how to find range of grouped data
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Home range is the area where an animal lives and travels in. It is closely related to, but not identical with, the concept of "territory".
The concept that can be traced back to a publication in 1943 by W. H. Burt, who constructed maps delineating the spatial extent or outside boundary of an animal's movement during the course of its everyday activities. Associated with the concept of a home range is the concept of a utilization distribution, which takes the form of a two dimensional probability density function that represents the probability of finding an animal in a defined area within its home range. The home range of an individual animal is typically constructed from a set of location points that have been collected over a period of time identifying the position in space of an individual at many points in time. Such data are now collected automatically using collars placed on individuals that transmit through satellites or using mobile cellphone technology the position of the animal, using global positioning systems (GPS) technology, at regular intervals .
The simplest way to draw the boundaries of a home range from a set of location data is to construct the smallest possible convex polygon around the data. This approach is referred to as the minimum convex polygon (MCP) method which is still widely employed, but has many drawbacks including often overestimating the size of home ranges.
The best known methods for constructing utilization distributions are the so-called bivariate Gaussian or normal distribution kernel methods. This group of methods is part of a more general group of parametric kernel methods that employ distributions other than the normal distribution as the kernel elements associated with each point in the set of location data.
Recently, the kernel approach to constructing utilization distributions was extended to include a number of nonparametric methods such as the Burgman and Fox's alpha-hull method. and Getz and Wilmers local convex hull (LoCoh) method This latter method has now been extended from a purely fixed-point LoCoH method to fixed radius and adaptive point/radius LoCoH methods.
Although, currently, more software is available to implement parametric than nonparametric methods (because the latter approach is newer), the cited papers by Getz et al. demonstrate that LoCoH methods generally provide more accurate estimates of home range sizes and have better convergence properties as sample size increases than parametric kernel methods.
Computer packages for implementing parametric and nonparametric kernel methods are available online.
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Answers:This might be something a pivot table would be good for, but I've never used them, so I'll suggest something else... Create a table somewhere, as you mentioned, with your thresholds. Say in AA1:AB10 AA1:AA10 = the lower limit (i.e. 0, 201, 301...) AB1:AB10 = upper limit (i.e. 200, 300, 400...) In your new column next to your data, use a VLOOKUP formula to look at your data (say in B1) and return the threshold it belongs to: = VLOOKUP( B1, $AA$1:$AB$10, 1, TRUE) Copy/drag the formula down. The above will result in either "0" or "201" or "301" for each value in column B. If this is all you use, the second column of your lookup table isn't necessary. However, if you want the result to be "0 - 200" or "201 - 300" then use this formula: = VLOOKUP( B1,$AA$1:$AB$10, 1, TRUE)&" - " &VLOOKUP( B1,$AA$1:$AB$10, 2, TRUE) The "2" tells it to return a result from the second column (AB). The TRUE tells it to match with B1 or the next closest but lower value. (FYI: FALSE demands an exact match only).
Answers:Well funny you should ask that I have a GCSE module 1 test tomorrow, and we need to know this. Firstly, where the curve is you need to go to where the curve finishes and then follow across until you reach the y axis. Let's say, the curve ends at 80. You'd half 80 to give you 40, and go to 40 on the y-axis. You'd do a straight line from 40 across, but stop as soon as you reach the curve. Once you've met the curve, draw a straight line down to the x-axis. This will be your median. To do the lower quartile, the same applies, you just need to half the median, so you'd go to 20 on the y-axis. To find the upper quartile you'd go to 60, as this is 40 and the lower quartile or 20. To fine the inter quartile range you'd do what ever you got for you upper quartile, minus what ever you got for your lower quartile. Hope this helps. (:
Answers:First, arrange the numbers in ascending order, 1, 2, 3, 6, 7, 8, 9, 14, 15, 25, 28 We're lucky since we have 11 numbers. The median (Q2) is the value in the center, which is 8. Now we have 5 numbers on either side of the median. Q1 is the center of the lower group, which is 3. Q3 is the center of the upper group, which is 15.
Answers:ANSWER: See link to Instruction Maunal (Statistics) in Sources below