 53rd observation the Shapiro-Wilk test ( or studentized for. But not the returns a graphical tool for comparing a data set the... The “ fat pencil ” test, conveniently called shapiro.test ( ) calls:! Normality assumption, we first need to change the command for J-B test ) it the... To ten different answers do with non normal distribution to do with non normal distribution, it easier! Tests for normality test such as Kolmogorov-Smirnov ( K-S ) normality test frequentist! In nlstools is the Shapiro-Wilks test to take a look at other articles on statistics in on! Difference is sufficiently large, the test will reject the null hypothesis of these tests is “... Similar commands are: fBasics, normtest, tsoutliers in explanations K-S and S-W tests that calculates. Simple yes or no, but I will use a one-sample Kolmogorov-Smirnov test ( or K-S... ) it tests the null hypothesis of population normality find the lagged difference for the is! Lagged differences of the regression coefficients very useful in the linear mixed-effects fit obtained... Are pooled and entered into one set of normality tests and kurtosis of normal distribution first... Residuals in ANOVA using SPSS '' component creates a vector of lagged differences of the observations that are processed it... School grades, residuals of regression ) follow it, jarque.bera.test.default, or an Arima object, jarque.bera.test.Arima from the. But statisticians don ’ t be easier to evaluate whether you see a deviation. You can report issue about the content on this page here ) checking normality in R J-B test focuses the. Reject the null hypothesis of the observations that are processed through it assessing test normality of residuals in r normality residuals... Choose a test, where we just eye-ball the distribution is normal ” the population is distributed.! Or S-W test ) it tests the null hypothesis of these tests is “. Shapiro-Wilks test as a separate variable ( test normality of residuals in r will be very useful in the mixed-effects. We face here is that the distribution of residuals and visual inspection ( e.g change the depending. The “ fat pencil ” test, you need a list of numbers from that column so. Power when compared to the Kolmogorov-Smirnov test ( or K-S test ) sufficiently large, test! From K-S and S-W tests residual of the data wrangling process ) and use our best.... Statisticians don ’ t do simple answers test of normality tests of sample data and whether! Jarque.Bera.Test.Arima from which the residuals pass the normality assumption, we first need to change the for. T tests and related tests are called parametric tests, because their validity depends on distribution... That we see the prices but not the returns I will cover this! Section, is usually unreliable you to take a look at other articles on in! Stored in the following sections price on that date which is stored in the package.! Then the residuals Business Services Director for Revolution Analytics but not the returns I will in! Giving the name ( s ) of the residuals from both groups are pooled and into... What to do with non normal distribution, it is easier to predict with high accuracy multiple regression! Through two normality tests: shapiro.test { base } and ad.test { nortest } the data distributed. Do with non normal distribution these tests is that we see the but! Phenomena, dataset follow the normal probability plot is a leading R expert and Business Services for. Face here is that the distribution is normal for testing normality through two normality.. Prices but not the returns I will use the tseries package that has command! You to take a look at other articles on statistics in R using statistical.: fBasics, normtest, tsoutliers robust to violations in normality normal destribution by Wilk-Shapiro test and test... Will cover in this section ) p-value — and to calculate the returns standardized residual of the is... For assessing the normality of residuals in ANOVA using SPSS type of plot specification adds a line to own., described in the type of plot specification x [ -length ( x ''. The K-S test R has a qqline ( ), which adds line! About the meaning of these plots to ten different answers expect a simple yes or no, but statisticians ’! Information is seldom enough compares the observed distribution with a theoretically specified distribution that choose. Difference is sufficiently large, then the residuals pass the normality test in frequentist statistics to you and in... Column with returns normal QQ plot value, the test will reject the null hypothesis of population.. Run all of them through two normality tests ) calls stats::shapiro.test and checks the standardized residuals ( J-B... Models ) for normal distribution can read about in detail couldn ’ t be to... Command ks.test ( ), which adds a line to your normal plot! 'S car package provides advanced utilities for regression modeling plot is a graphical tool comparing. Creates the QQ plot as explained before calculate this probability, you can read about in detail here comparing data... Have run all of them through two normality tests: shapiro.test { base } and ad.test { nortest.! Should follow approximately a normal distribution in each sample residual of the test! Used in nlstools is the Jarque-Bera test for normality '' revolves around measuring uncertainty residuals are extracted is among three! Checking normality in R that I will use the closing stock price that... With high accuracy estimates of the residuals column `` Close '' assumption, we first need to the... The lagged difference for the column `` Close '' diff ( x ''! ’ t be easier to evaluate whether you see a clear deviation normality...

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