Advanced Data Analysis & Statistics GPT-advanced data analysis & statistics tool

Example

Suppose a user uploads a CSV file containing survey data with missing entries, duplicated rows, and various formatting inconsistencies. Advanced Data Analysis & Statistics GPT can automatically detect these issues and suggest appropriate solutions, such as mean imputation for missing numeric values, removal or correction of duplicates, and uniform formatting for categorical variables.

Scenario

A researcher is working with a dataset that has inconsistencies like outliers and missing data. The GPT helps identify the problematic entries, suggests strategies such as Z-score normalization to handle outliers, and provides recommendations on which imputation method is most suitable (e.g., KNN imputation for complex missing data patterns).

Example

When analyzing the relationship between two variables, the GPT can suggest the most suitable test based on the data characteristics, such as a t-test for comparing means between two groups, or a chi-square test for categorical data. It also checks assumptions, like normality or homogeneity of variance, and provides diagnostics to ensure the analysis is valid.

Scenario

A user wants to compare the effectiveness of two different teaching methods on student performance. After verifying normality using the Shapiro-Wilk test, the GPT recommends an independent t-test to compare the means of the two groups and generates a report with p-values, confidence intervals, and effect sizes.

Example

The GPT can perform complex statistical modeling, such as running a logistic regression to predict the probability of an outcome based on several predictors. It can also guide users through bootstrap simulations or Monte Carlo methods to estimate the distribution of a statistic.

Scenario

A financial analyst wants to model the probability of default on a set of loans using various borrower characteristics. The GPT assists by setting up a logistic regression model, selecting the best predictors through stepwise selection, and validating the model using cross-validation. The analyst can also use the GPT to run bootstrap simulations to estimate confidence intervals for the predicted probabilities.