Biostatistics · Biostatistics Fundamentals

Biostatistics: Correlation, Regression, and ANOVA

Correlation coefficients, linear regression, and analysis of variance concepts as cloze cards with each term and formula defined.

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The ____ measures the strength and direction of a linear relationship between two continuous variables.

correlation coefficient (r)

The correlation coefficient r ranges from ____.

negative 1 to positive 1

A correlation coefficient of ____ indicates no linear relationship between two variables.

0

A correlation coefficient close to ____ indicates a strong positive linear relationship, where both variables increase together.

positive 1

A correlation coefficient close to ____ indicates a strong negative linear relationship, where one variable increases as the other decreases.

negative 1

The ____ represents the proportion of variance in the dependent variable explained by the independent variable in a regression model.

coefficient of determination (r-squared)

Correlation does not imply ____, since an observed association may result from confounding or chance.

causation

In ____, a single independent variable is used to predict a continuous dependent variable using a straight-line equation.

simple linear regression

In the linear regression equation y = a + bx, the term ____ represents the slope, or the change in y for a one-unit change in x.

b

In the linear regression equation y = a + bx, the term ____ represents the y-intercept, the predicted value of y when x equals zero.

a

____ uses two or more independent variables to predict a single continuous dependent variable.

Multiple linear regression

____ is used when the dependent variable is binary (categorical with two outcomes) rather than continuous.

Logistic regression

In logistic regression, the output is typically expressed as an ____ for each predictor variable.

odds ratio

____ are the differences between observed values and the values predicted by a regression model.

Residuals

The method of ____ finds the regression line that minimizes the sum of the squared residuals.

least squares

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