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This deck focuses on Introducing Statistics Are Variables Related, giving you a quick way to review the definitions, rules, and examples that matter most for AP Statistics.
Study Introducing Statistics Are Variables Related in AP Statistics with focused flashcards that help you recognize the idea, recall the key rule, and apply it in practice-style prompts.
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What is the definition of a dependent variable in statistics?
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A variable affected by changes in the independent variable. It responds to changes in the independent variable.
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This deck focuses on Introducing Statistics Are Variables Related, giving you a quick way to review the definitions, rules, and examples that matter most for AP Statistics.
Work through these flashcards in short sessions. Try to answer each prompt before flipping the card, then revisit any cards you miss until the explanation feels automatic.
Answer: A variable affected by changes in the independent variable. It responds to changes in the independent variable.
Answer: No correlation. Horizontal line shows no relationship between variables.
Answer: χ2=∑Ei(Oi−Ei)2. Compares observed and expected frequencies.
Answer: A variable affected by changes in the independent variable. It responds to changes in the independent variable.
Answer: Residual plot. Patterns in residuals reveal model inadequacies.
Answer: Model may not be appropriate. Patterns suggest violations of regression assumptions.
Answer: Between -1 and -0.7. Values near -1 indicate strong negative association.
Answer: The rate of change of the dependent variable per unit change in the independent variable. Shows how much y changes per unit increase in x.
Answer: Between -1 and -0.7. Values near -1 indicate strong negative association.
Answer: R2=1. Model explains all the variability.
Answer: Positive correlation. Both variables move in the same direction.
Answer: Negative correlation. Variables move in opposite directions.
Answer: The variables are independent. Assumes no association between the variables.
Answer: Between 0.7 and 1. Values near 1 indicate strong positive association.
Answer: R2=0. Model explains none of the variability.
Answer: Weak evidence against the null hypothesis. Less likely to reject null of no correlation.
Answer: r=∑(xi−xˉ)2∑(yi−yˉ)2∑(xi−xˉ)(yi−yˉ). Standardized covariance divided by product of standard deviations.
Answer: No correlation. Points show no discernible linear pattern.
Answer: Perfect positive linear relationship. All points lie on a perfectly upward-sloping line.
Answer: To assess the fit of the regression model. Shows whether model assumptions are satisfied.
Answer: Positive correlation. Both variables move in the same direction.
Answer: Dependent variable. By convention, the response variable goes on y-axis.
Answer: Regression analysis. Uses relationships to make predictions about outcomes.
Answer: Strong evidence against the null hypothesis. More likely to reject null of no correlation.
Answer: Measuring the strength and direction of a monotonic relationship. Works with ranked data instead of raw values.
Answer: Correlation does not imply causation. Association doesn't prove one variable causes another.
Answer: Negative correlation. Variables move in opposite directions.
Answer: Residual plot. Patterns in residuals reveal model inadequacies.
Answer: R2=0. Model explains none of the variability.
Answer: Correlated variables. Variables that show association in their values.
Answer: When independent variables are highly correlated. Creates problems for interpreting individual variable effects.
Answer: Chi-square test. Tests whether categorical variables are related.
Answer: Strong positive correlation. Points cluster tightly around an increasing trend.
Answer: Independent variable. By convention, the explanatory variable goes on x-axis.
Answer: When independent variables are highly correlated. Creates problems for interpreting individual variable effects.
Answer: Regression analysis. Uses relationships to make predictions about outcomes.
Answer: Pearson correlation coefficient. Quantifies linear association strength and direction.
Answer: The expected value of the dependent variable when the independent variable is zero. Starting value when predictor equals zero.
Answer: Correlated variables. Variables that show association in their values.
Answer: Independent variable. By convention, the explanatory variable goes on x-axis.
Answer: A variable that is manipulated to observe its effect on a dependent variable. It's controlled by the researcher to study its effects.
Answer: Between 0.7 and 1. Values near 1 indicate strong positive association.
Answer: The variables are not independent. Claims there is an association between variables.
Answer: Perfect negative linear relationship. All points lie on a perfectly downward-sloping line.
Answer: Strong positive correlation. Points cluster tightly around an increasing trend.
Answer: The rate of change of the dependent variable per unit change in the independent variable. Shows how much y changes per unit increase in x.
Answer: To assess the fit of the regression model. Shows whether model assumptions are satisfied.
Answer: The variables are independent. Assumes no association between the variables.
Answer: Spurious correlation. False correlation caused by confounding variable.
Answer: Scatter plot. Shows the association pattern between two quantitative variables.
Answer: Scatter plot. Shows the association pattern between two quantitative variables.
Answer: Perfect positive linear relationship. All points lie on a perfectly upward-sloping line.
Answer: A variable that is manipulated to observe its effect on a dependent variable. It's controlled by the researcher to study its effects.
Answer: No correlation. Points show no discernible linear pattern.
Answer: r=∑(xi−xˉ)2∑(yi−yˉ)2∑(xi−xˉ)(yi−yˉ). Standardized covariance divided by product of standard deviations.
Answer: Strong evidence against the null hypothesis. More likely to reject null of no correlation.
Answer: Dependent variable. By convention, the response variable goes on y-axis.
Answer: A relationship where one variable directly affects another. Changes in one variable cause changes in another.
Answer: χ2=∑Ei(Oi−Ei)2. Compares observed and expected frequencies.
Answer: Weak evidence against the null hypothesis. Less likely to reject null of no correlation.
Answer: Model may not be appropriate. Patterns suggest violations of regression assumptions.
Answer: The variables are not independent. Claims there is an association between variables.
Answer: Correlation does not imply causation. Association doesn't prove one variable causes another.
Answer: R2=1. Model explains all the variability.
Answer: Spurious correlation. False correlation caused by confounding variable.
Answer: A relationship where one variable directly affects another. Changes in one variable cause changes in another.
Answer: Chi-square test. Tests whether categorical variables are related.
Answer: The expected value of the dependent variable when the independent variable is zero. Starting value when predictor equals zero.
Answer: No linear relationship. Variables have no linear association.
Answer: It measures the proportion of variance in the dependent variable explained by the model. Higher R2 means better model fit.
Answer: No correlation. Horizontal line shows no relationship between variables.
Answer: Perfect negative linear relationship. All points lie on a perfectly downward-sloping line.
Answer: Measuring the strength and direction of a monotonic relationship. Works with ranked data instead of raw values.
Answer: It measures the proportion of variance in the dependent variable explained by the model. Higher R2 means better model fit.
Answer: The difference between observed and predicted values. Measures prediction error for each data point.
Answer: Pearson correlation coefficient. Quantifies linear association strength and direction.
Answer: The difference between observed and predicted values. Measures prediction error for each data point.