Python + ChatGPT 3.5 for A-Z Statistical Data Analysis

Learn statistical data analysis and hypothesis testing, distinguishing population from sample, drawing inferences, and evaluating methods with null and alternative hypotheses.

Understand confidence level, significance level, and p-value as core measures in hypothesis testing, and learn how these thresholds guide decisions about null and alternative hypotheses.

Outline a step-by-step hypothesis testing workflow, from defining null and alternative hypotheses and the 0.05 significance level to normality checks, one-way ANOVA, and regression or correlation on exam scores.

Load your Excel data into a Jupyter notebook using pandas, install and import pandas as pd, then read_excel to create a data frame and view its first rows.

Identify and impute missing values in a data frame using Python, pandas, and ChatGPT, applying a simple imputer with median for numeric data and most frequent for categorical data.

Identify and rectify inconsistent data types in a Python data frame by checking dtypes, locating non-numeric values in numeric columns like cost, and converting to float after removing errors.

Remove rows containing inconsistent value from the numeric column using a mask and tilde in pandas. Then apply the astype method to convert cost to float64 and date to datetime64.

Identify and remove duplicate values in a data set using Python and ChatGPT, employing df.duplicated to detect duplicates and drop_duplicates to produce a data frame without duplicates.

Sort a dataset by numeric values in ascending or descending order using Python and pandas, with ChatGPT-generated code and examples like age, cost, and product categories.

Learn to filter data sets using Python by applying conditional filters for country and product category, including and, or, not operations, with ChatGPT guidance, using France and games as examples.

Load the extra data containing the refund variable, then merge it with the cleaned data on the common order_id to add the refund column to the dataset.

Learn to concatenate datasets by stacking rows to add extra data to a merged dataframe, using pandas pd.concat and an extra_data file, producing a final_df with 1007 rows.

learn to check whether three numeric variables—order value, costs of goods sold, and refund amount—follow a normal distribution, and apply transformations to normalize them using Python and Seaborn.

Apply the square root transformation to numeric variables with numpy in Python to improve normality, testing order value, cost, and refund with CDF plots, noting refund may not normalize.

Apply logarithmic transformations to three numeric variables to improve normality, compare with square root transformation, and preview the upcoming Box-cox method.

Apply the box-cox transformation to three numeric variables (order, value, cost, refund) in Python using SciPy’s stats, compare with square root and log transforms, and assess improvements toward normality.

Apply the Yeo-Johnson transformation to three numeric variables in Python, compare with Box-Cox, and decide transformations for order value euro, cost, and refund, preparing for ANOVA, correlation, and regression.

Learn how to perform frequency and percentage analysis on categorical data to reveal product category distributions, visualize with interactive bar and pie charts using Python and Plotly.

Learn descriptive analysis of numeric data by computing mean, median, standard deviation, min, max, and 25th/75th percentiles, and compare frequency and percentage, with interactive histograms and box plots in Python.

Learn to perform a one sample t test in inferential data analysis and hypothesis testing, comparing the current average order value euro to the €100,000 threshold using p-values.

Compare the average refunds of two groups, France and Sweden, using an independent sample t-test in Python to assess significance with t statistics and p values.

Apply one-way ANOVA to test whether the cost of goods sold differs across PC, mobile, and tablet devices, using a 0.05 significance level; conclude no significant difference.

Explore chi-square test for independence on device type and product category using cross tabulation, interpret the p-value for significance, and visualize the relationship with a clustered bar chart in Python.

Compute and interpret the Pearson correlation among order value, cost, and refund, test significance at 0.05 with box-cox transformed data using python code to confirm statistically significant relationships.

Use regression analysis to measure how order value and cost influence refunds, with significance tested at 0.05. Order value shows a significant positive impact, while cost does not.

Learn to diagnose and debug Python errors by using ChatGPT to identify issues, reshape data for imputation, and generate interactive visualizations of age by education, with corrected, readable code.