what do I do if I have zero values in my data set and want to perform this transformation for a positive skew? It keeps telling me a division of zero was attempted and that the dataset set this a system-missing value
I have a dataset of 16 parameter in 175 water sample. Except one parameter all 15 parameters are skewed. Some get normalized by logarithmic while some don't. To the remaining I performed square root transformation. Now I am confused. Will the result will be valid of Finding a correlation among all the normalized parameters (log vs sq root)?
Thank you for the explanation. It was great. I have a question though... in my transformed data I am having that same issue. After I have transformed with Log10 the skewness shows a normal distribution but either KS or Shapiro-Wilk doesn't. I proceed with Ln and square root transformation and the same is happening. I am dealing with a two-way anova. Some groups end up being normal distributed but some others remain non-normal. Can I choose my independent variables (resulting with normal distribution) and report them under different transformations? I.e., group A was significantly different with a normal distribution (p value of 0.035) after being transformed with log10 while group B was significantly distributed with a square root transformation (p value of 0.012)?? Is there a video where I can see an example of this? Thanks so much for the explanation here!! :)
A very informative video but I have a simple doubt. So if there was another column say "Neutral Skewness" and it had a normal distribution, can we run the the entire dataset when the other two columns i.e. +ve and -ve are normalized. Or should we have to sqrt the third column also to maintain a equality. Thank you
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is .72 an acceptable skewness? I found somewhere that a skew value between .5 and 1 is moderate and that the normality is between 0 and 0.5.
what do I do if I have zero values in my data set and want to perform this transformation for a positive skew? It keeps telling me a division of zero was attempted and that the dataset set this a system-missing value
I have a dataset of 16 parameter in 175 water sample. Except one parameter all 15 parameters are skewed. Some get normalized by logarithmic while some don't. To the remaining I performed square root transformation. Now I am confused. Will the result will be valid of Finding a correlation among all the normalized parameters (log vs sq root)?
on the interpretation of coefficient after finished the regression, is there any influence of using transformed square root data?
Thank you for the explanation. It was great. I have a question though... in my transformed data I am having that same issue. After I have transformed with Log10 the skewness shows a normal distribution but either KS or Shapiro-Wilk doesn't. I proceed with Ln and square root transformation and the same is happening. I am dealing with a two-way anova. Some groups end up being normal distributed but some others remain non-normal. Can I choose my independent variables (resulting with normal distribution) and report them under different transformations? I.e., group A was significantly different with a normal distribution (p value of 0.035) after being transformed with log10 while group B was significantly distributed with a square root transformation (p value of 0.012)?? Is there a video where I can see an example of this? Thanks so much for the explanation here!! :)
I also have the same problem with multiple regression analysis. Did you solve this problem?
5:40 here
A very informative video but I have a simple doubt. So if there was another column say "Neutral Skewness" and it had a normal distribution, can we run the the entire dataset when the other two columns i.e. +ve and -ve are normalized. Or should we have to sqrt the third column also to maintain a equality. Thank you
is it necessary to correct kurtosis for normalization?
Great info