I think you mean ordinal data. Similar to the golf tournament, you need to determine where to "cut" (from the ordinal data) so as to divide the data into different categories (to the nominal data). For example, if the ordinal data range from 1 to 6 (where 1 = the best) and the cut is 3, then you convert all the numbers from 1 to 3 to "1" (which represents "good") and the all numbers from 4 to 6 to "2" (which represents "bad"). In other words, 1, 2, and 3 from the original ordinal data set are converted to "1" (ordinal data); whereas 4, 5, and 6 from the original date set now become "2" (ordinal data). Eddie T.C. Lam
There are many advantages of performing a database normalization. Some of the advantages include faster index searching, data commands are faster with less indexes and a more compact database with less null data.
First arrange the data set in ascending order. Suppose the data set consists of n observations. the index for the lower quartile is (n + 1)/4 and the index for the upper quartile is 3*(n + 1)/4. Find the values that correspond to the number in these positions in the ordered list. For example, if n = 15, then lower index = 4 and upper index = 12. So the lower quartile is the fourth number and the upper quartile is the twelfth. If n is large, you may skip the +1 and just look at n/4 and 3n/4. Often the indices are not integers. Then, if you are a beginner (nd the fact that you asked this question suggests that you are), find the nearest whole numbers for the two indices. Otherwise you need to interpolate and that is a whole new ball game!
becaused it is exact
disadvantages of index numbers
Index is a data structure that improve the performance of data.
There are three types of index data structures: unique, non-unique, bitmap
When a clustered index (also known as data) is made, the SQL server will momentarily duplicate itself and sort the data from the heap into a clustered index key order.
The data is stored along side the index keys, so when a query searches by the index, once it's found the "leaf" in the index tree, it usually has gotten the data in the same disk read. So your searches by that key are much faster. The down-side is that it takes much longer to save data with a clustered index, because it has to make space where the key should be stored for the table data.
Database indexes do not contain data; they are structures that improve the speed of data retrieval.
non cluster index
The details depend on the language, but the index of an array is usually an integer data type. Anything that is compatible with an integer can be used.
#include<iostream> #include<iomanip> #include<time.h> template<typename T> size_t find(T& data, T a[], size_t size) { size_t index=0; do { if(a[index]==data) break; } while(++index<size); return(index); } template<typename T> void print(T a[], size_t size) { using std::cout; using std::endl; using std::setw; size_t index=0; do{ if(index&&index%20==0) cout<<endl; cout<<setw(3)<<a[index]; }while(++index<size); cout<<endl; } int main() { srand((unsigned)time(NULL)); const size_t size=100; unsigned int a[size]; size_t index=0; do{ unsigned int data=rand()%100; do{ data=rand()%100; } while(find(data,a,index)<index); a[index]=data; } while(++index<size); print(a,size); }
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to organize similar data
tally 7.2 data convert into excel
Mean is the average of a set of data points = (Sum of all data) / Quantity of DataMedian is the middle of a set of data points = Data whose index is the integer nearest ((Number of Data+1) / 2)Example Data:2, 3, 5, 3, 2The mean = (2+3+5+3+2)/5 = 15/5 = 3The median = Index((5+1)/2) = Index(6/2) = Index(3). The third item on our list is 5.