Midterm Exam: Qualitative Methods

Regression analysis helps to predict the value of a dependent variable based on one or more independent variables.

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In simple linear regression, the relationship between the dependent and independent variable is assumed to be nonlinear.

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In inferential statistics, hypothesis testing is used to draw conclusions about a population based on sample data.

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In multiple regression, multicollinearity refers to high correlation between dependent variables.

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In regression analysis, the error term accounts for the variability in the dependent variable that is explained by the independent variable.

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The alternative hypothesis in inferential statistics usually posits that there is no effect or no relationship between variables.

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A confidence interval provides a range of values that is likely to contain the population parameter of interest.

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In a simple linear regression, the intercept is the predicted value of the dependent variable when the independent variable is zero.

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The residual in regression analysis is the difference between the observed value and the predicted value.

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Inferential statistics involves drawing conclusions about a sample rather than the population.

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