1. Enter the estimated intercept and slope for the simple linear regression mode
ID: 3229227 • Letter: 1
Question
1. Enter the estimated intercept and slope for the simple linear regression model below:
LifeExp = _____+ _______ * YrsSchool
2. The 95% CI for the population slope is to
3. The linear correlation coefficient for these two variables is
4. About % of the variation in LifeExp can be explained by variation in YrsSchool.
5. From our data set we can see that, in Brazil, adults have had on average 6.6 years of schooling. According to our regression model, what is the predicted life expectancy for a country in which adults have had, on average, 6.6 years of schooling?
6. Calculate the residual for Brazil by comparing this predicted life expectancy to Brazil's observed life expectancy.
7. Brazil's observed life expectancy is ---Select--- greater than the reciprocal of less than extrapolated from minimized with respect to perfectly correlated with equal to what we predict with our regression model.
8. (a) According to our model, what is the predicted life expectancy for a country in which adults have had, on average, 12 years of schooling?
(b) We can be 95% confident that the average life expectancy for all countries in which adults have had, on average, 12 years of schooling is between and
(c) We can be 95% confident that the life expectancy for a single country not in our dataset in which adults have had, on average, 12 years of schooling will be between and
9. Suppose we were to draw some line other than the line of best fit through our data. Which of the following do we know will be true? Select all that apply. (It's possible to have more than 1 answer)
The sum of squared residuals would less than 3211.9556
The sum of squared residuals would greater than 3211.9556
The sum of squared residuals would no longer be minimized
The sum of squared residuals would less than 1791.048T
he sum of the squared residuals would no longer be maximized
The sum of squared residuals would be greater than 1791.048
Explanation / Answer
Correlation Coefficient between Gii & YrsSchool = -0.8584
Because the scatter plot for Gii vs YrsSchool is inversely proportional and the points are not much scattered and it lies towards center.
Correlation Coefficient between EduIndex & YrsSchool = 0.9804
Because the scatter plot for EduIndex and YrsSchool is directly proportional and almost allpoints lie togthere in a straight line
Correlation Coefficient between LifeExp & YrsSchool = 0.0001
Because the scatter plot for LifeExp and YrsSchool is upward slopping but the points are completely scattered and it is clustered to one end. Therefore it has poor correlation
Correlation Coefficient between GDP & YrsSchool = 0.6651
Because the scatter plot for GDP and YrsSchool is directly proportional and the points doesn't lie on the center. Therefore it is positively correlated with 0.6651.
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