Accounting
Anthropology
Archaeology
Art History
Banking
Biology & Life Science
Business
Business Communication
Business Development
Business Ethics
Business Law
Chemistry
Communication
Computer Science
Counseling
Criminal Law
Curriculum & Instruction
Design
Earth Science
Economic
Education
Engineering
Finance
History & Theory
Humanities
Human Resource
International Business
Investments & Securities
Journalism
Law
Management
Marketing
Medicine
Medicine & Health Science
Nursing
Philosophy
Physic
Psychology
Real Estate
Science
Social Science
Sociology
Special Education
Speech
Visual Arts
Finance
Q:
A time series decomposition method would not be used to forecast seasonal data.
Q:
Dummy variable regression would be an appropriate method to use to forecast a time series that exhibits a linear trend with no seasonal or cyclical patterns.
Q:
Holt-Winters double exponential smoothing would be an appropriate method to use to forecast a time series that exhibits a linear trend with no seasonal or cyclical patterns.
Q:
A simple exponential forecasting method would not be used to forecast seasonal data.
Q:
A Paasche index more accurately provides a year-to-year comparison of the annual cost of selected products in the market-basket than a Laspeyres index.
Q:
While a simple index is calculated by using the values of one time series, an aggregate index is computed based on the accumulated values of more than one time series.
Q:
Dummy variables are used to model increasing seasonal variation.
Q:
When deseasonalizing a time series observation, the actual time series observation is divided by its seasonal factor.
Q:
When using moving averages to estimate the seasonal factors, we need to compute the centered moving average if there are an odd number of seasons.
Q:
Removing the seasonal effect by dividing the actual time series observation by the estimated seasonal factor associated with the time series observation is called deseasonalization.
Q:
Forecasters using a multiplicative decomposition model or time series regression model, assume that the time series components are changing over time.
Q:
The forecaster who uses MSD (mean squared deviations) to measure the effectiveness of forecasting methods would prefer method 1, which results in several smaller forecast errors, to method 2, which results in one large forecast error equal to the sum of the absolute values of several small forecast errors given by method 1.
Q:
Simple exponential smoothing is an appropriate method for prediction purposes when there is a significant trend present in a time series.
Q:
The smoothing constant is a number that determines how much weight is attached to each observation.
Q:
Exponential smoothing is a forecasting method that applies equal weights to the time series observations.
Q:
Trend refers to a long-run upward or downward movement of a time series over a period of time.
Q:
A univariate time-series model is used to predict future values of a time series based only upon past values of a time series.
Q:
The _________ regression method is used when the response variable is a qualitative or a categorical variable.
A. quadratic
B. logistic
C. multiple
D. simple
Q:
In general, a multiple regression model is considered to be desirable if the value of the C statistic is small and the value of C is less than _____.A. kB. k - 1C. k + 1D. n - k
Q:
If a multiple regression model has a(n) ____________ statistic substantially greater than k + 1, then it can be shown that this model has substantial bias and is undesirable.
A. C
B. F
C. Cook's distance
D. Durbin-Watson
Q:
Stepwise regression uses a series of ___________ tests during each iteration in order to determine which independent variables should be brought into the regression model.
A. C
B. chi-square
C. t or F
D. VIF
Q:
___________ is an iterative variable selection procedure that allows an independent variable to be added to a multiple regression model in one iteration and deleted during the next iteration.
A. Logistic regression
B. Stepwise regression
C. Quadratic regression
D. Backward regression
Q:
Significant _________ may exist when the overall F statistic is significant and the individual t statistics for all independent variables are insignificant.
A. autocorrelation
B. independence
C. multicollinearity
D. outliers
Q:
In a multiple regression model, we can conclude that multicollinearity exists if the average variance inflation factor is substantially greater than _____.
A. 100
B. 5
C. 10
D. 1
Q:
Multicollinearity is severe if the largest variance inflation factor (VIF) is greater than _____.
A. 100
B. 5
C. 10
D. 1
Q:
The primary use of stepwise regression is to identify the most important ___________ that should be included in the multiple regression model.
A. independent variables
B. dependent variables
C. dummy variables
D. quadratic variables
Q:
If the simple correlation coefficient between two independent variables is greater than .90, then ______________________ is considered to be severe.
A. autocorrelation
B. interaction
C. multicollinearity
D. coefficient of determination
Q:
Given the regression model y = 0 + 1x1 + 2x2 + 3x12 + 4x22 + , if we wish to test the significance of higher-order terms (x12and x22),which test would we use?A. overall F testB. Durbin-Watson testC. partial F testD. t testE. Cook's distance measure
Q:
A very insignificant independent variable (an independent variable that has a very weak relationship with the dependent variable) is added to a multiple regression equation. As a result of this change, the value of the explained variation (SSR) will _________, the value of the multiple coefficient of determination () will _________, and the calculated value of the F statistic will most likely _________.
