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ISYE 6414 Midterm Prep WITH 100- SURE ANSWERS.
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10/6/24, 7:56 AMEUNICE
ISYE 6414 Midterm Prep WITH QUESTIONS AND 100% SURE
ANSWES
Terms in this set (74)
We can assess the constant variance True
assumption in linear regression by plotting
the residuals vs. fitted values.
If one confidence interval in the pairwise True
comparison in ANOVA includes zero, we
conclude that the two corresponding
means are plausibly equal.
The assumption of normality is not required False (Explanation: is required)
in linear regression to make inference on
the regression coefficients.
We cannot estimate a multiple linear False (Explanation: linearly dependent)
regression model if the predicting variables
are linearly independent.
If a predicting variable is a categorical True
variable with 5 categories in a linear
regression model without intercept, we will
include 5 dummy variables.
If the normality assumption does not hold True
for a regression, we may use a
transformation on the response variable.
The prediction of the response variable has True
higher uncertainty than the estimation of
the mean response.
Statistical inference for linear regression False (Explanation: small sample size is fine)
under normality relies on large sample size.
A nonlinear relationship between the False (Explanation: Nonlinear relationships can often be modeled using linear
response variable and a predicting variable regression by including polynomial terms of the predicting variable, for example.)
cannot be modeled using regression.
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Assumption of normality in linear True
regression is required for confidence
intervals, prediction intervals, and
hypothesis testing.
If the confidence interval for a regression True
coefficient contains the value zero, we
interpret that the regression coefficient is
plausibly equal to zero.
The smaller the coefficient of False (Explanation: The larger the R-squared)
determination or R-squared, the higher the
variability explained bythe simple linear
regression.
The estimators of the variance parameter True
and of the regression coefficients in a
regression model are random variables.
The standard error in linear regression True
indicates how far the data points are from
the regression line, on average.
A linear regression model is a good fit to False (Explanation: There are other things to check: assumptions, MSE, etc.)
the data set if the R-squared is above 0.90.
In ANOVA, we assume the variance of the False (Explanation: is the same across all populations)
response variable is different for each
population.
The F-test in ANOVA compares the True
between variability versus the within
variability.
In testing for subsets of coefficients in a False (Explanation: The null hypothesis is that all coefficients are equal to zero; none
multiple linear regression, the null are significant in predicting the response.)
hypothesis we test
for is that all coefficients are equal;
H_0: B_1 = B_2 = ... = B_kf
The only assumptions for a simple linear False
regression model are linearity, constant
variance, and normality.
In a simple linear regression model, the True
variable of interest is the response variable.
The constant variance assumption is False
diagnosed by plotting the predicting
variable vs. the response variable.
β 1 is an unbiased estimator for β 0 . False
The estimator σ ^ 2 is a fixed variable. False
The ANOVA model with a qualitative True
predicting variable with k levels/classes will
have k + 1 parameters to estimate.
Under the normality assumption, the True
estimator for β 1 is a linear combination of
normally distributed random variables.
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