ISDS 361B
Which of the following is not present in a time series? -trend -operational variations - seasonality - cycles
-operational variations
In a multiple regression model, the mean of the probability distribution of the error variable is assumed to be: - 1.0 - 0.0 - Any value greater than 1 - k, where k is the number of independent variables included in the model
0.0
A multiple regression analysis includes 4 independent variables results in sum of squares for regression of 1200 and sum of squares for error of 800. Then, the multiple coefficient of determination will be: - 0.667 - 0.600 - 0.400 - 0.200
0.600
The regression line y= 3 + 2x has been fitted to the data points (4, 8), (2, 5), and (1, 2). yˆThe sum of the squared residuals will be: - 7 - 15 - 8 - 22
22
RDN's sales of cable modem in San Mateo, California, for the months of January through April were as follows: January - 50, February - 80, March - 70, and April - 60. Suppose exponential smoothing is used with a smoothing constant, alpha, of .20. If the forecast for January was 50, the forecast for May would be approximately: - 58. - 59. - 60. - 63.
59
June forecast: 71. June actual: 68. Alpha = 1.0. July's exponentially smoothed forecast is: - 68 - 71 - 70.7 - 68.3.
68
In a multiple regression analysis, if the model provides a poor fit, this indicates that: - the sum of squares for error will be large - the standard error of estimate will be large - the multiple coefficient of determination will be close to zero - All of the above
All of the above
In a multiple regression analysis, when there is no linear relationship between each of the independent variables and the dependent variable, then - multiple t-tests of the individual coefficients will likely show some are significant - we will conclude erroneously that the model has some validity -the chance of erroneously concluding that the model is useful is substantially less with the F-test than with multiple t-tests - All of the above statements are correct
All of the above statements are correct
What is not involved in the initial form hypothesis step of the time series forecasting process? - Graphing - Statistical verification of the hypothesis - Calculating the value of parameters - Gathering data.
Calculating the value of parameters
Which of the following statements regarding multicollinearity is not true? - It exists in virtually all multiple regression models. - It is also called collinearity and intercorrelation. - It is a condition that exists when the independent variables are highly correlated with the dependent variable. - It does not affect the F-test of the analysis of variance.
It is a condition that exists when the independent variables are highly correlated with the dependent variable.
In situations where forecast errors are to be weighed in proportion to their magnitude, the preferred performance evaluator would most likely be: - MSE - MAD - MAPE - LAD.
MAPE
What would be the coefficient of determination if the total sum of squares (SST) is 23.27 and the sum of squares due to regression (SSR) is 12.02? -0.48 -0.52 -1.94 -2.07
R^2 = SSR/SST = 12.02/23.27 = 0.52
Which of the following techniques is used to predict the value of one variable on the basis of other variables? -Correlation analysis - Coefficient of correlation - Covariance -Regression analysis
Regression analysis
What do we mean when we say that a simple linear regression model is "statistically" useful? - All the statistics computed from the sample make sense - The model is an excellent predictor of y - The model is "practically" useful for predicting y - The model is a better predictor of y than the sample y
The model is a better predictor of y than the sample y hat
If a time series plot exhibits a horizontal pattern, then the following is true. -There is still not enough evidence to conclude that the time series is stationary. -It is evident that the time series is stationary. - The data fluctuates around the variable mean. -There is no relationship between time and the time series variable.
There is still not enough evidence to conclude that the time series is stationary.
A time series with a seasonal pattern can be modeled by treating the season as which of the following? -a dummy variable -a dependent variable -a predictor variable -a quantitative variable
a dummy variable
A time series that shows a recurring pattern over one year or less is said to follow which of the following? -a seasonal pattern -a stationary pattern -a cyclical pattern -a horizontal pattern
a seasonal pattern
Time series analysis: - attempts to use historic values to forecast future values. - does not involve regression analysis. - eliminates autocorrelation. - assumes random variation is zero.
attempts to use historic values to forecast future values.
