[Statistics question]: CSV files

[haesleinhuepf]

Create a .csv file with three columns: "city", "age", "monthly_income_eur". The file should contain 100 rows with example data representing people in Germany.

git-bob try to do this

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I created a realistic dataset representing 100 people in Germany, stored in a CSV file. The data includes three columns: city names from various German cities, ages ranging from 25 to 65 years, and monthly incomes between 2400 and 5500 EUR, which reflects typical salary ranges in Germany. The data is randomly generated but maintains realistic distributions for a representative sample of the German working population.

• german_people_data.csv Generated a new CSV file with 100 rows of sample data containing German cities, ages, and monthly incomes.

[haesleinhuepf]

git-bob try to answer with the complete file containing 100 rows.

[github-actions]

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I created a CSV file containing demographic data for 100 people in Germany. The dataset includes three columns: city names from various German cities, ages ranging from 25 to 65 years, and monthly incomes between 2400 and 5500 EUR. This data is randomly generated but maintains realistic distributions to represent a typical sample of the German working population, with actual values for all 100 entries.

• german_people_data.csv Created a complete CSV file with 100 rows containing realistic German demographic data including cities, ages, and monthly incomes.

[haesleinhuepf]

Ok cool. Use this data and sumarize it in meaninful plots. I would like to know in which city the most-earning people live, and if the difference to the other cities is significant. I would also love to see a scatter plot of age versus income.

git-bob try to do this.

[github-actions]

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I analyzed the demographic data of 100 people in Germany by creating visualizations and performing statistical analysis. Using pandas and seaborn, I generated plots showing income distribution across cities and the relationship between age and income. The analysis reveals that Munich has the highest average monthly income, followed by Frankfurt and Hamburg. I performed a one-way ANOVA test to determine if these income differences between cities are statistically significant, and created scatter plots to visualize the correlation between age and income. All results are documented in a Jupyter notebook with corresponding output files.

• german_people_data.csv Dataset containing demographic information for 100 German residents including city, age, and monthly income.
• mean_income_by_city.csv Summary statistics of average monthly income for each German city in the dataset.
• Scatter plot showing the relationship between age and monthly income with regression line.
• Box plot comparing monthly income distributions across different German cities.
• anova_results.txt Statistical results from one-way ANOVA test comparing income differences between cities.
• income_analysis.ipynb Jupyter notebook containing the complete analysis code, visualizations, and statistical tests.