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.
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correlation coefficient (r)- The correlation coefficient r ranges from ____.
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negative 1 to positive 1- A correlation coefficient of ____ indicates no linear relationship between two variables.
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0- A correlation coefficient close to ____ indicates a strong positive linear relationship, where both variables increase together.
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positive 1- A correlation coefficient close to ____ indicates a strong negative linear relationship, where one variable increases as the other decreases.
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negative 1- The ____ represents the proportion of variance in the dependent variable explained by the independent variable in a regression model.
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coefficient of determination (r-squared)- Correlation does not imply ____, since an observed association may result from confounding or chance.
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causation- In ____, a single independent variable is used to predict a continuous dependent variable using a straight-line equation.
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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.
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b- In the linear regression equation y = a + bx, the term ____ represents the y-intercept, the predicted value of y when x equals zero.
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a- ____ uses two or more independent variables to predict a single continuous dependent variable.
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Multiple linear regression- ____ is used when the dependent variable is binary (categorical with two outcomes) rather than continuous.
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Logistic regression- In logistic regression, the output is typically expressed as an ____ for each predictor variable.
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odds ratio- ____ are the differences between observed values and the values predicted by a regression model.
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Residuals- The method of ____ finds the regression line that minimizes the sum of the squared residuals.
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least squares
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