A. decrease, increase, decrease
B. increase, decrease, decrease
C. increase, increase, increase
D. increase, increase, decrease
E. decrease, decrease, decrease
Q:
In a multiple regression analysis, the current model has three independent variables. The analyst decides to add another (fourth) independent variable while retaining the other three independent variables. As a result of this addition, the value of MSE will ____________ decrease.
A. always
B. sometimes
C. never
Q:
In using a regression model, if a new independent variable is added, the value of (the coefficient of multiple determination) will ___________ decrease.
A. always
B. sometimes
C. never
Q:
If a regression model with k independent variables has a C statistic less than _______, then the model is considered to be desirable.A. k + 1B. kC. k - 1D. 1/kE. k - 2
Q:
Adding any independent variable to a regression model will increase ____________.
A. adjusted or B. s
C. MSE
D. E. the length of all prediction intervals
Q:
The graph of the prediction equation obtained from the model y = 0 + 1X1 +2X12 + is a(n) ____________.A. lineB. planeC. parabolaD. exponential curve
Q:
In the quadratic regression model y = 0 + 1X1 + 2X12 + , the 2 term represents theA. rate of curvature of the parabola.B. value of Y when X is zero.C. shift parameter of the parabola.D. y-intercept of the parabola.
Q:
In the quadratic regression model y = 0 + 1X1 + 2X12 + ,the 1term represents theA. rate of curvature of the parabola.B. value of Y when X is zero.C. shift parameter of the parabola.D. y-intercept of the parabola.
Q:
In the quadratic regression model y = 0 + 1X1 + 2X12 + , if the term 2 is ___________ zero, then the parabola opens ____________.A. less than, upwardB. greater than, upwardC. greater than, either upward or downwardD. less than, either upward or downwardE. equal to, downward
Q:
As we increase the number of independent variables in a multiple regression model, the F statistic will __________ increase.
A. always
B. sometimes
C. never
Q:
The general form of the quadratic multiple regression models isA. y = 1x1 + 2x2 + B. y = 0 + 1x1 + 2x2 + C. y = 0 + 1x + 2x2 + D. y = 0 + 1x2 + E. y = 0 + 1x1 +2x22+
Q:
Assumptions of a regression model can be evaluated by plotting and analyzing the ____________.
A. independent variables
B. dependent variables
C. error terms
D. beta values
Q:
In a multiple regression model, the residuals were plotted against the values of one of the independent variables. The plot exhibited a funneling out pattern of residuals. This means that as the value of the independent variable increases, the error terms tend to ___________ and the model assumption of __________ is violated.
A. increase, constant variance
B. increase, independence
C. decrease, constant variance
D. decrease, normality
Q:
Plotting the residuals in a time-ordered sequence will reveal possible violations of the __________ of error terms assumption.
A. normality
B. independence
C. constant variation
D. residual sum
Q:
Dummy variables take on the values of _________ and are used to model the effects of different levels of qualitative variables.A. 1 or -1B. 1 or 2C. 0 or 2D. 0 or 1
Q:
The effects of different levels of qualitative independent variables are described using ___________ variables.
A. dependent
B. response
C. dummy
D. quantitative
Q:
The y-intercept (0) in a multiple regression model represents the estimated value of the __________ variable, when the value of all independent variables are _________.A. response, oneB. dummy, zeroC. response, zeroD. dummy, one
Q:
The ____________ term describes the effects on y of all factors other than the independent variables in a multiple regression model.
A. dependent
B. t test
C. error
D. dummy
Q:
Dummy or indicator variables typically are values of zero or one, and are used to model the effects of different levels of ___________ variables.
A. qualitative
B. quantitative
C. ratio
D. measured
Q:
The multiple _________ measures the proportion of the variation in y (response variable) explained by the multiple regression model or the set of independent variables included in the multiple regression equation.
A. correlation coefficient
B. coefficient of determination
C. total variation
D. standard error
E. F test
Q:
In using the multiple regression method, we can model the effects of the different levels of a qualitative independent variable by using a(n) ____________.
A. interaction variable
B. cross-product term
C. quadratic term
D. dummy (indicator) variable
E. variance equalizing transformation
Q:
If we are testing the significance of the independent variable X1 and we reject the null hypothesis H0: 1 = 0, we conclude thatA. X1 is significantly related to y.B. X1 is not significantly related to y.C. X1 is an unimportant independent variable.D. 1 is significantly related to the dependent variable y.
Q:
Which one of the following tools is not used to check the normality of residuals assumption for a multiple regression model?
A. histogram
B. stem-and-leaf display
C. scatter diagram
D. normal plot
Q:
In a multiple regression analysis, if the normal probability plot ____________, then it can be concluded that the assumption of normality is not violated.
A. is a straight line
B. has the shape of a symmetric bell-shaped curve
C. is greatly curved
D. is left skewed
E. has the shape of a parabola that opens upward
Q:
In regression analysis, the standard error(s) is _________ greater than the standard deviation of y (the dependent variable).
