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In statistical analysis, it's common to encounter scenarios where the input variable is categorical and the output variable is quantitative. To analyze such data, several parametric statistical tests are available that can help us understand the relationship between the two variables. In this blog post, we will discuss three such tests - ANOVA, t-Test, and Chi-Square test - and provide examples to demonstrate their usage. ANOVA Test t-Test Chi-Square Test Example Conclusion ANOVA Test: ANOVA (Analysis of Variance) is a statistical test used to compare the means of three or more groups. It's often used in research to determine if there are significant differences between multiple groups based on a categorical variable. In this scenario, the categorical variable defines the groups, while the quantitative variable is the variable of interest being compared across the groups. ANOVA test can be used to determine if the means are significantly different or not. t-Test: The t-test i...

  As a machine learning algorithm, decision trees have been widely used in various industries, from finance to healthcare to marketing. The reason why it's so popular is that decision trees can be easily understood by humans and provide a transparent way to make predictions. Introduction to Decision Trees Decision trees are a type of machine learning algorithm that is used for classification and regression tasks. They are a popular algorithm due to their simplicity and interpretability. Decision trees are represented in a tree-like structure, where each node represents a feature or attribute, and the edges represent the decision rules that lead to a certain outcome. How Decision Trees Work Decision trees work by recursively partitioning the data based on the feature that provides the most information gain. Information gain measures the difference between the impurity of the parent node and the sum of the impurities of the child nodes. The algorithm chooses the feature with the high...