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PDF filler has been fantastic for me as a small business owner. It truly is the one stop shop for all my business document needs. They handle everything expertly. Totally takes out the stress of filling out documents and sending them to the IRS. I can just fill them out and click my way through the process with ease.
2018-02-06
Wish we had a phone number to speak to customer service directly. I am old school and like to get directions with a call otherwise you have a great service.
2019-02-20
Avis personnel pour une PME
Très belle expérience, que je continue encore à ce jour.
Son aspect ludique, sobre et sophistiqué à la fois, d'un simple logiciel PDF il en devient une ressource majeure pour une entreprise, quel que soit le domaine.
Franchement, je n'en ai pas trouvé, peut être que mon avis n'est pas suffisamment objectif pour répondre honnêtement à cette question.
2023-03-12
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2022-11-01
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Found it very user friendly when using it to get signatures. The audit trail is also simple and understandable in terms of tracking.
2021-10-18
cancelled as a gesture of goodwill
I downloaded the software and although it has a lot of good features, I didn't really need it. I thought I had cancelled right then, but I haven't found any more mail from me. Now the annual amount has been debited and I have contacted their support. After I explained that I didn't actually use the software, the subscription contract was subsequently and I was refunded the amount.
2021-02-16
The PDF filler application and the great tool list
The PDF filler application and the service from customer service and billing were all really good. The application is very helpful in this format for small business needs. Would recommend.
2021-01-22
This is super easy and when I needed…
This is super easy and when I needed something, I went on chat and voila, he told me exactly how to split a file and done!!!
2021-01-06
Superb customer service and an amazing program! I used the program several times. I love all the features and it’s super easy to use. Highly recommended.
2020-04-28
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Why do we do log transformation?
The log transformation can be used to make highly skewed distributions less skewed. This can be valuable both for making patterns in the data more interpretable and for helping to meet the assumptions of inferential statistics. Figure 1 shows an example of how a log transformation can make patterns more visible.
Why do we transform data?
Transforms are usually applied so that the data appear to more closely meet the assumptions of a statistical inference procedure that is to be applied, or to improve the interpretability or appearance of graphs. Nearly always, the function that is used to transform the data is invertible, and generally is continuous.
What happens if log transformation does not normalize data?
1 Answer. Log transformation leads to a normal distribution only for log-normal distributions. Not all distributions are log-normal, meaning they will not become normal after the log transformation.
Why we use log linear model?
If you use natural log values for your dependent variable (Y) and keep your independent variables (X) in their original scale, the econometric specification is called a log-linear model. These models are typically used when you think the variables may have an exponential growth relationship.
How do you convert skewed data in R?
square-root for moderate skew: sort(x) for positively skewed data, log for greater skew: log10(x) for positively skewed data, inverse for severe skew: 1/x for positively skewed data. Linearity and heteroscedasticity:
How do I standardize data in R?
To standardize your data, i.e., data with a mean of 0 and a standard deviation of 1, you can use the scale function from the base package which is a generic function whose default method centers and/or scales the columns of a numeric matrix.
What does the log function do in R?
R log Function. Log() function computes natural logarithms (Ln) for a number or vector. Log10 computes common logarithms (Lg). Log2 computes binary logarithms (Log2).
What is log transformation in dip?
Log transformation The value 1 is added to each of the pixel value of the input image because if there is a pixel intensity of 0 in the image, then log (0) is equal to infinity. During log transformation, the dark pixels in an image are expanded as compare to the higher pixel values.
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