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When dealing with missing data, data scientists can use two primary methods to solve the error: imputation or the removal of data. The imputation method develops reasonable guesses for missing data. It's most useful when the percentage of missing data is low.
If (a + b + c) = 9, find (a – 4)3 + (b – 2)3 + ( c – 3)3 – 3(a – 4 ) (b – 2 ) (c &n...
If x = 222, y = 223 and z = 224, then find the value of x³ + y³ + z³ – 3xyz
If x:y:z = 3:4:6, and (5x + 3z) = 132, then find the value of '3y'.
If x = 99, then the value of x5 - 100x4 + 100x3 - 100x2 + 100x - 1 is
If 10x2 – 6xy+y² – 4x+4= 0, then find the value of (3x+2y).
If {x + (1/x)} = 7, then find the value of {x2 - (1/x)2}.
If a2+2 = 2a
then find a4 - a3 + 2a2 -2a+ 9 = ?
In an orchard, the ratio of the number of mango and lichi trees is 9:10, respectively. Also, there are few apple trees in the orchard which is 50% more ...
If (p/q) = (7/8) and (q/r) = (3/11), then the find the value of {(r – p)/(p + q)}.
