This analysis examines the group differences in post-test anxiety using MANOVA, ANCOVA, and MANCOVA on the Datarium dataset. Results show a significant group effect on t2 and t3, with grp3 consistently having the lowest anxiety. After controlling for pretest scores (t1), the effects become stronger, highlighting the importance of the covariate. Overall, group membership significantly influences anxiety outcomes.

This analysis applies 5 clustering methods, which are K-Means, K-Median, DBSCAN, Mean Shift, and Fuzzy C-Means, to the UCI Red Wine Quality dataset to explore natural groupings based on physicochemical properties. After preprocessing, validation, and clustering, the findings suggest that wine quality forms a continuous spectrum rather than distinct clusters, with alcohol, volatile acidity, and sulphates emerging as the key influencing features.

Financial markets generate complex multidimensional data from various sources, such as stock prices, technical indicators, and investor sentiment. The large number of intercorrelated variables often causes multicollinearity problems and increases the complexity of analysis. Therefore, dimension reduction techniques are needed to simplify the data structure without losing important information. This study aims to apply Principal Component Analysis (PCA) and Factor Analysis (FA) to a Multi-Channel Stock Market Dataset consisting of 981 observations and 19 numerical variables. Before the analysis was conducted, the data was tested using Kaiser-Meyer-Olkin (KMO) and Bartlett's Test of Sphericity to ensure the feasibility of factor analysis. The test results showed a KMO value of 0.71 and a Bartlett's Test significance value of < 0.001, indicating that the data was feasible for analysis using PCA and FA. The results of PCA and Factor Analysis (FA) showed that the three main components had eigenvalues greater than 1 and were able to explain 85.68% of the total data variance. The first factor represents the Price Trend Factor, which consists of price and moving average variables. The second factor represents the Technical Momentum Factor, which consists of RSI, MACD, and Signal indicators. The third factor represents the Market Activity and Sentiment Factor, which consists of Volume and Sentiment Compound.

This analysis explores relationships among Age, SibSp, Parch, and Fare in the Titanic dataset using correlation matrix, variance-covariance matrix, eigenvalues, and eigenvectors to understand multivariate patterns.