This document discusses log-linear growth models and how to analyze them. It explains that a growth model shows the rate of change in a variable over time and can indicate changes in both magnitude and direction. It also outlines how to convert estimates from the log-linear function back to the original form using transformations. Finally, it discusses evaluating the precision of estimates by using a t-test and defining key terms like error term, standard error, and standard deviation.

1. Log-lin model
or growth model

3. Growth model:
• Growth indicates the magnitude of the rate of
change in the variable under consideration per unit
of time.
• Change in magnitude and also direction
• Note:

4. 4
Growth rate:
Since the estimate of ‘b’ will be in log-linear function, the following
transformation will be used to convert it into the original form:

5. Precision of the estimates:
Significance of the growth rate will be tested by running Student’s t-test as given.
Where:

6. Precision of the estimates:
• Error Term: The variable in a simple or multiple regression
equation that contains unobserved factors that affect the
dependent variable. The error term may also include
measurement errors in the observed dependent or
independent variables.
• Standard error: It represents the average distance that the
observed values deviate from the regression line. In other
words, SE shows how wrong the regression model is on
using the units of the response variable.
• Standard deviation: It shows how much a given data is
spread out around the mean or average.