We have below data with 10 transactions. What is the performance measu

1.	We have below data with 10 transactions. What is the performance measure “Confidence” for “if white then blue”?
Answer» When we want to find out the statistical significance between two variables, then the chi-square test is used to understand the DEVIATION between observed and expected FREQUENCY and divided by the expected frequency. Probability of 0: It indicates that both categorical variables are dependent Probability of 1: It shows that both variables are independent. Probability less than 0.05: It indicates that the relationship between the variables is SIGNIFICANT at 95% CONFIDENCE. The chi-square test statistic for a test of independence of two categorical variables is found by: Square of X = Sum[Square of (O - E) / E] Where O – Observed frequency E – Expected frequency under the null hypothesis We use this between two Categorical variables.

We have below data with 10 transactions. What is the performance measure “Confidence” for “if white then blue”?

Answer»

When we want to find out the statistical significance between two variables, then the chi-square test is used to understand the DEVIATION between observed and expected FREQUENCY and divided by the expected frequency.

Probability of 0: It indicates that both categorical variables are dependent
Probability of 1: It shows that both variables are independent.
Probability less than 0.05: It indicates that the relationship between the variables is SIGNIFICANT at 95% CONFIDENCE. The chi-square test statistic for a test of independence of two categorical variables is found by:
- Square of X = Sum[Square of (O - E) / E]
- Where O – Observed frequency
- E – Expected frequency under the null hypothesis

We use this between two Categorical variables.

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)?

We have below data with 10 transactions. What is the performance measure “Confidence” for “if white then blue”?

Discussion

No Comment Found

Related InterviewSolutions

Reply to Comment