Binary Logistic Regression

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.

Questions

• 1. Which of the following is true?

  • (a) Binomial logistic regression is the same as multiple regression

  • (b) Binomial logistic regression can only be used with scores.

  • (c) Binomial logistic regression is not at all like multiple regression

  • (d) Binomial logistic regression is analogous to multiple regression

• 2. The logit value in logistic regression is

  • (a) Is the cube root of the sample size

  • (b) Is an instruction to record the data

  • (c) Is a logarithm of a digit

  • (d) Is the natural logarithm of the odds ratio

• 3. In binary logistic regression, the dependent variable is

  • (a) Metric and non-categorical

  • (b) Non-metric and dichotomous

  • (c) It can be metric or non-metric

  • (d) None of these

• 4. Logistic regression follows a distribution of

  • (a) Normal

  • (b) Binomial

  • (c) Poisson

  • (d) Skewed-Normal

• 5. The shape of logistic curve is

  • (a) S-Shaped

  • (b) L-Shaped

  • (c) U-Shaped

  • (d) Inverted U-shaped