100 ChatGPT Prompts for - Data analysis prompts

The ChatGPT output for the first prompt in the first subject would look like this-
Dealing with missing data is a crucial step in data preprocessing to ensure the quality and reliability of your analysis. Here are several techniques for handling missing data:

Deletion Methods:


Listwise deletion: Also known as complete-case analysis, this method involves removing entire rows with missing values. While straightforward, it can lead to a reduction in sample size and potential bias if the missing data is not random.
Pairwise deletion: In this approach, missing values are ignored for each pairwise analysis. It allows for the maximum utilization of available data but can lead to different sample sizes for different analyses.
Column (variable) deletion: If a variable has a high proportion of missing values and is not critical for analysis, it may be reasonable to delete the entire variable. However, this should be done cautiously as it can result in loss of potentially valuable information.


Imputation Methods:


Mean/Median imputation: Replace missing values with the mean or median of the observed values for that variable. This method is simple and can preserve the overall distribution of the data, but it may underestimate variability and distort relationships if missingness is not random.
Mode imputation: Replace missing categorical values with the mode (most frequent value) of the observed values for that variable.
Regression imputation: Predict missing values based on other variables using regression models. This method can capture relationships between variables but may introduce bias if the relationships are not correctly specified.
K-nearest neighbors (KNN) imputation: Replace missing values with the values of the nearest neighbors in the feature space. KNN imputation can preserve the underlying structure of the data but may be computationally intensive and sensitive to the choice of k.
Multiple imputation: Generate multiple imputed datasets, where missing values are replaced with plausible values multiple times based on the observed data's uncertainty. Analyze each imputed dataset separately and then combine the results using specific rules. Multiple imputation can provide unbiased estimates and valid standard errors under certain assumptions.


Data Substitution Methods:


Constant substitution: Replace missing values with a predetermined constant, such as zero or a placeholder value. While simple, this method may introduce bias and distort relationships.
Last observation carried forward (LOCF): Use the last observed value to fill in missing values. This method is commonly used in time-series data but may not be appropriate for all datasets, especially if the missingness is not related to time.

It's essential to carefully consider the characteristics of your data and the reasons for missingness when choosing an appropriate method for handling missing data. Multiple imputation methods are generally preferred when missing data are non-negligible, and the missingness mechanism is not completely at random, as they can provide more robust and reliable results compared to simpler techniques like deletion or mean imputation. Additionally, sensitivity analysis can be performed to assess the robustness of results to different missing data handling strategies.
Sources:

Handling Missing Data: A Practical Guide
Imputation Methods for Missing Data: A Review
Multiple Imputation for Missing Data: A Cautionary Tale