Indeed - data recording resolution is a better way to describe it. As far as digital acquisition goes, it depends on the measurement being taken - in the engineering world, our measurements are, for the most part, digitally acquired, but analog instrumentation or other types of data will not necessarily be associated with an A/D converter. Whether the conversation is about bit depth or the resolution of an analog device, the result and cause are essentially the same.
Data is data - if you have repeated values from what ought to be a continuous variable, then it's evidence of a resolution problem - your measuring device isn't precise enough. In any case, if the data is valid, leave it in. You'll see a vertical jump on the graph at your repeated value, which is a good way to spot resolution problems with instrumentation. Does that make sense?
Do you not mean your data recording resolution? Chances are it's not the measurement but the digital acquisition of the measurement's resolution that's at issue with repeated values. Analog to digital conversion of less than 12 bit is awful and 12 bit is bad but around 16 bit gets very good. I guess it depends on what you need. Cheap A/D is easier these days than when I was growing up. If you are not at least using 16 bit, look at that first and the quality of your sensors and outputs. Nice vid.
I'm confused on one issue - Your graphical explanation of the average uses the median to calculate the average. That is, you organize the data and find the value 50% along the way to calculate the average. Then you compare this to the average calculated with the mean formula. Are these values close but not the same?
This is definitely pedantic, but the approach of assigning frequencies as p = n/N is biased when modeling sample data - median ranks (or Benard's approximation, Kaplan-Meier, etc) should be used instead.
If I have similar data, but also have repeated values, o do i have to remove the repeated values? or should I keep them, because with repeated values my graph looks kind of odd...Pls respond
There is no way someone could explain me CDF easier than this man.
Man you're awesome! Never thought plotting a CDF in Excel was that easy!
I've been always using tools in Linux, etc. but this one is amazing!
this is a decade later, but thank you, this helped me with My decisions making under uncertainty class.
THANKS ALOT you remind me of changing title and axises
Thank you very much Sir!!, you saved me a lot of time trying to figure this out.
Indeed - data recording resolution is a better way to describe it. As far as digital acquisition goes, it depends on the measurement being taken - in the engineering world, our measurements are, for the most part, digitally acquired, but analog instrumentation or other types of data will not necessarily be associated with an A/D converter. Whether the conversation is about bit depth or the resolution of an analog device, the result and cause are essentially the same.
Very helpful! Thank you very much! !
Now it is time for PDF...
---one struggling with homework
Here is the second part for those who need it
Thanks heaps for this, very helpful and clear examples
That's really helpful! Thanks a lot! Is there an explanation on how to plot CCDF? (Complementary Cumulative Distribution Function)
tnx a great relearning/review ,more power
Thanks for the review in cumulative probability.
It is really helpful for me. Thank you.
Thank you SO MUCH! This video was so helpful.
Thanks a lot. Saved my day.
Data is data - if you have repeated values from what ought to be a continuous variable, then it's evidence of a resolution problem - your measuring device isn't precise enough. In any case, if the data is valid, leave it in. You'll see a vertical jump on the graph at your repeated value, which is a good way to spot resolution problems with instrumentation. Does that make sense?
Thank you so much ! Very helpful !
Do you not mean your data recording resolution? Chances are it's not the measurement but the digital acquisition of the measurement's resolution that's at issue with repeated values. Analog to digital conversion of less than 12 bit is awful and 12 bit is bad but around 16 bit gets very good. I guess it depends on what you need. Cheap A/D is easier these days than when I was growing up. If you are not at least using 16 bit, look at that first and the quality of your sensors and outputs. Nice vid.
excellent video... thank you very much!
I'm confused on one issue - Your graphical explanation of the average uses the median to calculate the average. That is, you organize the data and find the value 50% along the way to calculate the average. Then you compare this to the average calculated with the mean formula. Are these values close but not the same?
but if the values are repeating it is possible that have the same frequency?
Just a small question: CDF tell us that the probability of a particular value on x axis is equal to value on y axis right?
Ok basically what i have is distances for a list of locations... So in this case should i take the repeated values?
Great, thanks mate.
thank u so muuuuuuuuuuuch :D
thanks :D awesome
doesnt look like a normal distribution, looks uniform to me
True, a normal distribution has a stretched 'S' look to it.
This is definitely pedantic, but the approach of assigning frequencies as p = n/N is biased when modeling sample data - median ranks (or Benard's approximation, Kaplan-Meier, etc) should be used instead.
Thanks! :)
thanks
If I have similar data, but also have repeated values, o do i have to remove the repeated values? or should I keep them, because with repeated values my graph looks kind of odd...Pls respond
i have the same question
What’s the largest value?
*you're OR you are