Exercises: Using only the information above (i.e., you are not expected to load the data into RStudio to do any investigations), respond to the following questions. 1. Give the equation of the least square regression line that uses a country's birth rate to predict the life expectancy of its citizens. 2. Use your linear model from part 1. to predict the life expectancy in a country with a birth rate of 36 per 1000 people. 3. Guinea-Bissau has a birth rate of 36 per 1000 people and a life expectancy of 57.8 years. Use the answer in part 2. to find the residual for this data point.
Exercises: Using only the information above (i.e., you are not expected to load the data into RStudio to do any investigations), respond to the following questions. 1. Give the equation of the least square regression line that uses a country's birth rate to predict the life expectancy of its citizens. 2. Use your linear model from part 1. to predict the life expectancy in a country with a birth rate of 36 per 1000 people. 3. Guinea-Bissau has a birth rate of 36 per 1000 people and a life expectancy of 57.8 years. Use the answer in part 2. to find the residual for this data point.
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
Related questions
Question

Transcribed Image Text:Exercises: Using only the information above (i.e., you are not expected to load the
data into RStudio to do any investigations), respond to the following questions.
1. Give the equation of the least square regression line that uses a country's birth rate
to predict the life expectancy of its citizens.
2. Use your linear model from part 1. to predict the life expectancy in a country with a
birth rate of 36 per 1000 people.
3. Guinea-Bissau has a birth rate of 36 per 1000 people and a life expectancy of 57.8
years. Use the answer in part 2. to find the residual for this data point.
4. Is there evidence that birth rate is an effective predictor of life expectancy? Carry
out the calculation of the p-value to make an assessment. (Show your work.)
5. Find the 99% confidence interval for the slope of the true linear model and interpret
the interval.
6. Using the R output above, determine the value of the correlation coefficient. What
does this value tell you about the linear relationship between life expectancy and birth
rate?

Transcribed Image Text:Consider trying to predict the life expectancy of citizens in a country based on the birth
rate (per 1000 people) in that country.
Here is a scatterplot of the data from a random sample of 50 countries.
> ggplot (SampCountries, aes (BirthRate, LifeExpectancy)) +
geom_point (color = "darkred")+
labs (title = "Life Expectancy by Birth Rate")
+
Life Expectancy by Birth Rate
80 -
70 -
10
20
30
40
BirthRate
Here is (some of) the R output for the linear model for this data.
> LifeBirth.lm <-lm (LifeExpectancy~BirthRate, data=SampCountries)
> summary (LifeBirth. lm)
Call:
Im (formula =
LifeExpectancy
BirthRate, data = SampCountries)
Residuals:
Min
10
Median
30
Маx
-7.0592 -2.3047
0.9123
2.5019
5.8358
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 85.26295
1.03301
82.54
BirthRate
-0.63291
0.04492
-14.09
Residual standard error: 3.424 on 48 degrees of freedom
Multiple R-squared:
0.8053,
Adjusted R-squared:
0.8012
F-statistic: 198.5 on 1 and 48 DF,
p-value: < 2.2e-16
LifeExpectancy
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