Use the equation of a linear model to solve problems in the context of bivariate measurement data, interpreting the slope and intercept. For example, in a linear model for a biology experiment, interpret a slope of 1.5 cm/hr as meaning that an additional hour of sunlight each day is associated with an additional 1.5 cm in mature plant height.

Example Problems
Isabella is studying the relationship between daily study hours and exam score. She records data for a random sample of 22 students. Here is computer output from a least-squares regression analysis on her sample:

PredictorCoefSE CoefTP
Constant52.305.0510.360.00
Study hours4.521.054.300.00




Assume that all conditions for inference have been met.

Write the expression for a
99% confidence interval for the slope of the least squares regression line.
Jamal collected data on vehicle engine displacement (in liters) and CO2 emissions (in grams per kilometer) for a random sample of 41 car models. Here is computer output from a least-squares regression analysis on his sample:

PredictorCoefSE CoefTP
Constant74.2022.803.260.00
Displacement68.3011.905.740.00


S = 48.60 R-sq = 69.5%

Assume that all conditions for inference have been met.

Write the expression for a
95% confidence interval for the slope of the least squares regression line.
Aisha is studying the relationship between altitude (in meters) and the boiling point of water (in °C) across 35 towns. Here is computer output from a least-squares regression analysis on her sample:

PredictorCoefSE CoefTP
Constant100.300.60167.170.00
Altitude-0.0180.005-3.600.00




Assume that all conditions for inference have been met.

Write the expression for a
99% confidence interval for the slope of the least squares regression line.
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