When your day-to-day tasks scope includes a lot of document editing, you already know that every file format needs its own approach and often specific software. Handling a seemingly simple LOG file can often grind the whole process to a halt, especially if you are attempting to edit with inadequate tools. To avoid this sort of troubles, find an editor that can cover all your needs regardless of the file format and cut off point in LOG without roadblocks.
With DocHub, you will work with an editing multitool for virtually any situation or file type. Reduce the time you used to invest in navigating your old software’s functionality and learn from our intuitive interface as you do the work. DocHub is a efficient online editing platform that handles all of your file processing needs for virtually any file, such as LOG. Open it and go straight to efficiency; no prior training or reading guides is needed to reap the benefits DocHub brings to papers management processing. Start with taking a couple of minutes to create your account now.
See improvements in your papers processing right after you open your DocHub account. Save time on editing with our one solution that will help you become more efficient with any document format with which you need to work.
hi in this video were going to work with the normal distribution and find cutoff points corresponding to a given percentile lets try it with an example the average daily high temperature in June and ly is 77 degrees Fahrenheit with a standard deviation of 5 degrees Fahrenheit suppose that the temperatures in June closely follow a normal distribution how cold are the coldest 10% of the days during June in LA were given a normal distribution so the first thing we should do is to draw a curve and Mark our mean then we want to think about where does this observation live the cutoff point for the coldest 10% of the days in June in LA since as the coldest 10% its going to be located at the lower end of the distribution were kind of guesstimating a cut-off your ear there and were interested in this unknown observation X were working with a normal distribution we have some percentiles it makes sense to think about z-scores usually we calculate a z-score as an observation minus the mean
