Normal and lognormal distribution

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Normal distributions can be added and subtracted directly, with the means combining arithmetically and variances adding. For lognormal distributions, the means and variances cannot be combined in the same way due to their multiplicative nature, requiring transformation into a normal distribution for calculations. Statistical independence is crucial when adding or subtracting distributions, as it affects the resulting mean and variance. Resources like online statistics courses or updated textbooks can provide clearer explanations and examples. Understanding these rules simplifies the process of working with normal and lognormal distributions.
omgitsroy326
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Could someone explain to me the simple rules of adding subtracting multiplying dividing Normal and lognormal distributions?

My profs notes are very messy and it's really hard to keep up w/ also the supplied book was published in 1964 w/ no updates. Possibly a site?

I just need simple rules to follow which will make it easier for me to solve. thanks again
 
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also how statistical independence plays a role in adding and subtracting two or more stdev and mean
 

Thread 'Hypothesis testing: Defining H0, HA hypotheses so that ( H_A)_A' makes sense'

The standard _A " operator" maps a Null Hypothesis Ho into a decision set { Do not reject:=1 and reject :=0}. In this sense ( HA)_A , makes no sense. Since H0, HA aren't exhaustive, can we find an alternative operator, _A' , so that ( H_A)_A' makes sense? Isn't Pearson Neyman related to this? Hope I'm making sense. Edit: I was motivated by a superficial similarity of the idea with double transposition of matrices M, with ## (M^{T})^{T}=M##, and just wanted to see if it made sense to talk...
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