Corporations with international operations need to assess the risks associated with setting up and maintaining operations in different regions of the world. Consideration of the risks include considering such issues as political and economic stability. One indicator of the healthcare and quality of life in a country or region that is considered correlated with the risk and stability in the region is the child mortality rate. As a result, the healthcare and quality of care as measured by the child mortality rate in a region can impact the type and amount of investment in a region and countries within a region. The child mortality rate is the number of children 5 and under that die per thousand people in the population. The Inter-agency Group for Child Mortality Estimation (IGME) was obtained from www.childmortality.org. In 2008 the US rate was 7.6 deaths per thousand and in North America (US and Canada) the rate was 6.65 with a standard deviation of 0.68. The Excel file for this assignment has labels for the year, country name, and continent group.

NAME OF FIELD	DESCRIPTION
Country	Name of the countries
Continent / Region	Countries were categories based on region into 8 continents. The coding scheme was 1= South-East Asia, 2 = South Asia, 3 = Western Europe, 4 = Eastern Europe, 5 = Africa, 6 = South America and Islands, 7 = Oceania with Australia and New Zealand, 8 = Middle East, 9 = North America
CMR1998	child mortality rate for 1998
CMR2008	child mortality rate for 2008

Use the provided random sample of observations to test the hypotheses that

(i) the mean child mortality rate for countries in Africa is more than the mean Child mortality rate for countries in South Asia. (a = .05),

(ii) the mean child mortality rate for countries in Eastern Europe is more than the mean

Child mortality rate for countries in the Middle East. (a = .01),

(iii) the mean child mortality rate for countries in South East Asia is more than the mean Child mortality rate for countries in Western Europe. (a = .05) by 10 per thousand.

Instruction using Excel

1. You will need to have the data file for this assignment (Assgt#1.xls) copied onto a disk or flash drive. These files reside on the course web site (Campus Connect) on the Resource page that you can find by clicking on the Excel files link near the top of the page.

2. Click on the file to download and allow about a half minute for the downloading to be completed.

3. Start with Microsoft Excel 2016 or other edition.

4. Click on File Open, click on Look In, then on the down arrow. Choose the address where the data file resides and select Assgt#1.xls

5. Highlight the all data and Click on Solt&Filter button in Editing ribbon. Choose the Custom Sort and Sort by Continent (region). The data is shown below before and after the sort

Original data

Country	Region	CMR1998	CMR2008
Afghanistan	2	257	257
Algeria	5	49.4	37
Angola	5	213.9	158
Argentina	6	23.6	16.2
Australia	7	6.6	5.7
Bahrain	8	14.1	10.2
Belarus	4	19.8	13.4

Sorted data

Country	Region	CMR1998	CMR2008
Brunei	1	8.92	8.8
Indonesia	1	58.8	31.2
Korea, Dem. Rep.	1	55	55

6. Click on cross button (create new spreadsheet) in the left-bottom corner area and rename a new spread sheet with a related title (e.g., Africa-Asia 2008)

7. Copy the net child mortality rates in 2008 for the sample of countries in Africa (Continent = 5) and paste these values into a new column, beginning at the second row. Put a title (label) at the top of this new column to indicate what the values represent (e.g., Africa-CMR2008). Next, copy the net child mortality rates in 2008 for the sample of countries in the South Asia (Continent = 2) and paste these values in the column adjacent to the first new column. Put a title (label) at the top of the newest column to indicate what these values represent (e.g., SoAsia-CMR2008).Please note that in these two new columns you should have numbers representing child mortality rates per 1000 and NOT the 5s or 2s! The table below shows what the first lines in your two new columns should resemble.

Africa2008	So-Asia2008
37	257
158	84

8. Select the Data tab then Click on Data, then on descriptive statistics and check the ‘summary statistics’ box. Examine that data and consider its meaning.

Africa2008		So-Asia2008
Mean	137.175	Mean	105.0175
Standard Error
Median
Mode
Standard Deviation
Sample Variance	2748.53671	Sample Variance	11411.13
Kurtosis
Skewness
Range
Minimum
Maximum
Sum
Count	20	Count	4

9. Once the descriptive statistics are examined, repeat the steps to get to Excel Data Analysis macros and select the t-test: Two-Sample Assuming Unequal Variances. Then set the cursor in the Variable 1 box, and then highlight the column with Africa-CMR2008 data. Repeat with the Variable 2 box and the SoAsia-CMR2008 data.

10. Click in the labels box to put a check mark, and take the default .05 significance level.

11. Enter 0 (the number zero) in the box for hypothesized mean difference.

12. Click on the Output Range radio button then set the cursor in the box beside it and choose any convenient 13 row x 4 column space in your current Excel spreadsheet. Click on O.K.

