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Business Development

Q: Which of the following is an example of an interdependence analysis method? A.multidimensional scaling B.multiple regression analysis C.conjoint analysis D.all of these choices
Q: All of the following are examples of dependence methods of analysis EXCEPT _____. A.multiple regression analysis B.multiple discriminant analysis C.cluster analysis D.multivariate analysis of variance
Q: Which of the following is a dependence method of analysis? A.structural equations modeling B.multiple regression analysis C.multiple discriminant analysis D.all of these choices
Q: When a multivariate statistical technique is used to predict job satisfaction from several independent variables, such as age, salary, and number of years in that position, the researcher is studying _____. A.dependence B.independence C.interdependence D.segments
Q: The two basic groups of multivariate techniques are _____. A.dependence methods and interdependence methods B.primary methods and secondary methods C.simple methods and complex methods D.partial methods and complete methods
Q: Which of the following is a mathematical way in which a set of variables can be represented with one equation? A.structuralism B.variate C.ANOVA D.synergy
Q: Which type of analysis involves three or more variables? A.univariate statistical analysis B.bivariate statistical analysis C.multivariate statistical analysis D.all of these choices
Q: Multidimensional scaling provides a means for placing objects in multidimensional space on the basis of respondents' judgments of the similarity of objects.
Q: In cluster analysis, each cluster should have low internal homogeneity and high external heterogeneity.
Q: In factor analysis, communality is a measure of the percentage of a variable's variation that can be explained by the factors.
Q: The rule of parsimony basically means "less is better."
Q: Factor analysis is considered a data reduction technique.
Q: Factor rotation is a mathematical way of simplifying factor results.
Q: A factor loading indicates how strongly a measured variable is correlated with a factor.
Q: The most common rule for extracting factors in factor analysis is to base the number of factors on the number of eigenvalues greater than 1.0
Q: To determine whether the discriminant analysis can be used as a good predictor, information provided in the factor profile is used.
Q: Discriminant analysis predicts a categorical dependent variable based on a linear combination of independent variables.
Q: MANOVA predicts multiple continuous dependent variables with multiple continuous independent variables.
Q: Multicollinearity in regression analysis refers to how strongly interrelated the independent variables in a model are.
Q: In multiple regression, the coefficient of multiple determination, or c2, indicates the percentage of the variation in Y that can be explained by all independent variables.
Q: Partial correlations measure the variance inflation among independent variables.
Q: In multiple regression, dummy variables are those that have no effect on the dependent variable.
Q: In multiple regression, the dependent and independent variables must be metric measures.
Q: Mulitvariate dependence techniques are variants of the general linear model (GLM).
Q: Multiple regression analysis includes a single independent variable but several dependent variables.
Q: The general linear model is a way of explaining and predicting a dependent variable based on fluctuations (variation) from its mean, with the fluctuations due to changes in independent variables.
Q: Nominal and ordinal scales are referred to as nonmetric scales.
Q: The type of measurement scales used does not influence which multivariate statistical techniques are appropriate for the data.
Q: Cluster analysis is a type of interdependence method.
Q: The basic types of multivariate techniques are primary methods and secondary methods.
Q: The variate is a mathematical way in which a set of variables can be represented with one equation.
Q: Multivariate statistical analysis permit the researcher to consider the effects of three or more variables at the same time.
Q: Discuss the steps in interpreting a simple regression output.
Q: Explain the ordinary least-squares (OLS) method of regression analysis and the logic behind it.
Q: Discuss the pros and cons of raw regression estimates and standardized regression estimates and discuss when each is appropriate.
Q: Explain how regression differs from correlation.
Q: Compare and contrast correlation, covariance, and causation.
Q: Discuss the common procedures for testing association for data that are nominal, ordinal, and interval/ratio.
Q: The first step in interpreting a simple regression output is to interpret the significance of the _____.
Q: The test for the statistical significance of the regression model is the ____ test.
Q: The technique use in regression analysis that guarantees that the resulting straight line will produce the least possible total error in using X to predict Y is called the _____ method.
Q: _____ regression estimates should be used when the researcher is testing explanatory hypotheses.
Q: If the purpose of the regression analysis is forecasting, then _____ parameter estimates must be used.
Q: In a regression equation, the slope of the regression line is denoted by the symbol ______.
Q: The regression parameter that represents the height of the regression line relative to horizontal is _____.
Q: In testing for the significance of a correlation, it is hypothesized that the correlation is equal to ______.
Q: In a correlation matrix, the values in the main diagonal equal ______.
Q: The standard format for reporting the correlations between several variables is called the ______.
Q: If the correlation between two variables is -.55, the coefficient of determination is approximately ______.
