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Statistics Discovering Statistics Using IBM SPSS Statistics QUESTIONS FOR PRACTICE
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Summary Discovering Statistics Using IBM SPSS Statistics 26th Dec 2022
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STUDY GUIDE INTERMEDIATE STATISTICS I UNIVERSITY COLLEGE 2020, Discovering statistics using IBM SPSS statistics, summary, samenvatting
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BBS1003-Statistics-Syllabus summary, lectures, videos, Andy Field
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Discovering Statistics using IBM SPSS Statistics Field 4th Edition Test BankDiscovering Statistics using IBM SPSS Statistics
Field 4th Edition Test Bank
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-4th-edition-test-bank/
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, Discovering Statistics Using IBM SPSS Statistics 4th edition
Andy Field Multiple Choice Testbank
Chapter 8
Regression
Using this testbank:
This testbank is designed to be used in conjunction with Field, A. P. (2013). Discovering Statistics Using
IBM SPSS Statistics, 4th edition. London: SAGE.
Please note, that in this testbank the first choice is always the correct answer. Additionally, each multiple
choice question comes with suggested feedback to further support your students.
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,Exercise FieldStat4 8.MC.001.
What does the graph below show?
Author’s Notes
Multiple Choice Options (correct choice comes first) Feedback (rejoinder) for this choice
Normality of residuals Yes, this is correct. The P-P plot shows
up deviations from normality as deviations
from the diagonal line. For our model, the
dots lie almost exactly along the diagonal,
which, as we know, indicates a normal
distribution: hence this plot also suggests
that the residuals are normally distributed
(see Section 8.7.7).
, Linearity This is incorrect. We can look for
heteroscedasticity and non-linearity using
a plot of standardized residuals against
standardized predicted values (see
Section 8.7.7).
Homoscedasticity This is incorrect. We can look for
heteroscedasticity and non-linearity using
a plot of standardized residuals against
standardized predicted values (see
Section 8.7.7).
Multicollinearity This is incorrect. Multicollinearity is when
a perfect linear relationship exists
between two or more of the predictors
(this is bad). The predictor variables
should not correlate too highly (see
Section 8.7.7).
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