1. Warm Up
The table below shows data on the number of live births per 1000 women (aged years) from 1965 to (Hint: enter the year as the years since 1900: 65, 70, …)
Year Births Year Births
1) Find the equation of the LSRL, r and r2.
2) Check to see if a linear model is appropriate. Explain.
3) Estimate the birth rate in 1997.
4) The actual rate in 1997 was What is its residual?

2. Computer Output
Data is often presented in a summary form of a computer program output. For the warm up problem we might get the following:
Dependent variable is: Births
R squared = 67.43%
S = 1.05
Variable Coefficient SE(Coeff) t-ratio p-value
Constant
Year <0.0001
What is the LSRL and r?
What is the predicted number of births in 1972?

3. Practice
Assembly line workers were studied to find a correlation between experience (in months) of the worker and the time (in minutes) to complete an assembly task. The data is summarized below.
Dependent variable is: Assembly time
R squared = 62.0%
S = 9.790
Variable Coefficient SE(Coeff) t-ratio p-value
Constant
Experience
What is the LSRL and r?
What is the predicted assembly time for a worker with 120 months of experience?

4. Bears
The following data on black bear age and weight came from a Canadian study completed in Age Weight (kg) Age Weight (kg) a) Make a scatterplot. b) Find the equation of the LSRL and the correlation r. c) Is there an influential point? Check by removing the outlier and calculating the LSRL and r again.