There are a number of ways to estimate the deviation, the most common method is to use the standard deviation which is is a root sum of deviations squared. That just means is it basically an average deviation if you look at how each result compares to the average result. I typically use that as it works well in most situations. Any spread sheet will just calculate it for you, as will almost all modern calculators with even basic statistic packages.jabba359 wrote:
To pick your brain just a bit more, how do you determine the deviation value?
It is an interesting material to cut because it is so abrasive. Practically I would have sharpened it after 50 cuts as it was already suffering from lack of performance. Initially when the blade is properly sharpened it just does long zip cuts. As work is done and the blade blunts then you have to switch to shorter cuts, multiple slices. But the time I hit ~100 cuts with each blade it was taking almost 10 times as many cuts to make one section of carpet. If I wasn't doing some kind of comparison I would have sharpened it before then.Surfingringo wrote:I was interested to see how that cast 440c performed on a task like this. Seems like some of the qualities that steel is purported to have might work well on material like carpet.
I didn't do one run, I did two with both knives and averaged the results. However even if you do one run you can still quote a standard deviation if the population statistics are known, or can be inferred. For example if you make a measurement with a normal weight scale you can quote a reading such as 1.6 (1) grams where the +/- 1 comes from the population statistics and it represents the precision of the scale from the inherent properties. Given the carpet cutting I have done in the past, i would infer a 50% +/- on one run for carpet cutting when the number of cuts is large and the carpet is random sampled. This is likely a bit larger than the actual deviation however I prefer to err on the side of caution and produce type II errors (lack of a claim of significance) vs type I errors (false claims of significance).arty wrote:How do you get a standard deviation and a mean when the N = 1?
A standard deviation doesn't assume a normal distribution, the standard deviation is distribution specific, i.e., it can be calculated for any given distribution. A standard deviation is just the root of the variance, and the variance is just the expected value of the squared deviation of a given distribution. The assumption of normality comes in when you do something like say "95% of measurements fall within +/- 2 standard deviations", that is only true for the normal distribution. Many measurements are not-normal, a lot tend to be binomial as they are based on discrete events which are not continuous. For example when you measure something with a ruler and it is say 10.1 cm then if you were to measure it repeatedly it would follow a binomial, but if you drew a normal curve it would well approximate it. The reason we use normal statistic inference often anyway is that they are simply much easier to calculate.The SD tells you where most of the scores in a distribution will fall and assumes a normal distribution.
Yes and no, in the same way if you replace the handle of a hammer is it still the same hammer? It contains some of that carpet but I have removed a lot of it from cutting and added to it. The main source of it is renovations. I help friends/family out with these projects as long as I can salvage it. I do this for two reasons. First, I actually do want/need the things I take which have no value to almost anyone else. However as important this "payment" keeps them from feeling they owe me anything as that can lead to a lopsided relationship. This is why I always have massive amount of scrap wood, ropes, cardboard, carpet, tires, etc. . I repurpose a lot of it, some directly and some indirectly.KevinOubre wrote:Cliff is that the same stock of carpet from the Normark/Wilson/K2 video?
Where did you get the idea that a standard deviation is only used for normal distribution? All distributions have standard deviations as it is just the root of the variance. When you are introduced to statistics the normal distribution is the one which is first shown because it has some very nice properties and in general a lot of physical behaviors are normal-like. However it isn't the case that only normal distributions have statistics, they all have means, standard deviations, etc. .arty wrote:...and it doesn't make sense to me to compute it if you don't have a normal distribution of scores.