Abstract
This chapter discusses a methodology that is more or less analogous to linear regression discussed in the previous chapter, Binary Logistic Regression. In a binary logistic regression, a single dependent variable (categorical: two categories) is predicted from one or more independent variables (metric or non-metric). This chapter also explains what the logistic regression model tells us: Interpretation of regression coefficients and odds ratios using IBM SPSS 20.0. The example detailed in this chapter involves one metric- and four non-metric-independent variables.
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Questions
Questions
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1.
Which of the following is true?
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(a)
Binomial logistic regression is the same as multiple regression
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(b)
Binomial logistic regression can only be used with scores.
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(c)
Binomial logistic regression is not at all like multiple regression
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(d)
Binomial logistic regression is analogous to multiple regression
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(a)
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2.
The logit value in logistic regression is
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(a)
Is the cube root of the sample size
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(b)
Is an instruction to record the data
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(c)
Is a logarithm of a digit
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(d)
Is the natural logarithm of the odds ratio
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(a)
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3.
In binary logistic regression, the dependent variable is
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(a)
Metric and non-categorical
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(b)
Non-metric and dichotomous
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(c)
It can be metric or non-metric
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(d)
None of these
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(a)
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4.
Logistic regression follows a distribution of
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(a)
Normal
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(b)
Binomial
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(c)
Poisson
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(d)
Skewed-Normal
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(a)
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5.
The shape of logistic curve is
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(a)
S-Shaped
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(b)
L-Shaped
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(c)
U-Shaped
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(d)
Inverted U-shaped
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(a)
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Sreejesh, S., Mohapatra, S., Anusree, M.R. (2014). Binary Logistic Regression. In: Business Research Methods. Springer, Cham. https://doi.org/10.1007/978-3-319-00539-3_11
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DOI: https://doi.org/10.1007/978-3-319-00539-3_11
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Publisher Name: Springer, Cham
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Online ISBN: 978-3-319-00539-3
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