QMB 3200 Final Updated Test Bank and
Answers (A+ Guaranteed)
‣ Durbin-Watson test -✓✓A test to determine whether first-order autocorrelation is
present.
‣ General linear model -✓✓A model of the form y=β0+β1z1+β2z2+⋯+βpzp+ε, where
each of the independent variables zj(j=1,2,...,p) is a function of x1,x2,...,xk, the variables
for which data have been collected.
‣ interaction -✓✓The effect produced when the levels of one factor interact with the
levels of another factor in influencing the response variable.
The effect of two independent variables acting together.
‣ variable selection procedures -✓✓Methods for selecting a subset of the independent
variables for a regression model.
‣ Time series -✓✓A sequence of observations on a variable measured at successive
points in time or over successive periods of time.
‣ Mean Squared Error (MSE) -✓✓The average of the sum of squared forecast errors.
‣ Time series plot -✓✓A graphical presentation of the relationship between time and the
time series variable. Time is shown on the horizontal axis and the time series values are
shown on the vertical axis.
‣ horizontal pattern -✓✓A horizontal pattern exists when the data fluctuate around a
constant mean.
‣ moving average -✓✓A forecasting method that uses the average of the most recent k
data values in the time series as the forecast for the next period.
‣ stationary time series -✓✓A time series whose statistical properties are independent of
time. For a stationary time series the process generating the data has a constant mean
and the variability of the time series is constant over time.
‣ trend pattern -✓✓A trend pattern exists if the time series plot shows gradual shifts or
movements to relatively higher or lower values over a longer period of time.
, ‣ smoothing constant -✓✓A parameter of the exponential smoothing model that
provides the weight given to the most recent time series value in the calculation of the
forecast value.
‣ seasonal pattern -✓✓A seasonal pattern exists if the time series plot exhibits a
repeating pattern over successive periods. The successive periods are often one-year
intervals, which is where the name seasonal pattern comes from.
‣ cyclical pattern -✓✓A cyclical pattern exists if the time series plot shows an alternating
sequence of points below and above the trend line lasting more than one year.
‣ mean absolute error -✓✓The average of the absolute values of the forecast errors.
‣ When autocorrelation is present... -✓✓the error terms are not independent.
‣ The Durbin-Watson test statistic ranges in value from -✓✓zero to four
‣ If successive values of the residuals are close together (positive autocorrelation) -
✓✓the value of the Durbin-Watson test statistic will be small.
‣ If successive values of the residuals are far apart (negative autocorrelation) -✓✓the
value of the Durbin-Watson statistic will be large.
‣ Null Hypothesis (Ho) -✓✓The hypothesis tentatively assumed true in the hypothesis
testing procedure
‣ Two-tailed test -✓✓A hypothesis test in which rejection of the null hypothesis occurs
for values of the test statistic in either tail of its sampling distribution.
‣ Alternative Hypothesis (Ha) -✓✓The hypothesis concluded to be true if the null
hypothesis is rejected
‣ P-Value -✓✓A probability that provides a measure of the evidence against the null
hypothesis given by the sample
‣ Type 1 error -✓✓The error of rejecting Ho when it is true
‣ Type 2 error -✓✓The error of accepting H0 when it is false
‣ Level of significance (alpha level) -✓✓The probability of making a Type 1 error when
the hypothesis is true as an equality
‣ Critical Value -✓✓A value that is compared with the test statistic that helps determine
whether Ho should be rejected.
Answers (A+ Guaranteed)
‣ Durbin-Watson test -✓✓A test to determine whether first-order autocorrelation is
present.
‣ General linear model -✓✓A model of the form y=β0+β1z1+β2z2+⋯+βpzp+ε, where
each of the independent variables zj(j=1,2,...,p) is a function of x1,x2,...,xk, the variables
for which data have been collected.
‣ interaction -✓✓The effect produced when the levels of one factor interact with the
levels of another factor in influencing the response variable.
The effect of two independent variables acting together.
‣ variable selection procedures -✓✓Methods for selecting a subset of the independent
variables for a regression model.
‣ Time series -✓✓A sequence of observations on a variable measured at successive
points in time or over successive periods of time.
‣ Mean Squared Error (MSE) -✓✓The average of the sum of squared forecast errors.
‣ Time series plot -✓✓A graphical presentation of the relationship between time and the
time series variable. Time is shown on the horizontal axis and the time series values are
shown on the vertical axis.
‣ horizontal pattern -✓✓A horizontal pattern exists when the data fluctuate around a
constant mean.
‣ moving average -✓✓A forecasting method that uses the average of the most recent k
data values in the time series as the forecast for the next period.
‣ stationary time series -✓✓A time series whose statistical properties are independent of
time. For a stationary time series the process generating the data has a constant mean
and the variability of the time series is constant over time.
‣ trend pattern -✓✓A trend pattern exists if the time series plot shows gradual shifts or
movements to relatively higher or lower values over a longer period of time.
, ‣ smoothing constant -✓✓A parameter of the exponential smoothing model that
provides the weight given to the most recent time series value in the calculation of the
forecast value.
‣ seasonal pattern -✓✓A seasonal pattern exists if the time series plot exhibits a
repeating pattern over successive periods. The successive periods are often one-year
intervals, which is where the name seasonal pattern comes from.
‣ cyclical pattern -✓✓A cyclical pattern exists if the time series plot shows an alternating
sequence of points below and above the trend line lasting more than one year.
‣ mean absolute error -✓✓The average of the absolute values of the forecast errors.
‣ When autocorrelation is present... -✓✓the error terms are not independent.
‣ The Durbin-Watson test statistic ranges in value from -✓✓zero to four
‣ If successive values of the residuals are close together (positive autocorrelation) -
✓✓the value of the Durbin-Watson test statistic will be small.
‣ If successive values of the residuals are far apart (negative autocorrelation) -✓✓the
value of the Durbin-Watson statistic will be large.
‣ Null Hypothesis (Ho) -✓✓The hypothesis tentatively assumed true in the hypothesis
testing procedure
‣ Two-tailed test -✓✓A hypothesis test in which rejection of the null hypothesis occurs
for values of the test statistic in either tail of its sampling distribution.
‣ Alternative Hypothesis (Ha) -✓✓The hypothesis concluded to be true if the null
hypothesis is rejected
‣ P-Value -✓✓A probability that provides a measure of the evidence against the null
hypothesis given by the sample
‣ Type 1 error -✓✓The error of rejecting Ho when it is true
‣ Type 2 error -✓✓The error of accepting H0 when it is false
‣ Level of significance (alpha level) -✓✓The probability of making a Type 1 error when
the hypothesis is true as an equality
‣ Critical Value -✓✓A value that is compared with the test statistic that helps determine
whether Ho should be rejected.