A. always
B. sometimes
C. never
Q:
Which of the following residual plots is not used in regression analysis?
A. residuals vs. parameter estimates
B. residuals vs. values of an independent variable
C. residuals vs. time order
D. residuals vs. predicted values of the dependent variable
E. standardized residuals vs. predicted values of the dependent variable
Q:
In multiple regression analysis, a desirable residual plot has what type of appearance?
A. curved
B. cyclical
C. fanning out
D. funneling in
E. horizontal band
Q:
Which one of the following is not an assumption about the residuals in a regression model?
A. constant variance
B. independence
C. normality
D. variance of zero
E. mean of zero
Q:
An acceptable residual plot exhibits
A. increasing error variance.
B. decreasing error variance.
C. constant error variance.
D. a curved pattern.
E. a mixture of increasing and decreasing error variance.
Q:
is defined as
A. total variation/explained variation.
B. explained variation/total variation.
C. unexplained variation/explained variation.
D. unexplained variation/total variation.
Q:
In multiple regression analysis, which one of the following is the appropriate notation for error (residual)?
A. B. C. D. E. None of these answers is correct.
Q:
In multiple regression analysis, the explained sum of squares divided by the total sum of squares yields the __________.
A. standarderror
B. F statistic
C. D. adjusted or E. t statistic
Q:
In multiple regression analysis, the mean square regression divided by mean square error yields the _________.
A. standard error
B. F statistic
C. D. adjusted or
E. t statistic
Q:
The graph of the prediction equation obtained from the model y = 0 +1X1 + 2X2 + is a(n) __________.A. lineB. planeC. parabolaD. exponential curve
Q:
An investigator hired by a client suing for sex discrimination has developed a multiple regression model for employee salaries for the company in question. In this multiple regression model, the salaries are in thousands of dollars. For example, a data entry of 35 for the dependent variable indicates a salary of $35,000. The indicator (dummy) variable for gender is coded as X1 = 0 if male and X1 = 1 if female. The computer output of this multiple regression model shows that the coefficient for this variable (X1) is -4.2. The t test showed that X1 was significant at = 0.1. This result implies that for male and female workers of the company,A. on the average, females earn $4,200 less.B. on the average, males earn $4,200 less.C. on the average, salaries do not differ.D. on the average, males have 4.2 more years of experience.E. on the average, females have 4.2 more years of experience.
Q:
If it is desired to include marital status in a multiple regression model by using the categories single, married, separated, divorced, and widowed, what will be the effect on the model?
A. One more independent variable will be included.
B. Two more independent variables will be included.
C. Three more independent variables will be included.
D. Four more independent variables will be included.
E. Five more independent variables will be included.
Q:
A multiple regression analysis with 20 observations on each of three independent variables and the dependent variable would yield ______ and _______ degrees of freedom, respectively, for regression (explaineD) and error.
A. 3, 17
B. 3, 16
C. 4, 16
D. 3, 19
E. 3, 20
Q:
Which of the following is not an assumption of the multiple linear regression model?
A. The error terms are independent.
B. The population of error terms has a normal distribution.
C. Populations of error terms observed at different combinations of values of the independent variable (x1, x2,. . .,xk) have equal variances.
D. The level of measurement of the data for the dependent variable is at least ordinal.
E. At any combination of values of x1, x2, . . . ,xk, the population of potential error term values has a mean equal to zero.
Q:
The mean square error of a multiple regression model with k independent variables and n observations is __________.A. SSE/nB. SSE/[n + (k+1)]C. SSE/[n- (k+1)]D. SSE/(k+1)
Q:
For a given multiple regression model with three independent variables, the value of the adjusted multiple coefficient of determination is_________ less than R2.
A. always
B. sometimes
C. never
Q:
The range of feasible values for the multiple coefficient of correlation is from ________.A. 0 to B. -1 to 0C. -1 to 1D. 0 to 1E. - to 0
Q:
The range of feasible values for the multiple coefficient of determination is from ________.A. 0 to B. -1 to 0C. -1 to 1D. 0 to 1E. - to 0
Q:
The multiple coefficient of determination is the _______ divided by the total variation
A. unexplained variation
B. SSE
C. explained variation
D. distance value
E. leverage value
Q:
When using a multiple regression model, we assume that error terms (residuals) are distributed according to a(n) ________________ distribution.
A. binomial
B. normal
C. exponential
D. Poisson
Q:
Neural network modeling represents the response variable as a linear function of the predictor variables.
Q:
The penalty weight used in the neural network models controls the tradeoff between overfitting and underfitting.
Q:
A neural network model is nonlinear.
Q:
To avoid overfitting in a neural network model, the parameter estimates that are used minimize the least squares criterion.
Q:
A major drawback of neural network modeling is that its parameters are usually uninterpretable.
Q:
A single-layer perceptron neural network model consists of one layer called the hidden layer.
Q:
A regression technique for analyzing large data sets is neural network modeling.
Q:
The natural logarithm of the odds is called the logit.