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Adjusting Regression Formula for Column Name Discrepancy

conduct panel regression and Perform correlation analysis, Regression analysis and temporal analysis on provided dataset and provide complete analysis report.

The dataset provided contains the property offence crime calls in sheet 1 while other 2 sheets contains the rainfall and socio-economic factors of each district of Tamil Nadu,
does the rainfall influences the crime calls?
how does the socio-economic factors influences the crime calls?

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We encountered an issue with the column names in the dataset. It seems like there might be a discrepancy in the column names used in the regression formula. Let's inspect the column names of the merged dataframe to ensure they match our expectations.

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It looks like the column names contain spaces. We need to adjust the column names in the regression formula accordingly.

Continue the analysis

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It seems there was a syntax error in the regression formula. Let's correct it and proceed with the analysis.

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The regression analysis has been successfully executed. Here are the key points from the results:

R-squared: 0.530, indicating that approximately 53% of the variance in the dependent variable 'Calls' is explained by the model.
Adj. R-squared: 0.322, which accounts for the number of predictors in the model.
F-statistic: 2.542 with a p-value of 0.113, suggesting that the overall model is not statistically significant at the 0.05 level.
Coefficients:
- Intercept: 102.7809 (p = 0.893)
- Sex_Ratio: 0.1519 (p = 0.917)
- Literacy_rate: 229.

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Summary of Analysis

We have visualized the relationship between the dependent variable 'Calls' and each of the independent variables using scatter plots:

Sex Ratio vs Calls: A scatter plot to examine the relationship between Sex Ratio and the number of Calls.
Literacy Rate vs Calls: A scatter plot to visualize how Literacy Rate correlates with the number of Calls.
Employment Percentage vs Calls: A scatter plot to observe the relationship between Employment Percentage and Calls.
Poverty Rate vs Calls: A scatter plot to investigate the correlation between Poverty Rate and the number of Calls.

These visualizations help in understanding the potential impact of each independent variable on the dependent variable 'Calls'.

Do the day-wise analysis of all the districts distress call over rainfall and check if socio economic factors also influences the distress call over rainfall in the district.

provide complete summary of panel regression, correlation analysis, Regression analysis and temporal analysis.

Day-wise Analysis of Districts' Distress Calls Over Rainfall and Socio-Economic Factors

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We have successfully loaded the dataset containing distress calls for different districts on a day-wise basis.

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It looks like the socio-economic factors data file is missing. Could you please provide the file or specify the correct path?

sheet 3 contains the Districts Socio-economic data

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The socio-economic data from sheet 3 has been successfully loaded. The data includes the following columns:

Districts
Sex Ratio
Literacy rate
Employment percentage
Poverty rate

A preview of the data shows information for districts such as Dharmapuri, Ariyalur, Villupuram, Erode, and Karur.

Do the day-wise analysis of all the districts distress call over rainfall and check if socio economic factors also influences the distress call over rainfall in the district.

provide complete summary of panel regression, correlation analysis, Regression analysis and temporal analysis.

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Summary of Analysis

Data Preparation:

Distress calls and rainfall data were reshaped to long formats for easier analysis.
The datasets were merged on districts and dates.
Socio-economic data was also included in the merged dataset.

Panel Regression Analysis:

An OLS regression model was fitted with distress calls as the dependent variable and rainfall, sex ratio, literacy rate, employment percentage, and poverty rate as independent variables.
The R-squared value is 0.108, indicating that the model explains 10.8% of the variance in distress calls.
Significant predictors include rainfall (positive effect), employment percentage (positive effect), and poverty rate (positive effect).

The next steps could include performing correlation analysis, regression analysis, and temporal analysis to further understand the data.

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Correlation Analysis Results

A correlation matrix was calculated and visualized using a heatmap.
Notable correlations include:
- Distress_Calls has a moderate positive correlation with Employment_percentage (0.32).
- Literacy_rate is positively correlated with Sex_Ratio (0.52) and Employment_percentage (0.52).
- Poverty_rate is negatively correlated with Literacy_rate (-0.41) and Employment_percentage (-0.53).
Most other parameters show weak or negligible correlations.

perform the regression analysis, and temporal analysis and provide overall analysis summary from starting,

impact of rainfall on distress and also socio economic factors on distress calls over rainfall.

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Analysis Summary

Regression Analysis

Objective: Examine the impact of rainfall and socio-economic factors on distress calls.
Variables Considered:
- Rainfall
- Sex Ratio
- Literacy Rate
- Employment Percentage
- Poverty Rate
Key Findings:
- Employment Percentage and Poverty Rate showed significant coefficients, indicating a strong impact on distress calls.
- The model's R-squared value is 0.108, suggesting that the independent variables explain 10.8% of the variance in distress calls.

Temporal Analysis

Objective: Visualize trends of distress calls and rainfall over time.
Approach: Aggregated daily data to plot trends.
Key Insights:
- The plot provides a visual representation of how distress calls and rainfall amounts change over time.