The data contains haematological parameters of rats treated with Sesamum indicum leaves extract for 21 days. It includes various measurements like Hb, WBC, RBC, and more. To analyze the distribution of practical results, we'll visualize the data for each parameter.

Let's proceed by cleaning the data to remove any NaN values and unnecessary rows, then visualize the distribution for each parameter.

Next, we'll rename the columns for clarity and prepare the data for visualization. This involves assigning appropriate column names based on the data previewed.

We've successfully loaded and cleaned the dataset from Practical No. 9.xlsx, specifically from Sheet1, which contains haematological parameters of rats treated with Sesamum indicum leaves extract for 21 days. The steps taken include:

Next, we'll proceed to analyze the distribution of practical results for each parameter.

To continue the analysis, we will visualize the distribution of each haematological parameter across the different treatment groups. This will help us understand how the treatment with Sesamum indicum leaves extract affects these parameters in rats.

We'll use histograms and boxplots for this purpose, as they are effective for showing distributions and identifying outliers.

We've completed the analysis of the distribution of haematological parameters in rats treated with Sesamum indicum leaves extract for 21 days. The analysis involved:

The visualizations provide insights into the central tendency, dispersion, and outliers for each haematological parameter across different treatment groups.

To compare the distribution of parameters between treatment groups, we'll create separate plots for each parameter, highlighting differences across groups. This involves using grouped boxplots or violin plots for a detailed comparison.

We've analyzed and compared the distribution of haematological parameters across different treatment groups of rats treated with Sesamum indicum leaves extract for 21 days. The key steps in our analysis included:

The grouped boxplots provided insights into how each treatment group affects the haematological parameters, highlighting differences in central tendencies and variabilities among the groups.

To determine if there's a significant difference among the treatment groups for each haematological parameter, we'll perform statistical tests. Specifically, we can use ANOVA (Analysis of Variance) if the data meets the assumptions of normality and homogeneity of variances. Otherwise, the Kruskal-Wallis test, a non-parametric alternative to ANOVA, can be used.

These tests will help us understand if the differences in means (for ANOVA) or medians (for Kruskal-Wallis) among the groups are statistically significant.