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In most business research, the estimate of in a regression equation is most important.

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False

The null hypothesis for an ANOVA test comparing the means of three groups is: _{1 =} _{2 =} _{3.}

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True

In a regression equation, the slope of the line, , is the change in X that is due to a corresponding change of one unit of Y.

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False

In multiple regression, the dependent variable must be continuous and interval-scaled.

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When the difference between two groups is measured, bivariate statistics are used.

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If the purpose of the regression analysis is forecasting, then standardized regression estimates are most appropriate.

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Choosing the right statistic boils down to what type and how many variables are involved in a particular research question or hypothesis.

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A Z-test for differences of proportions requires a sample size greater than 100.

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The chi-square test requires that the expected frequency in each cell of the contingency table be at least 30.

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Conceptually, ANOVA partitions the total variability into three types: total variation, between-group variation, and within-group variation.

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The chi-square test tests the significance of the relationship shown in an R X C contingency table in which R stands for row and C stands for column.

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The independent samples t-test tests the differences between means from no more than four independent samples or groups.

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A simple before-and-after test calls for a paired-samples t-test so long as the dependent variable is not continuous.

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Multivariate statistical analysis permits the researcher to consider more than one dependent variable at the same time.

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One way to test the significance of the relationship shown in a contingency tables is by means of the chi-square test.

True False

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Once a variable has more than four categories, a cross-tabulation table has a stronger advantage in its simplicity in communicating the result.

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The chi-square test involves comparison of the observed frequencies of the groups with the expected frequencies of the groups.

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The degrees of freedom are calculated as df = n 1 when using the t-test for comparing two means, where n = n_{1} + n_{2}.

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If a researcher is interested in whether adult males purchase a product more frequently than adult females, univariate statistics would be used in the analysis of data.

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