IMR and Life Expectancy. From the International Data Base, published by the U.S. Census Bureau, we obtained data on infant mortality rate (IMR) and life expectancy (LE), in years, for a sample of 60 countries. The data are presented on the Weiss Stats CD. For part (d), predict the life expectancy of the country with an IMR of 30.

From the International Data Base, published by the U.S. Census Bureau, we obtained data on infant mortality rate (IMR) and life expectancy (LE), in years, for a sample of 60 countries.

Construct a scatter plot by using MINITAB.

1. Choose Graph > Scatterplot.

2. Choose Connect Line > OK.

3. Under Y-variables, enter column of LE.

4. Under X-variables, enter the colum of IMR.

5. Click OK.

The MINITAB output is:

To find if it is reasonable to find a regression line.

Yes, it is reasonable to find the regression line for the data because the observation are scattered around a line. There are no outlier and an influential observation.

Find the regression equation by using MINITAB procedure.

1. Choose Stat > Regression > Regression.

2. In response enter the column LE.

3. In predictor, enter the columns of IMR.

4. Click OK.

The MINITAB output is:

Regression analyis: LE versus IMR.

From the output, the regression equaton is$LE=79.4-0.354IMR$.

The regression equation denotes that the life expectany is decreased as the IMR decreased.

$$\begin{gathered} LE=79.4-0.354\left(30\right) \\ =79.4-10.62 \\ =68.78years \end{gathered}$$

By using MINITAB procedure, the correlation coefficient is $r=-0.873$.

The value of the correlaion coefficient indicates that there is a strong relation between LE and IMR. And the slope of the regression line is negative because the correlation value is negative.

From MINITAB output in part (A), there are 3 potential outliers:

$$\begin{gathered} IMR=46;LE=49.89 \\ IMR=34;LE=44.28 \\ IMR=44;LE=50.16 \end{gathered}$$

There are 2 influential observation. They are:

$$\begin{gathered} IMR=100;LE=47.43 \\ IMR=91;LE=43.45 \end{gathered}$$