There are two ways to drop an observation in Stata. First, you can use the logical operator =. However, you have to be careful while using this command. For example, you may not want to tell Stata to drop the case if DHHGAGE > 10.

You may want to specify the value in a more specific way, such as if condition=3.

Second, you can also drop an observation to reduce the dataset size. Dropping an observation is useful when there are duplicates and missing observations. The “drop” command will remove any irrelevant observation or variable from your dataset.

For example, in the CCHS dataset, the variable SLP_01 will be coded with “. a” (NOT APPLICABLE) for every observation.

Third, you can also use a variable to identify a specific observation number. This way, you can choose which row you want to look at. The system variable _n keeps track of this information and can be used as a variable in your analysis.

Similarly, you can use the -c variable to convert a string variable into a numeric one.

Another way to drop an observation is to merge two datasets. First, you need to understand the dataset. You can do this by introducing data science concepts in Stata. This will give you a better understanding of the data and how to clean it.

Contents

- What does the collapse command do in Stata?
- How do you delete a variable?
- How do you set a variable to equal to nothing?
- Can I delete a variable in C++?
- Which function is used to delete a variable?
- What does clear mean Stata?
- What is preserve in Stata?
- What is the difference between quantile and percentile?
- How does quantile regression work?

## What does the collapse command do in Stata?

The “collapse” command in Stata is used to create a new dataset that is a regrouped version of a current dataset. The new dataset will have fewer observations than the current one, but each observation in the new dataset will represent a group of observations from the current dataset.

The new dataset can be created by collapsing across one or more variables. For example, if we have a dataset with variables named “gender” and “age”, we could collapse across both variables to create a new dataset that has one observation for each combination of gender and age.

## How do you delete a variable?

In general, to delete a variable, you need to use the delete keyword followed by the variable name. For example,

let myNum = 5;

delete myNum;

However, this will only work if the variable has been declared using the let or const keywords. If the variable was declared using the var keyword, then you cannot delete it.

## How do you set a variable to equal to nothing?

To set a variable to equal nothing, you can use the null keyword. This will create a variable that doesn’t point to any object and will return false when used in a boolean context.

## Can I delete a variable in C++?

Yes, you can delete a variable in C++. To do so, simply use the delete keyword followed by the variable name. For example:

int* ptr = new int;

delete ptr;

This will delete the variable pointed to by ptr.

## Which function is used to delete a variable?

As variables are automatically deleted when they are no longer needed by the program. However, it is possible to remove all traces of a variable from a program by using the “unset” function. This function will remove the value of a variable and make it unavailable for use by the program.

## What does clear mean Stata?

In Stata, “clear” is a command that is used to free up memory. When you are done using a dataset, it is good practice to clear it from memory so that you do not accidentally use old data. To clear a dataset, type “clear” followed by the name of the dataset.

For example, to clear the dataset “mydata”, you would type “clear mydata”.

## What is preserve in Stata?

The preserve command in Stata is used to keep a copy of the data in memory. This is useful when you want to run a series of commands on the data and then return to the original data.

## What is the difference between quantile and percentile?

A percentile is a value on a scale of one hundred that indicates the percent of a distribution that is equal to or below it. For example, the 20th percentile is the value below which 20% of the values in a distribution fall.

A quantile is a value that divides a distribution into two parts, so that the upper part contains a certain percentage of the values, and the lower part contains the rest of the values. For example, the median is the 50th quantile; it divides the distribution so that 50% of the values are above it and 50% are below it.

## How does quantile regression work?

Quantile regression is a type of regression analysis that estimates the effects of covariates on quantiles of the dependent variable. Unlike linear regression, which estimates the mean of the dependent variable, quantile regression estimates the conditional quantiles of the dependent variable given the values of the covariates.

Quantile regression is a useful tool for analyzing data with non-normal distributions, providing a flexible alternative to traditional linear regression methods.

Quantile regression is based on the idea of quantiles, which are points that divide a distribution into equal parts. The quantiles of a distribution can be estimated using the order statistics of the data.

The first quantile is the data point that divides the distribution into two equal parts, the second quantile divides the distribution into four equal parts, and so on. The pth quantile is the data point that divides the distribution into p equal parts.

The quantiles of a distribution can be estimated using the order statistics of the data. The first quantile is the data point that divides the distribution into two equal parts, the second quantile divides the distribution into four equal parts, and so on.

The pth quantile is the data point that divides the distribution into p equal parts.

Quantile regression estimates the quantiles of the dependent variable given the values of the covariates. Unlike linear regression, which estimates the mean of the dependent variable, quantile regression estimates the conditional quantiles of the dependent variable.

Quantile regression is a useful tool for analyzing data with non-normal distributions, providing a flexible alternative to traditional linear regression methods. Quantile regression can be used to estimate the effects of covariates on the entire distribution of the dependent variable, not just the mean.

Quantile regression is based on the idea of quantiles, which are points that divide a distribution into equal parts. The quantiles of a distribution can be estimated using the order statistics of the data.

The first quantile is the data point that divides the distribution into two equal parts, the second quantile divides the distribution into four equal parts, and so on. The pth quantile is the data point that divides the distribution into p equal parts.

Quantile regression estimates the quantiles of the dependent variable given the values of the covariates. Unlike linear regression, which estimates the mean of the dependent variable, quantile regression estimates the conditional quantiles of the dependent variable.

Quantile regression is a useful tool for analyzing data with non-normal distributions, providing a flexible alternative to traditional linear regression methods. Quantile regression can be used to estimate the effects of covariates on the entire distribution of the dependent variable, not just the mean.

## Leave a comment