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What are the differences between joint relative frequency and marginal relative frequency?
Joint Relative Frequency: Proportion of observations in a specific cell. | Marginal Relative Frequency: Proportion of observations in each category of a single variable.
What are the differences between correlation and causation?
Correlation: Measures the strength and direction of a linear relationship. | Causation: Indicates that one variable directly causes a change in another variable.
What are the differences between slope and y-intercept in a regression line?
Slope: The change in the predicted y-value for every one-unit increase in x. | Y-intercept: The predicted value of y when x is zero.
What are the differences between r and R²?
r: Correlation coefficient, measures the strength and direction of a linear relationship. | R²: Coefficient of determination, represents the proportion of variance in the dependent variable explained by the independent variable.
What is the definition of Bivariate Data?
Data involving two variables analyzed simultaneously to explore their relationship.
What is the definition of Joint Relative Frequency?
The proportion of observations that fall into a specific cell in a two-way table.
What is the definition of Marginal Relative Frequency?
The proportion of observations in each category of a single variable.
What is the definition of Conditional Relative Frequency?
The proportion of observations in a specific category of one variable, given a specific category of the other variable.
What is the definition of Correlation Coefficient (r)?
A measure of the strength and direction of a linear relationship between two quantitative variables, ranging from -1 to 1.
What is the definition of Coefficient of Determination (R²)?
The proportion of variation in the dependent variable that is predictable from the independent variable.
What are residuals?
The difference between the actual and predicted y-values in a regression analysis.
What is the formula for calculating a residual?
Residual = Actual y - Predicted y
What is the formula for the regression equation?
\$\hat{y} = a + bx$, where $\hat{y}$ is the predicted value, a is the y-intercept, b is the slope, and x is the independent variable.
How do you calculate predicted y?
\$\hat{y} = a + bx$