What is the difference between “listwise deletion” and “pairwise

1.	What is the difference between “listwise deletion” and “pairwise deletion”?
Answer» When a PARTICULAR VARIABLE is missing in an observation or row, then we delete an entire row. This is called List wise deletion. When the analysis is performed with all CASES of a variable and then only those variable instances are deleted and not the entire row. This is called PAIRWISE deletion. This works like a CORRELATION matrix. Generally, pairwise deletion is preferred over listwise deletion as listwise deletion removes the entire row for a particular missing variable.

What is the difference between “listwise deletion” and “pairwise deletion”?

Answer»

When a PARTICULAR VARIABLE is missing in an observation or row, then we delete an entire row. This is called List wise deletion.

When the analysis is performed with all CASES of a variable and then only those variable instances are deleted and not the entire row. This is called PAIRWISE deletion. This works like a CORRELATION matrix.

Generally, pairwise deletion is preferred over listwise deletion as listwise deletion removes the entire row for a particular missing variable.

Discussion

No Comment Found

Related InterviewSolutions

We have below data with 10 transactions. What is the “Lift Ratio” for “if white then blue”?
Transactions#FaceplateColorsPurchased1redwhitegreen2whiteorange3whiteblue4redwhiteorange5redblue6whiteblue7whiteorange8redwhitebluegreen9redwhiteblue10yellow
We have below data with 10 transactions. What is the performance measure “Confidence” for “if white then blue”?
Transaction#FaceplateColorsPurchased1redwhitegreen2whiteorange3whiteblue4redwhiteorange5redblue6whiteblue7whiteorange8redwhitebluegreen9redwhiteblue10yellow
We have below data with 10 transactions. What is the performance measure “Support” for “if white then blue”?
UnivSATTop10AcceptSFRatioExpensesGradRateBrown131089221322,70494CalTech141510025663,57581CMU12606259925,02672Columbia131076241231,51088Comell128083331321,86490Dartmouth134089231032,16295Duke131590301231,58595Georgetown125574241220,12692Harvard140091141139,52597JohnHopkins13057544758,69187MIT138094301034,87091Northwestern126085391128,05289NotreDame125581421315,12294PennState108138541810,18580Priceton13759114830,22095Purdue10052890199,06669Stanford136090201236,45093TexasA&M10754967258,70467UCBerkeley124095401715,14078UChicago129075501338,38087UMichigan118065681615,47085UPenn128580361127,55390UVA122577441413,34992UWisconsin108540691511,85771Yale137595191143,51496
SATAverageSAT score of new freshmenTop10% new freshmen in top 10% of highschool classAccept% of applicants accepted SFRatioStudent to faculty ratioSExpensesEstimated annual expensesGradRateGraduation Rate(%)
Consider universities dataset below. Data for 25 undergraduate programs at business schools in US universities in 1995. The dataset excludes image variables (student satisfaction, employer satisfaction, dean’s opinions, etc.). Given this
We have trained/executed our model with the given dataset. We have noticed that we have used a regression model and it is suffering from multicollinearity. Is it possible to improvise on our model without losing any information?
When is Ridge regression used and when is Lasso regression (ideally)?

What is the difference between “listwise deletion” and “pairwise deletion”?

Discussion

No Comment Found

Related InterviewSolutions

Reply to Comment