Term 1 of 77
When working with regression analysis, an outlier is:
a measurement that is taken from a different population than the rest of the data.
any observation that does not fit the trend shown by the remaining data.
a data point that is significantly higher than the average of the dataset.
an observation that perfectly aligns with the trend of the data.
Term 2 of 77
The tests of significance in regression analysis are based on several assumptions about the
error term ɛ. Additionally, we make an assumption about the form of the relationship between
x and y. We assume that the relationship between x and y is:
normal
exponential
linear
autocorrelation
Term 3 of 77
guarantees that the best model for a given number of variables will be found
linear trend regression.
stepwise regression
autocorrelation
best-subsets regression
,Term 4 of 77
durbin-watson is generally inconclusive for
smaller sample sizes
dependent variable
increases
bigger populations
Term 5 of 77
what value of Durbin-Watson statistic indicates that no autocorrelation is present?
three
two
one
four
Term 6 of 77
In regression analysis, the equation in the form y = 𝛽0 + 𝛽1x + ε is called the:
regression equation
regression model
normal probability plot
interaction
Term 7 of 77
test used to determine whether first-order autocorrelation is present
ramsey reset test
confidence interval
durbin-watson test
vif test
,Term 8 of 77
Influential observations always:
cannot be negative
dependent variable
r2 = SSR/(SSE + SSR)
none of the above
Term 9 of 77
when determining the best estimated regression equation to model a set of data, the
procedure that begins each step by determining whether any of the variables already in the
model should be removed is called:
forward selection
stepwise regression
individual significance
best-subsets regression
Term 10 of 77
Suppose a residual plot of x verses the residuals, y - ŷ, shows a nonconstant variance. In
particular, as the values of x increase, suppose that the values of the residuals also increase.
This means that:
as the values of x decrease, the variance of the residuals becomes constant.
they assume that the pattern of the past will continue into the future.
the same for all values of x
as the values of x get larger, the ability to predict y becomes less accurate.
, Term 11 of 77
When studying the relationship between two quantitative variables, an interval estimate of the
mean value of y for a given value of x is called a(n):
confidence interval
multicollinearity
linear
regression model
Term 12 of 77
The mathematical equation relating the independent variable to the expected value of the
dependent variable, , is known as the:
prediction interval
correlation equation
regression equation
regression model
Term 13 of 77
If the coefficient of determination is a positive value, then the coefficient of correlation:
cannot be negative.
can be either negative or positive
can be approximated by a normal distribution.
must also be positive
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