One-Way ANOVA Exploring World Fertility Rates
One-way ANOVA can be thought of in two different ways. First ANOVA
extends testing sample means beyond the two sample mean situation used
for t-tests. Second one-way ANOVA is the simplest statistical procedure
for more complicated experimental designs. The logic behind ANOVA
is that we are partitioning variation into variation explained by differences
in samples means and variation not explained by differences in sample
means or what we call error. Once the variance for each type of variation
is computed, we can test to see if these variances are equal to each other
using the F-distribution.
How do fertility rates compare with other regions of the country?
Go to the United
Nations Cyber School Bus web site. Choose INFONATION to go to
the UN database with over 30 different fields of information on 185 different
|Goal of Data Analysis Lab:
Using ANOVA, determine if there is a difference in fertility rates
for four different regions of the world.
Choose four regions of the world from the six regions specificed by the
UN database. One of these regions must be North America. Within
each of these four regions randomly select ten countries (okay if one or
two have missing information) to investigate. Included is a copy of the
all the six regions of the world and all countries in each regions.
In the North American region, one of the countries selected must be the
United States since you are studying how your country relates to other
countries in the world for fertility. For each of these countries
choose three data fields: total fertility rate, GDP Per Capita and
Threatened Species as shown in the printscreen for the INFONATION database.
For the ANOVA data analysis lab you are going to only use the numerical
variable total fertility rate but for the next data analysis lab using
linear regression we are going to use GDP Per Capita and Threatened Species
for the same countries. On a piece of data create five columns to
input to SPSS, one for each variable. The first column is the country
name, the second is a code of 1-4 for each region of the world each country
belongs, the third is the fertility rate, the fourth is GDP Per Capita
and the fifth is the number of threatened species.
Using the One-way ANOVA, test the research question that the mean fertility
rate is different for the four different regions of the world. Use
Tukey's multiple comparison test to determine which regions of the world
are different from each with an alpha of .05.
What are the test assumptions for ANOVA? Have these assumptions been met
for this example?
State what SSB, SSW and SSTotal actually represent in the context of this
data seet. Why do we use a F distribution to test means in ANOVA
rather than a t distribution?
Write an APA results paragraph explaining the inferential test you just
How does fertility rate affect the economy of a country?
Is the fertility rate of a man different than a woman?