15  Tang-tastic Ordinal Logistic Regression

Fun fact!

Tang-tastic! So tangy it could wake you up better than coffee.

mindmap
  root((Regression 
  Analysis)
    Continuous <br/>Outcome Y
      {{Unbounded <br/>Outcome Y}}
        )Chapter 3: <br/>Ordinary <br/>Least Squares <br/>Regression(
          (Normal <br/>Outcome Y)
      {{Nonnegative <br/>Outcome Y}}
        )Chapter 4: <br/>Gamma Regression(
          (Gamma <br/>Outcome Y)
      {{Bounded <br/>Outcome Y <br/> between 0 and 1}}
        )Chapter 5: Beta <br/>Regression(
          (Beta <br/>Outcome Y)
      {{Nonnegative <br/>Survival <br/>Time Y}}
        )Chapter 6: <br/>Parametric <br/> Survival <br/>Regression(
          (Exponential <br/>Outcome Y)
          (Weibull <br/>Outcome Y)
          (Lognormal <br/>Outcome Y)
        )Chapter 7: <br/>Semiparametric <br/>Survival <br/>Regression(
          (Cox Proportional <br/>Hazards Model)
            (Hazard Function <br/>Outcome Y)
    Discrete <br/>Outcome Y
      {{Binary <br/>Outcome Y}}
        {{Ungrouped <br/>Data}}
          )Chapter 8: <br/>Binary Logistic <br/>Regression(
            (Bernoulli <br/>Outcome Y)
        {{Grouped <br/>Data}}
          )Chapter 9: <br/>Binomial Logistic <br/>Regression(
            (Binomial <br/>Outcome Y)
      {{Count <br/>Outcome Y}}
        {{Equidispersed <br/>Data}}
          )Chapter 10: <br/>Classical Poisson <br/>Regression(
            (Poisson <br/>Outcome Y)
        {{Overdispersed <br/>Data}}
          )Chapter 11: <br/>Negative Binomial <br/>Regression(
            (Negative Binomial <br/>Outcome Y)
        {{Overdispersed or <br/>Underdispersed <br/>Data}}
          )Chapter 13: <br/>Generalized <br/>Poisson <br/>Regression(
            (Generalized <br/>Poisson <br/>Outcome Y)
        {{Zero Inflated <br/>Data}}
          )Chapter 12: <br/>Zero Inflated <br/>Poisson <br/>Regression(
            (Zero Inflated <br/>Poisson <br/>Outcome Y)
      {{Categorical <br/>Outcome Y}}
        {{Nominal <br/>Outcome Y}}
          )Chapter 14: <br/>Multinomial <br/>Logistic <br/>Regression(
            (Multinomial <br/>Outcome Y)
        {{Ordinal <br/>Outcome Y}}
          )Chapter 15: <br/>Ordinal <br/>Logistic <br/>Regression(
            (Logistic <br/>Distributed <br/>Cumulative Outcome <br/>Probability)

Figure 15.1