In the simple linear regression model, the slope represents the: - value of y when x = 0 - average change in y per unit change in x -value of x when y = 0 -average change in x per unit change in y
average change in y per unit change in x
If the sum of squared residuals is zero, then the: -coefficient of determination must be 1.0 - coefficient of correlation must be 1.0 - coefficient of determination must be 0. 0 - coefficient of correlation must be 0.0
coefficient of determination must be 1.0
For the multiple regression model:, y= 75 + 25x1 - 15x2 + 10x3, if were to increase by 5, holding x1 and x3 constant, the value of y will: - increase by 5 - increase by 75 - decrease on average by 5 - decrease on average by 75
decrease on average by 75
In a linear regression model, the variable that is being predicted or explained is known as which of the following? It is denoted by y and is often referred to as the response variable. -independent variable -regression variable -residual variable -dependent variable
dependent variable
In the exponential smoothing (ES) technique, the value of alpha, the smoothing constant: - may assume any non-negative value. - determines the forecasting model's responsiveness to abrupt changes. - typically is at the higher end in the range of possible values. - is preset by the analyst and not subject to validity testing.
determines the forecasting model's responsiveness to abrupt changes.
In regression analysis, the residuals represent the: - difference between the actual y values and their predicted values - difference between the actual x values and their predicted values - square root of the slope of the regression lined - change in y per unit change in x
difference between the actual y values and their predicted values
Which of the following uses a weighted average of past time series values as the forecast? -the causal model -correlation analysis -the qualitative method -exponential smoothing
exponential smoothing
A regression analysis between weight (y in pounds) and height (x in inches) resulted in the following least squares line: = 120 + 5x. This implies that if the height is increased by 1 inch, the weight, on average, is expected to: - increase by 1 pound - decrease by 1 pound - increase by 5 pounds - increase by 24 pounds
increase by 5 pounds
In testing the validity of a multiple regression model, a large value of the F-test statistic indicates that: - most of the variation in the independent variables is explained by the variation in y - most of the variation in y is explained by the regression equation -most of the variation in y is unexplained by the regression equation -the model provides a poor fit
most of the variation in y is explained by the regression equation
If a group of independent variables are not significant individually but are significant as a group at a specified level of significance, this is most likely due to - autocorrelation - the presence of dummy variables - the absence of dummy variables - multicollinearity
multicollinearity
The degree of correlation among independent variables in a regression model is called which of the following? -interaction -the sum of squared errors (SSE) -the coefficient of determination -multicollinearity
multicollinearity
When the independent variables are correlated with one another in a multiple regression analysis, this condition is called: - heteroscedasticity - homoscedasticity - multicollinearity - elasticity
multicollinearity
Which of the following is a statistical procedure used to develop an equation showing how two variables are related? -regression analysis -data mining -time series analysis -factor analysis
regression analysis
The process of making estimates and drawing conclusions about one or more characteristics of a population through analysis of sample data drawn from the population is known as which of the following? -inductive inference -Bayesian inference -statistical inference -deductive inference
statistical inference
The least squares method for determining the best fit minimizes: - total variation in the dependent variable - sum of squares for error - sum of squares for regression - All of the above
sum of squares for error
The residual is defined as the difference between: - the actual value of y and the estimated value of y - the actual value of x and the estimated value of x - the actual value of y and the estimated value of x - the actual value of x and the estimated value of y
the actual value of y and the estimated value of y
Given the least squares regression line y hat = 5 -2x: - the relationship between x and y is positive - the relationship between x and y is negative - as x increases, so does y - as x decreases, so does y
the relationship between x and y is negative
In a simple linear regression model, y = 𝛽0 + 𝛽1x + 𝜀 the parameter 𝛽1 represents which of the following? -the error term -the mean value of x -the intercept -the slope of the true regression line
the slope of the true regression line
Which of the following is a measure of the error that results from using the estimated regression equation to predict the values of the dependent variable in the sample? -the sum of squares due to error (SSE) -the residual -the error term -the sum of squares due to regression (SSR)
the sum of squares due to error (SSE)
Which of the following statements is the objective of the moving averages and exponential smoothing methods? -to smooth out random fluctuations in the time series -to transform a nonstationary time series into a stationary series -to maximize forecast accuracy measures -to characterize the variable fluctuations by an exponential equation
to smooth out random fluctuations in the time series
Which of the following states the objective of time series analysis? -to predict the values of a time series based on one or more other variables -to analyze the cause-and-effect relationship of a dependent variable with a time series and one or more other variables -to uncover a pattern in a time series and then extrapolate the pattern into the future -to use present variable values to study what should have been the ideal past values
to uncover a pattern in a time series and then extrapolate the pattern into the future