Q: The symbol for the coefficient of determination is _____.
Q: The square of the correlation coefficient is called the _____.
Q: Covariation in which the association between variables is in the opposite direction indicates a(n) _____ relationship.
Q: A correlation coefficient indicates both the ______ of the relationship between two variables and the ______ of this relationship.
Q: The Pearson product-moment correlation coefficient ranges between ______ and ______.
Q: The Pearson product-moment correlation requires at least ______ data.
Q: The extent to which a change in one variable corresponds systematically to a change in another is referred to as _____.
Q: The statistical measure of the association between two variables is known as the ______ coefficient.
Q: If the number of coupons given out at shopping malls is used to predict the number of tickets that will be sold to a country and western band's performance at a local club, the number of coupons is the ______ variable.
Q: What is the first step in interpreting a simple regression output? A.interpret the individual parameter estimates B.determine the degrees of freedom C.look at the c2 to see if it is significant D.interpret the overall significance of the model
Q: The statistical significance of a regression model is determined using which test? A.t-test B.c2 C.F-test D.Z-test
Q: Which regression estimation technique is based on the logic of how much better a regression line can predict values of Y compared to simply using the mean as a prediction for all observations no matter what the value of X may be? A.visual estimation B.maximum likelihood C.coefficient of determination D.ordinary least squares (OLS)
Q: All of the following are simple regression estimation techniques EXCEPT _____. A.chi-square estimation B.maximum likelihood C.visual estimation D.ordinary least squares (OLS)
Q: Lance is studying the relationship between sales training of the sales force and customer satisfaction and loyalty. When researchers like Lance are focused on explanation rather than prediction, then which of the following is most appropriate when using simple regression? A.standardized regression coefficient (b) B.Y-intercept a C.c2 D.raw parameter estimates
Q: Which of the following is most appropriate if the purpose of the regression analysis is forecasting? A.standardized regression coefficient (b) B.Y-intercept a C.coefficient of determination (R2) D.raw parameter estimates
Q: Which of the following provides a common metric allowing regression results to be compared to one another no matter what the original scale range may have been? A.standardized regression coefficient (b) B.c2 C.coefficient of determination (R2) D.raw parameter estimates
Q: If the regression equation is: Y = -4.2 + 3.6 X , then the expected score for Y when X is 4 would be _____. A.-18.6 B.10.2 C.18.6 D.4
Q: If the regression equation is: Y = 24.35 - 14.2 X , then 24.35 is the ______ , while -14.2 is the ______. A.slope; y-intercept B.independent variable; slope C.dependent variable; y-intercept D.Y-intercept; slope
Q: In the regression equation, Y = a + b X, a is the symbol for the _____. A.slope of the regression line B.Y-intercept of the regression line C.dependent variable D.independent variable
Q: In the regression equation, Y = a + b X , b is the _____. A.y-intercept B.independent variable C.slope of the regression line D.dependent variable
Q: In the regression equation, Y = a + b X, Y is the _____. A.dependent variable B.slope C.independent variable D.Y-intercept
Q: In regression analysis, the dependent variable is also called the _____ variable. A.predictor B.exogenous C.criterion D.internal
Q: In regression analysis, the symbol X is commonly used for the ______ variable, and the symbol Y is commonly used for the ______ variable. A.dependent; moderating B.independent; dependent C.dependent; independent D.independent; moderating
Q: Regression is a(n) _____ technique whereas correlation is a(n) _____ technique. A.dependence; interdependence B.interdependence; dependence C.primary; secondary D.univariate; bivariate
Q: If the t-test is used to test the significance of a correlation coefficient, it is hypothesized that _____. A.r = +1.00 B.r = 0 C.r = -1.00 D.r = 100
Q: In a correlation matrix, the correlations in the main diagonal are all equal to _____. A.-1.00 B.0 C.+0.50 D.+1.00
Q: Which of the following is the standard form for reporting observed correlations among multiple variables? A.correlation matrix B.contingency table C.Pearson grid D.inverse table
Q: If the correlation between X and Y is -0.72, approximately what percentage of the variance in Y can be explained by X? A.52 percent B.72 percent C.85 percent D.28 percent
Q: If the correlation coefficient is - 0.62, the coefficient of determination is approximately: A.+ 0.38 B.+ 0.62 C.- 0.38 D.- 0.23
Q: Which of the following represents the proportion of the total variance of a variable accounted for by another value of another variable? A.product-moment correlation B.correlation coefficient C.F-test D.coefficient of determination
Q: The _____ is a measure obtained by squaring the correlation coefficient. A.t-statistic B.coefficient of determination (R2) C.F-ratio D.Pearson coefficient
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