transformation

Hello, I'm very new to statistics and ran into a problem that I don't know the answer to. I want to do a capability analysis, but my data set is not normally distributed, so I transformed it using the Johnson transform in Minitab. Now I had a normal distribution and very good CP, CPK values...

Hi, for a university project I currently have to analyze a dataset inherent to a cluster of stars, in which the two main variables are luminosity and temperature of each star. The main goal is to validate the normality hypothesis in both variables (usually I go with >qqnorm() + >qqline() +...

I am conducting research on the effects of social media usage (frequency of checking and duration) on body image and self esteem n = 264. Every ANOVA is not significant :( however Boxplots have revealed a good number of outliers. When I removed 5 "moderate outliers" (between the mild and extreme...

Consider the function y=ax +b. The term "change of variable" is used for inside changes, i.e. changes that affect the input ( x-) axis values. Can the term "change of variable" be used for changes that affect the output ( y-) axis values?

Hello! This is my first post and I'm hoping to get some help on how to work with transformed data for a research publication I am writing up. Basically what we did was monitor the levels of a particular protein after traumatic brain injury. Our sample sizes were originally about 25 but due...

Hello, I have a negatively skewed Likert scale as my dependent variable and would like to use parametric testing (multiple regression and ANOVA). Does anyone have any advice on the best transformation? Thanks in advance.

Hi There, I would like to know more about transforming data. I know that for parametric statistics data needs to be normally distributed, but I am confused about when this does and does not apply. Doesn't transforming data to make it normally distributed hide any experimental effects you...

Hi, I'm trying to transform a positively skewed variable that includes 10 zeros, so I understand that when using the Log10 function, '+1' should be added, and then all the zeros would become 1. I have performed this, and although the distribution of the variable seems better, unfortunately...

I have two independent groups, (roughly 30 in each) – and their performance on 3 different tasks, there are 10 scores in total for each group. The majority of them are negatively skewed so I know I have to reflect the data before I transform it – if the two groups have different maximum...

Hi, I want to apply box cox transformation on my time series. I was just wondering can I apply the transformation on any time series or do i have to fix up the time series prior to. I have a non stationary time series with deterministic trends.. would i have to make it stationary first or no...

I need to convert F statistics from 1-Way & from 2-Way ANOVAs into Cohen's d. The formula in my handbook: d = (2 * the square root of F) /over/ (the square root of df(error)) My question is: Is df(error) equal to (N - k) - (n - 1)? Or does "error" denote something else? If anyone has a...

I study and insect that has a tendency to produce non-normal data sets. I have transformed my current data set using sqrt, sqrt + 0.5, log, log10 and arcsin (even thought this prob isn't appropriate) transformations, but when I check the assumptions on the transformed data set it's still...

I run the following code in R: boxcox(data, lambda = seq(-2,2), interp=TRUE, plotit=TRUE) Where data is a vector of integers, but I get the error Error: $ operator is invalid for atomic vectors How can I fix this? Furthermore, how can I specify how much I want the lambda to increment by?

Hello! For school, I have decided to do a project on airline reliability; specifically, looking at on-time departures. I decided to use a four factor design with my factors being: airline, airport, day, and time. The data has all been collected. I'm verifying that the conditions of ANOVA...

If X_i is distributed as normal with mean \mu and variance-covariance matrix D,where D is the diagonal matrix, then show that X^TD^{-1}D is distributed as noncentral \chi^2 with k degrees of freedom and noncentrality parameter \mu^TD^{-1}\mu=\lambda.

Let X_i\sim N(\mu_i,\sigma^2) ; where [i=1,2,\ldots,n] show that \bar X and (X_i-\bar X) are independent. If all X_i had same mean \mu then we transform the random variables X_i; [i=1,2,\ldots,n] to Y_1=\bar X Y_2 =X_2-\bar X Y_3 = X_3-\bar X \vdots Y_n = X_n-\bar X...

Hi, I have done a box-cox transformation of my response variable, using the following formula: (Y^lambda - 1)/lambda Previously, I have got some excellent help in understanding the way interpretation works for different levels of Y (q1,median,q3). My formula for inverse transformation is...

Hi, I am reviewing someone's study in which they used z-scores to standardize and compare data. I see no mention of their test for normality and when I looked at their raw data, I found much of it to be heavily skewed and not-normally distributed. I need to determine whether this means...

Hi all. I am using PCA to generate PCs and then using them as independent variables in regression. I added an extra data-step after PCA which is transformation of the PCs e.g. X=prin1^2 This significantly improved the subsequent regression model, however I am not sure if transforming the...

I have the need to take a variable that is normally distributed around 0 and the lower and upper bound is at -1 and 1. I need a transformation to flatten the top and widen the tails. I just happened to think this may be a fishers z I'm looking for. If so I'll mark this thread as solved.