{"id":23979331,"url":"https://github.com/nagar2nd/apple_store_reviews---statistics-","last_synced_at":"2026-05-05T09:31:03.446Z","repository":{"id":270943743,"uuid":"911925189","full_name":"Nagar2nd/Apple_store_Reviews---Statistics-","owner":"Nagar2nd","description":"Analyzed Apple Store reviews using statistical methods to explore user ratings, likes, and satisfaction trends. 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Central Tendency Analysis**\n- **Objective:** Calculate the mean, median, and mode of app ratings and determine the best measure of central tendency.\n- **Results:**\n  - **Mean:** 2.869\n  - **Median:** 3.0\n  - **Mode:** 1.0\n  - **Conclusion:** The **median** best represents the central tendency, as it is unaffected by outliers and provides a reliable measure for the dataset.\n\n### **2. Spread of Data**\n- **Objective:** Calculate the range and interquartile range (IQR) of the purchase amounts to understand the spread of the data.\n- **Results:**\n  - **Range:** 19.97\n  - **IQR:** 10.19\n  - **Conclusion:** The **IQR** highlights the spread of the middle 50% of purchase amounts, showing moderate variability in typical spending.\n\n### **3. Variability Analysis**\n- **Objective:** Calculate the variance and standard deviation for the number of likes on reviews.\n- **Results:**\n  - **Standard Deviation:** 28.69\n  - **Variance:** 822.85\n  - **Conclusion:** The moderate standard deviation indicates variability in user engagement, with some reviews receiving significantly more likes than others.\n\n### **4. Correlation Analysis**\n- **Objective:** Determine the correlation between the number of likes and app ratings.\n- **Results:**\n  - **Correlation Coefficient:** 0.8425\n  - **Conclusion:** A strong positive correlation exists, suggesting that higher ratings are associated with more likes, indicating user preference for highly rated content.\n\n### **5. Distribution Analysis**\n- **Objective:** Plot the distribution of app ratings and analyze the skewness.\n- **Results:**\n  - **Skewness:** 0.102\n  - **Conclusion:** The distribution is approximately symmetrical with a slight positive skew, indicating balanced user satisfaction with a tendency for higher ratings.\n\n### **6. Hypothesis Testing**\n- **Objective:** Test if Instagram's average rating is significantly higher than WhatsApp's at a 95% confidence level.\n- **Results:**\n  - **T-Statistic:** -0.797\n  - **P-Value:** 0.787\n  - **Conclusion:** There is no statistically significant difference between the average ratings of Instagram and WhatsApp.\n\n### **7. Central Limit Theorem (CLT)**\n- **Objective:** Take random samples of ratings, calculate their means, and create a sampling distribution to demonstrate the CLT.\n- **Results:**\n  - **Population Mean:** 2.869\n  - **Sampling Mean:** 2.878\n  - **Standard Error:** 0.268\n  - **Conclusion:** The sampling distribution is normal, supporting the CLT. This allows reliable inference about the population mean from sample means.\n\n---\n\n## **How to Use This Repository**\n\n1. **Dataset:** The dataset used for this analysis is included in the `Apple_Store_Reviews.csv` folder.\n2. **Notebook:** The Python analysis notebook (`Apple_store_reviews_analysis.ipynb`) contains the complete code, visualizations, and statistical computations.\n3. **Requirements:**\n   - Python \n   - Libraries: `pandas`, `numpy`, `scipy`, `matplotlib`, `seaborn`\n\n---\n\n## **Connect**\nFeel free to reach out with any questions or feedback:\n- **Email:** shivaninagarofficial@gmail.com\n- **LinkedIn:** https://www.linkedin.com/in/shivani-nagar12/\n---\n\nThank you for exploring this analysis!\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnagar2nd%2Fapple_store_reviews---statistics-","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fnagar2nd%2Fapple_store_reviews---statistics-","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnagar2nd%2Fapple_store_reviews---statistics-/lists"}