Quantitative Analysis: Inferential Statistics and Multivariate Analysis

Quiz Content

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. Which of these is a characteristic of a normal distribution?

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. The central limit theorem states that the distribution of the means of random samples taken from a population approaches the normal distribution as _________.

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. The _____________ is the probability that a sample statistic is an accurate reflection of the population parameter, while the _____________ is the range in which the population parameter is likely to fall.

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. The ______ is a parametric test.

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. When examining the relationship between a nominal variable to a normally distributed interval-ratio variable, you would use _____________.

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. Before calculating chi-square, we must construct a ______.

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. For the chi-square, t-test, and F-test, determining the critical value requires calculating the ___________.

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. Multiple regression analysis is used when ______.

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. The following is NOT a major assumption of multiple regression analysis: ______.

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. Logistic regression is used rather than multivariate regression when ____________.

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. Many real world variables are distributed according to the normal distribution.

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. Only statistically significant results are ever substantively significant.

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. Chi-square tests the independence of two variables by assessing the likelihood that the relationship observed in the sample is due to chance.

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. The difference of means test compares rankings, while the Mann-Whitney U-test compares means.

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. One implication of the central-limit theorem is that, as sample size decreases, the distribution of cases more closely approximates a normal curve.

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. If your hypothesis implies that you are interested only in one direction of difference between your samples, then you should use a two-tailed test.

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. A dummy variable typically is characterized by the presence and the absence of a quality.

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. Multicollinearity refers to the existence of a relationship between a study's independent variables, which generally violates the linear regression assumption that independent variables should be unrelated to one another.

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