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    Statistics
  3. Study Set
    Business Statistics
  4. Exam
    Exam 16: Regression Models for Nonlinear Relationships
  5. Question
    For the Log-Log Model Ln(y) = β<Sub>0</sub> + β<Sub>1</sub>ln(x)
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For the Log-Log Model Ln(y) = β0 + β1ln(x)

Question 83

Question 83

Multiple Choice

For the log-log model ln(y) = β0 + β1ln(x) + ε, the predicted value of y is computed as ________.


A) For the log-log model ln(y)  = β<sub>0</sub> + β<sub>1</sub>ln(x)  + ε, the predicted value of y is computed as ________. A)    = b<sub>0</sub> + b<sub>1</sub>x B)    = exp(b<sub>0</sub> + b<sub>1</sub>ln(x)  +   /2)  C)    = b<sub>0</sub> + b<sub>1</sub>ln(x)  D)    = exp(b<sub>0</sub> + b<sub>1</sub>x +   /2) = b0 + b1x
B) For the log-log model ln(y)  = β<sub>0</sub> + β<sub>1</sub>ln(x)  + ε, the predicted value of y is computed as ________. A)    = b<sub>0</sub> + b<sub>1</sub>x B)    = exp(b<sub>0</sub> + b<sub>1</sub>ln(x)  +   /2)  C)    = b<sub>0</sub> + b<sub>1</sub>ln(x)  D)    = exp(b<sub>0</sub> + b<sub>1</sub>x +   /2) = exp(b0 + b1ln(x) + For the log-log model ln(y)  = β<sub>0</sub> + β<sub>1</sub>ln(x)  + ε, the predicted value of y is computed as ________. A)    = b<sub>0</sub> + b<sub>1</sub>x B)    = exp(b<sub>0</sub> + b<sub>1</sub>ln(x)  +   /2)  C)    = b<sub>0</sub> + b<sub>1</sub>ln(x)  D)    = exp(b<sub>0</sub> + b<sub>1</sub>x +   /2) /2)
C) For the log-log model ln(y)  = β<sub>0</sub> + β<sub>1</sub>ln(x)  + ε, the predicted value of y is computed as ________. A)    = b<sub>0</sub> + b<sub>1</sub>x B)    = exp(b<sub>0</sub> + b<sub>1</sub>ln(x)  +   /2)  C)    = b<sub>0</sub> + b<sub>1</sub>ln(x)  D)    = exp(b<sub>0</sub> + b<sub>1</sub>x +   /2) = b0 + b1ln(x)
D) For the log-log model ln(y)  = β<sub>0</sub> + β<sub>1</sub>ln(x)  + ε, the predicted value of y is computed as ________. A)    = b<sub>0</sub> + b<sub>1</sub>x B)    = exp(b<sub>0</sub> + b<sub>1</sub>ln(x)  +   /2)  C)    = b<sub>0</sub> + b<sub>1</sub>ln(x)  D)    = exp(b<sub>0</sub> + b<sub>1</sub>x +   /2) = exp(b0 + b1x + For the log-log model ln(y)  = β<sub>0</sub> + β<sub>1</sub>ln(x)  + ε, the predicted value of y is computed as ________. A)    = b<sub>0</sub> + b<sub>1</sub>x B)    = exp(b<sub>0</sub> + b<sub>1</sub>ln(x)  +   /2)  C)    = b<sub>0</sub> + b<sub>1</sub>ln(x)  D)    = exp(b<sub>0</sub> + b<sub>1</sub>x +   /2) /2)

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