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How would you deal with missing data and outliers?
One method is to remove outliers as a means of trimming the data set. Another method involves replacing the values of outliers or reducing the influence of outliers through outlier weight adjustments. The third method is used to estimate the values of outliers using robust techniques.
How do you extrapolate missing data?
Interpolation is a mathematical method that adjusts a function to your data and uses this function to extrapolate the missing data. The most simple type of interpolation is the linear interpolation, that makes a mean between the values before the missing data and the value after.
How do you interpolate missing values in data?
Linear Interpolation simply means to estimate a missing value by connecting dots in a straight line in increasing order. In short, It estimates the unknown value in the same increasing order from previous values. The default method used by Interpolation is Linear so while applying it we did not need to specify it.
How to deal with missing data in real statistics?
In Identifying Outliers and Missing Data we show how to identify missing data using a supplemental data analysis tool provided in the Real Statistics Resource Pack. A simple approach for dealing with missing data is to throw out all the data for any sample missing one or more data elements.
How to predict the value of missing data?
In this approach regression (as described in Regression and Multiple Regression) is used to predict the value of the missing data element based on the relationship between that variable and other variables.
Which is the best method for imputation of missing data?
Befo r e jumping to the methods of data imputation, we have to understand the reason why data goes missing. Missing at Random (MAR): Missing at random means that the propensity for a data point to be missing is not related to the missing data, but it is related to some of the observed data
Are there different ways to compensate for missing data?
The chained equations approach is also very flexible and can handle different variables of different data types (ie., continuous or binary) as well as complexities such as bounds or survey skip patterns. For more information on the algorithm mechanics, you can refer to the R esearch Paper