# How missing values are represented in Stata?

## How missing values are represented in Stata?

Stata represents a missing value as a very large number and displays it as a dot (“.”). You can use the dot in logical expression but you should use var <= . ( not var == .) to make sure that the comparison is always correct. Better use the missing(varname) function instead.

## How does Stata treat missing values in regression?

By default, Stata will handle the missing values using “listwise deletion”, meaning that it will remove any observation which is missing on the outcome variable or on any of the predictor variables. You do not need to do anything for Stata to do this, it does this automatically.

**What does Mvdecode mean in Stata?**

mvdecode changes occurrences of a numlist in the specified varlist to a missing-value code.

**Can I run regression with missing values?**

Linear Regression The variable with missing data is used as the dependent variable. Cases with complete data for the predictor variables are used to generate the regression equation; the equation is then used to predict missing values for incomplete cases.

### How do you drop missing values?

The pandas dropna function

- Syntax: pandas.DataFrame.dropna(axis = 0, how =’any’, thresh = None, subset = None, inplace=False)
- Purpose: To remove the missing values from a DataFrame.
- Parameters: axis:0 or 1 (default: 0).
- Returns: If inplace is set to ‘True’ then None. If it is set to ‘False’, then a DataFrame.

### Can you label missing values in Stata?

Stata allows us to code different types of numeric missing values. It has 27 numeric missing categories. “.

**How do you handle categorical missing values?**

When missing values is from categorical columns such as string or numerical then the missing values can be replaced with the most frequent category. If the number of missing values is very large then it can be replaced with a new category.

**How do you report missing data analysis?**

In their impact report, researchers should report missing data rates by variable, explain the reasons for missing data (to the extent known), and provide a detailed description of how missing data were handled in the analysis, consistent with the original plan.