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Key things to know about correlation as a data scientist ⤵️⤵️⤵️ 📌Correlation This quantifies or measure the strength and direction of a linear relationship between two variables. 📌 Range It ranges from - 1 to 1 ▪️ 1 means perfect positive correlation. ▪️ - 1 means perfect negative correlation. ▪️ 0 means no correlation. 📌 Types ▪️ Pearson's (r) measure linear relationships. ▪️ Spearman's (p) measure monotonic relationships. 📌 Ways to Check ▪️ Visual inspection (scatter plots or heat maps) ▪️ Statistical tests ▪️ Correlation coefficient (Calculate correlation matrix) ▪️ Hypothesis Testing 📌 Use Cases ▪️ Checking potential relationships. ▪️ Feature selection. ▪️ Data exploration. P.S. Context matters; correlation does not always imply causation so don't just jump to conclusions! ☆ᴊsᴘ☆ #datascience #machinelearning #deeplearning #Statistics #dataanalytics #AI #model

📈What is the Friedman test📈 The Friedman test is a non-parametric statistical test used to detect differences among three or more related (paired) groups. OR: It is the non-parametric alternative to repeated-measures ANOVA. It is applied when: The same subjects are measured under different conditions or time points Data do not satisfy normality assumptions It works by ranking the data within each subject and testing whether the rank sums differ significantly across conditions. Null hypothesis (H₀): The distributions (or median ranks) of all groups are equal. Test statistic: The Friedman statistic approximately follows a chi-square (χ²) distribution with (k − 1) degrees of freedom, where k is the number of conditions. 🔴 Academic interpretation of the given figure Figure description: The figure presents boxplots of scores measured at three time points (t1, t2, t3) for the same n = 10 subjects. A Friedman test was applied to evaluate whether the scores differed significantly across time. 🔴 Statistical results Friedman test: χ²(2) = 18.2 p-value: 0.00011 Sample size: n = 10 Since p < 0.05, the null hypothesis is rejected, indicating a statistically significant difference in scores across time points. 🔴 Post-hoc comparisons Pairwise comparisons (typically Wilcoxon signed-rank tests with correction) are indicated by brackets: t1 vs t2: ** (p < 0.01) t1 vs t3: ** (p < 0.01) t2 vs t3: * (p < 0.05) This shows that: Scores increased significantly from t1 to t2 Scores increased significantly from t1 to t3 A further significant increase occurred from t2 to t3 🔴 Data distribution and trend t1: Lowest median score with minimal variability t2: Moderate increase in median score t3: Highest median score with greater dispersion The boxplots and individual data points reveal a consistent upward trend over time, suggesting a progressive effect of time or treatment on the measured variable. Conclusion The Friedman test revealed a significant effect of time on the measured scores (χ²(2) = 18.2, p = 0.00011). Post-hoc analyses indicated significant increases in scores from t1 to t2 and t1 to t3 (p < 0.01), as well as from t2 to t3 (p < 0.05). These results demonstrate a statistically significant and progressive change in scores across the three time points.

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