Linked Questions

0
votes
0answers
9k views

Z Score for Non Normal Data [duplicate]

I am looking for some direction to see if there is an equivalent metric to a z score that could be used to quickly identify individual values in a non-normal distribution that are not likely to occur (...
1
vote
1answer
79 views

Method to determine outliers with a skewed dataset [duplicate]

How can we find outliers in a dataset with a (highly) skewed distribution? With a normal distribution, is it well documented to use 2 x Standard Deviation or the upper boundary of the box plot (1.5 x ...
2
votes
1answer
77 views

Detecting outliers in time-series if I don't have a “normal” dataset [duplicate]

I have been trying to detect anomalies in my time-series dataset. What I am trying to accomplish is the following: I have a multivariate dataset, where two columns are of special interest. One tells ...
0
votes
0answers
48 views

For univariate outlier analysis should I use z score if my data is skewed? [duplicate]

If my data is skewed, does it mean that my data does not follow a normal distribution? How do we define various distributions? What type of outlier analysis do I perform for the different type of ...
34
votes
2answers
10k views

Is there a boxplot variant for Poisson distributed data?

I'd like to know if there is a boxplot variant adapted to Poisson distributed data (or possibly other distributions)? With a Gaussian distribution, whiskers placed at L = Q1 - 1.5 IQR and U = Q3 + 1....
19
votes
1answer
7k views

Can we use leave one out mean and standard deviation to reveal the outliers?

Suppose I have normally distributed data. For each element of the data I want to check how many SDs it is away from the mean. There might be an outlier in the data (likely only one, but might be also ...
21
votes
1answer
9k views

Detecting outliers in count data

I have what I naively thought to be a fairly straight forward problem that involves outlier detection for many different sets of count data. Specifically, I want to determine if one or more values in ...
7
votes
2answers
12k views

Why does Tableau's Box/Whisker plot show outliers automatically and how can I get rid of it?

I have a data set shown as box-whisker graphs after disaggregating. See below. I am wondering why Tableau (the product I am using) automatically plots a whole bunch of values outside the box-whisker. ...
4
votes
1answer
17k views

how to determine skewness from histogram with outliers?

I have the following histogram created in Minitab. I am wondering whether this histogram is actually positively skewed, negatively skewed, or symmetric. By observing the graph itself, it seems that ...
6
votes
1answer
7k views

When finding outliers from the Interquartile range why I have to multiply by 1.5?

I was looking at the outlier detection formula which uses the IQR and I wonder why it should be multiplied by 1.5? Can the constant be increased i.e 3 or 6 to be more "acid" if so under what criteria?
2
votes
1answer
3k views

Using percentiles and inter-quartile-range for outlier detection in skewed data

I am analyzing the age of a certain group of people and I want to use percentiles and inter-quartile-range in the data to flag possible outliers. I am getting Q1 - 25th percentile, Q3 - 75th ...
1
vote
0answers
5k views

threshold based on mean and standard deviation

I have a time series of 70000 data points. I want to separate samples of this time series which have very large values as compared to the time series. How can i threshold sample to separate. What i ...
3
votes
1answer
137 views

In R, how to detect possible outliers in right skewed data assuming Poisson distribution?

I am attempting to identify possible outliers in data which is skewed to the right and I assume it is Poisson distributed. I am a novice in all things statistics, and the following may be utterly ...
1
vote
0answers
59 views

How to properly ignore results with high variance

I'm trying to estimate performance results of different configurations. In each test one machine is generating requests to a server for x minutes. The output is: 1. Number of attempts 2. Number of ...
1
vote
1answer
18 views

Consequences not fulfilling normality assumption when looking for outliers

Taking a large (n >> 10 000) data set where the population is clearly not normal and detecting/testing for outliers using mean +/- 3 standard deviations. Multiple colleagues of mine use this ...