I tried a lot of method of data transformation method but I did not succeed. while performing residual analysis, I noticed that Percentile Vs residual isnot linear, even Residual Vs predicted is lineary and not randomly distributed. Performance analyzes were successfully done but doing graphical residual analysis, I observed the trend to be a bit linear as shown below. I am currently developing a model based on Neural Networks.
Thank you in advance and hope that my questions are clear. May I know the definition of ‘percentage data’ refers to “%” in which the range is from 0-100, or also other forms of ‘percentage’ such as “%/day” or “% d-1”? (3) I understand that percentage data should be transform prior to statistical analysis. (2) Similarly, when transformation is required in case of non-normal distribution, should I transform data for each replicate first, and then calculate mean and standard deviation? or directly transform the mean? (1) When I want to do normality/homogeneity test, should I use the mean (from three replicates) or all the values from three replicates? And for these tests should be done within variable (mean-6 values replicate-18 values) or within timepoint (mean-3 values replicate-9 values) or within treatment (mean-1 values replicates-3 values)? Also, I want to do correlation test between variables 1 and 2 (e.g. My ultimate goals is to do statistical test to investigate effects of timepoint, treatment and their interactions on variables 1 and 2 (e.g. For each timepoint, I have three treatments, treatment 1, 2 and 3. For each variable, I have two timepoints, timepoint 1 and timepoint 2. I am in biology background and very new to statistical analyses. If data is not symmetric, sometimes it is useful to make a transformation whereby the transformed data is symmetric and so can be analyzed more easily.
Chi-square, Kolmogorov-Smironov, Shapiro-Wilk, Jarque-Barre, D’Agostino-Pearson) Review the distribution graphically (via histograms, boxplots, QQ plots).This can be done via the following approaches: Since a number of the most common statistical tests rely on the normality of a sample or population, it is often useful to test whether the underlying distribution is normal, or at least symmetric.
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