Quantitative data is numerical. It represents measurements or counts that can be quantified mathematically. For example, age, height, weight, or the number of objects are all quantitative data because they consist of numeric measurements.

T-tests are typically used when the population standard deviation is unknown. The sample size or normality of data isn't the primary deciding factor.

The use of Bayes' Theorem is controversial when the prior probability is subjective or not based on data. Critics argue that this introduces personal bias into the statistical analysis. However, Bayesians argue that all modeling involves subjective choices.

The key difference between one-way and two-way ANOVA lies in the number of independent variables they consider. A one-way ANOVA is used when there is one independent variable, whereas a two-way ANOVA is used when there are two independent variables.

The Durbin-Watson statistic is a test statistic used to detect the presence of autocorrelation (a relationship between values separated from each other by a given time lag) in the residuals (prediction errors) from a regression analysis.

Each coefficient in a multiple linear regression model represents the average change in the dependent variable for a one unit change in the corresponding independent variable, while keeping all other independent variables constant. This is known as ceteris paribus, or "all else being equal."

In simple linear regression, the dependent variable is the outcome we are trying to predict, and the independent variable is the predictor. The dependent variable is also known as the response or target variable, and the independent variable is also known as the explanatory or feature variable.

A binomial distribution is appropriate when conducting an experiment where each trial has exactly two possible outcomes (often termed success and failure), the trials are independent, and the probability of success is constant across trials.

The least squares method is used to find the best-fitting line through the data points. This is done by minimizing the sum of the squares of the vertical distances of the points from the line.

In multiple linear regression, the F-statistic is used to test the overall significance of the model. This test checks the null hypothesis that all regression coefficients are zero against the alternative that at least one of them is not zero. If the F-statistic is significantly large and the corresponding p-value is small, we reject the null hypothesis, concluding that the regression model has some validity in predicting the outcome variable.