# Conducting a Linear Regression using R

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1. The data for this problem give the infant mortality rate (per 1000 live births) in the United States for the period 1960-1979.

(a) Generate a scatterplot for the data in R. Does the plot make sense?

(b) Find the estimated regression equation using R, and give the units and real-world interpretations of the regression coefficients.

(c) Does there seem to be a strong relationship between time and mortality rate? Explain which number you are using to determine this and why.

(d) Calculate the estimated infant mortality rate in the year 1969, and report a 95% confidence interval for the estimate. Additionally, obtain a 95% prediction interval for the infant mortality rate in that year.

(e) Based on this model, what should the infant mortality rate be in 2007? Explain why this may not be a very accurate prediction.

Data for Problem 1: year <- c(1960,1965,1970:1979)

rate <- c(26.0,24.7,20.0,19.1,18.5,17.7,16.7,16.1,15.2,14.1,13.8,13.0)

mortality <- data.frame(rate,year),

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The solution explains how to conduct a linear regression for a problem involving infant mortality rate in the U.S. using R.

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