Summary Statistics:
Summary statistics, such as mean, median, standard deviation, skewness, and kurtosis, are fundamental for understanding the central tendency and spread of your data.
Consider using box plots, histograms, or density plots to visualize the distribution of your data in addition to summary statistics.
Data Modeling Techniques – Linear Regression and Logistic Regression:
Linear regression is excellent for modeling relationships between continuous variables, while logistic regression is used for binary classification problems.
Ensure that you have addressed assumptions such as linearity, independence, homoscedasticity (constant variance), and normally distributed residuals when using linear regression. For logistic regression, focus on interpreting odds ratios and assessing the significance of predictor variables.
Assessment Methods – Cross-Validation:
Cross-validation is crucial to evaluate the generalization performance of your models and avoid overfitting. Techniques like k-fold cross-validation (e.g., 5-fold or 10-fold) can provide robust estimates of model performance.
Consider using techniques like stratified cross-validation for classification tasks to ensure that each class is represented proportionally in each fold.
Assessment Methods – p-values and Confidence Intervals:
P-values are commonly used to assess the statistical significance of coefficients in linear regression. Confidence intervals provide a range of plausible values for a parameter estimate, helping to quantify uncertainty. Be cautious with p-values and multiple testing corrections, such as the Bonferroni correction, to mitigate the risk of false discoveries when conducting multiple hypothesis tests.
Further Considerations:
When working with regression models, assessing goodness-of-fit metrics like R-squared (for linear regression) or deviance or AIC (for logistic regression) can provide insights into how well your models explain the variation in the data. Think about model interpretability. Linear models are often more interpretable than complex machine learning models, which can be crucial for understanding the relationship between predictors and outcomes.