ANOVA PROJECT

In the ANOVA project, I undertook a comprehensive data cleansing and analysis task to evaluate various product attributes. The project involved cleaning raw data, performing statistical analyses, and presenting the results in a structured manner. The data set included multiple product categories with varying attributes, such as firmness, stickiness, and gloss, across different product lots and types.

The process began with data cleansing, where I addressed issues related to data inconsistencies and formatting. This step was crucial for ensuring the accuracy and reliability of the analysis. I consolidated and organized the data into a structured table, which included product attributes like firmness, stickiness, cohesiveness, and others, for different product lots and types. Each attribute was analyzed for its statistical significance using ANOVA, with results indicating whether there were significant differences between the various products.

For example, the attribute "Firmness" showed a high level of statistical significance with a p-value of <0.0001, indicating that differences in firmness between the products were statistically significant. Similarly, attributes like "Stickiness," "Cohesiveness," and "Gloss in Dish" also showed significant variations, with p-values indicating strong evidence of differences between product types.

The results were then compared using post-hoc tests to determine which specific groups differed from each other. This was essential for identifying which products performed better or worse in terms of the attributes measured. For instance, "Lather Type: Oily" and "Lather Type: Waxy" showed significant differences, which were highlighted in the results.

The final output included a detailed table summarizing the attributes, their means, statistical significance, and post-hoc test results. This table was instrumental in drawing conclusions about the performance of different products and understanding the factors that influenced their quality.

Overall, the project involved extensive data cleansing, rigorous statistical analysis, and detailed presentation of results. The insights gained from the ANOVA analysis provided valuable information for evaluating product performance and guiding decisions related to product improvements and development.