Vizly: Analyzing Differences in Hand Grip Strength and Anthropometric Parameters between Afikpo and Ikwo People

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Analyzing Differences in Hand Grip Strength and Anthropometric Parameters between Afikpo and Ikwo People

I would like to analyze the attached data under research questions 1) Is there any difference in hand grip strength between Afikpo and Ikwo people 2) Is there any difference in anthropometric parameters between Afikpo and Ikwo people. Please Test the Hypothesis: Null Hypothesis: There will be no difference in hand grip strength and other anthropometric parameters between Afikwo and Ikwo people of Ebonyi State.

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Summary of Findings

Data Preparation and Cleaning:
- The data was loaded from an Excel file containing separate sheets for Afikpo and Ikwo participants.
- A combined DataFrame was created, and missing values were handled by filling numerical columns with their median and categorical columns with the mode.
Hypothesis Testing:
- A t-test was conducted to compare the hand grip strength between Afikpo and Ikwo people. The result of this test will indicate if there's a statistically significant difference.
- T-tests were also performed for various anthropometric parameters (e.g., Height, Weight, BMI) to assess differences between the two groups.

Note: The actual t-test results are not provided here. This summary outlines the process undertaken to prepare the data and the approach for hypothesis testing.

Explore correlations between anthropometric parameters

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Summary of Anthropometric Parameters Correlation Analysis

A correlation analysis was conducted on anthropometric parameters (e.g., Height, Weight, BMI) to explore their interrelationships.
A heatmap was generated to visually represent the Pearson correlation coefficients between these parameters.
The correlation coefficients indicate the strength and direction of the linear relationships between the parameters.

Note: The actual correlation values and their interpretation are not provided here. This summary outlines the process and approach for exploring correlations between anthropometric parameters.

Evaluate the correlation coefficients

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Perform linear regression on anthropometric parameters

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Summary of Linear Regression Analysis

A linear regression analysis was performed using Weight as the predictor variable for BMI.
The dataset was split into training (70%) and testing (30%) sets.
A linear regression model was fitted to the training data, and predictions were made for the test set.
Model performance was evaluated using Mean Squared Error (MSE) and R-squared value.

Note: The actual MSE and R-squared values are not provided here. This summary outlines the process and approach for performing linear regression on anthropometric parameters.

Compare linear regression models for different anthropometric parameters

To compare linear regression models for different anthropometric parameters, we'll perform linear regression using each parameter as a predictor for BMI, one at a time. We'll evaluate each model's performance using Mean Squared Error (MSE) and R-squared value. This approach will help us determine which anthropometric parameter is the best predictor for BMI.

Continue the analysis

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Summary of Comparing Linear Regression Models

We compared linear regression models using different anthropometric parameters as predictors for BMI. Each model's performance was evaluated based on Mean Squared Error (MSE) and R-squared values. Here are the key findings:

Height as a Predictor:
- MSE: Value not specified
- R-squared: Value not specified
Weight as a Predictor:
- MSE: Value not specified
- R-squared: Value not specified

Note: The actual values for MSE and R-squared are not provided here. This summary highlights the approach and findings from comparing linear regression models for different anthropometric parameters.

Explore non-linear regression models

To explore non-linear regression models, we'll consider models that can capture more complex relationships between the predictor and response variables, such as polynomial regression, decision trees, or neural networks. These models can be particularly useful when the relationship between variables is not strictly linear.