a) Explain why logistic regression is the appropriate method. b) Fit a logistic regression model to the data. Write out the estimated model. c) Interpret the coefficient for experience, using the odds ratio. d) Use the regression model to estimate the probability of success for someone with 15 months of experience.
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Logistic regression is suitable for binary outcomes (success or failure) and can provide the relationship between the predictor variables and the probability of success. Show more…
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Text: 1. Basic Concepts: Logistic Regression (a) Explain what the three basic GLM components are for a logistic regression and why they are chosen that way. (b) Explain what the coefficients in a logistic regression tell us (i) for a continuous predictor variable and (ii) for an indicator variable. (c) Give the basic definition of an odds ratio and explain how this relates to your answers in part (b). (d) Explain the distinction between calibration and predictive accuracy.
Large values of the log-likelihood statistic in logistic regression models indicate: A) That the statistical model fits the data well. B) That as the predictor variable increases, the likelihood of the outcome occurring decreases. C) That the statistical model is a poor fit to the data. D) That there is a greater number of explained vs unexplained observations.
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