Table 1- Regressions with a Binary Dependent Variable Dependent Variable, YI or
ID: 3360873 • Letter: T
Question
Table 1- Regressions with a Binary Dependent Variable Dependent Variable, YI or 0) Linear Probability Probit Logit Independent Variable Xi (continuous) 0.2402 (0.110) 0.472 (0.382) 2.120 (2.011) X2 (Binary) ( 1 Yes, 0 = No) 0.315 (0.100) 0.659 (0.321) (0.081) 0.122 (0.004) 1.935 (0.280) 3.999 (0.331) Constant 1. Use the linear probability regression output (column (1)) to answer this question: What is the estimated effect of a 2.6 increase in Xii on the likelihood that Y1? 2. Do any of the models estimate a statistically significant effect of Xi on the probability that Y1? If yes, which one(s). If no, just write "no." 3. Use the probit regression output (column (2)) to answer this question: When X4 d X1 what is the estimated probability that Y'= 1?Explanation / Answer
Answer to question# 1)
In linear probability regression output model , We got :
Coefficient of x1 = 0.2402
Thus if X1 is increased by 2.6 , the estimated increase is :
Estimated increase = 0.2402 * 2.6 = 0.62452
Thus the likely probability of Y to be 1 is 0.62452
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