Data Science

Question 1

regression imputation

Answer 1

has the opposite problem of mean imputation. A regression model is estimated to predict observed values of a variable based on other variables, and that model is then used to impute values in cases where the value of that variable is missing.

Question 2

mean imputation

Answer 2

is a method in which the missing value on a certain variable is replaced by the mean of the available cases. This method maintains the sample size and is easy to use, but the variability in the data is reduced, so the standard deviations and the variance estimates tend to be underestimated.

Question 3

Imputation

Answer 3

the assignment of a value to something by inference from the value of the products or processes to which it contributes.Exp using the MEAN to find each value for each column

Question 4

Extension To Imputation

Answer 4

In this approach, we impute the missing values, as before. And, additionally, for each column with missing entries in the original dataset, we add a new column that shows the location of the imputed entries.

Question 5

Multiple Imputation

Answer 5

is essentially an iterative form of stochastic imputation. However, instead of filling in a single value, the distribution of the observed data is used to estimate multiple values that reflect the uncertainty around the true value.