13. Excel will then complete the independent two-samples t-test and place the output in the space that you named. Expand the column widths if necessary to reveal all of the output without truncation. Your output might resemble the truncated version of the output shown below:

t-Test: Two-Sample Assuming Unequal Variances
	Africa2008	So-Asia2008
Mean	137.175	105.0175
Variance	2748.53671	11411.12789
Observations	20	4
Hypothesized Mean Difference
df
t Stat
P(T<=t) one-tail
t Critical one-tail
P(T<=t) two-tail
t Critical two-tail

13. Repeat steps 6, 7 and 9-13 but with CMR data for countries in the Eastern European Region (4) and the Middle East (8). In Step 8 you will need to select the t-test for "Equal Variances". Make a separate pair of new columns with an appropriate title at the top of each column and be sure to change the a level to .01 but use the same hypothesized mean difference of 0. Below is a truncated illustration of the test output.

t-Test: Two-Sample Assuming Equal Variances
	Eastern European	Middle East
Mean	15.8375	15.71428571
Variance	119.382679	80.18809524
Observations	8	7
Pooled Variance
Hypothesized Mean Difference
df

t Stat
P(T<=t) one-tail
t Critical one-tail
P(T<=t) two-tail
t Critical two-tail

14. Repeat steps 6-8 and 10-12 but with CMR data for countries in the South East Asia and the Western European Region. In Step 9 you will need to select the t-test for "Unequal Variances". Make a separate pair of new columns with an appropriate title at the top of each column and be sure to change the a level to .05 and the hypothesized mean difference to 10. Below is a truncated illustration of how the test output might look.

t-Test: Two-Sample Assuming Unequal Variances
	So E Asia	Western Europe
Mean	35.95	5.775
Variance	1432.551	42.26568
Observations	6	12
Hypothesized Mean Difference
df
t Stat
P(T<=t) one-tail
t Critical one-tail
P(T<=t) two-tail
t Critical two-tail

15. Print out the data and output for each of your three t-tests. You might want to answer the questions by typing on the Excel Spreadsheet before printing. Alternatively you can copy and paste the output in the Excel window into a word processor (e.g., Microsoft Word) and type your answers there. Whatever method you choose, you should understand the link between each answer and the relevant Excel output to which it refers.

16. Conduct the same analysis with SPSS data and print out the output each of your first two t-tests.

(i) the mean child mortality rate for countries in Africa is more than the mean Child mortality rate for countries in South Asia. (a = .05),

(ii) the mean child mortality rate for countries in Eastern Europe is more than the mean Child mortality rate for countries in the Middle East. (a = .01),

For this assignment you should print the following on your spreadsheet.

Below each of the three t-test outputs, the null and alternative hypotheses, a decision, giving the reason, and a conclusion based on the output. For example

Null Hypothesis,

Ho: the mean child mortality rate for countries in Africa is the mean child mortality rate for countries in South Asia.

Alternative Hypothesis,

Ha: the mean child mortality rate for countries in Africa is the mean child mortality rate for countries in South Asia.

Decision:

Fail to reject (or reject - as your results determine) Ho because the p-value (xxxx) associated with the test statistic is ____ than the significance level (0.05).

Conclusion: there is evidence (or insufficient evidence - as your results determine) that the mean child mortality rate for countries in Africa is ____ the mean child mortality rate for countries in South Asia.

You are expected to use your output and written answers to complete the quiz. You are required to turn in the output.

We also want you to work in groups of 2-3 to discuss each of the following questions.

Questions

1. What is the critical value for the t-test to compare the mean child mortality rate for countries in Africa with the mean child mortality rate for countries in South Asia?

A. 2.35 B. 0.05 C. 0 D. 3.18 E. 0.588

2. What is the critical value for the t-test to determine if the mean child mortality rate for countries in Eastern Europe is more than the mean Child mortality rate for countries in the Middle East?

A. 2.65 B. 0.05 C. 3.01 D. 0 E. 0.024

3. The hypothesized mean difference to conduct the test to determine if the mean child mortality rate for countries in Eastern Europe is more than the mean Child mortality rate for countries in the Middle East?

A. 2.65 B. 0.05 C. 3.01 D. 0 E. 0.024

4. The decision rule for the t-test to compare the mean child mortality rate for countries in Africa with the mean child mortality rate for countries in South Asia

A. Reject Ho, if the p-value is less than .05.

B. F.T.R. Ho, if the p-value is less than .05.

C. Reject Ho, if the p-value is greater than .05.

D. Reject Ha, if the p-value is greater than .05.

E. F.T.R. Ho, if the p-value is equal to .05.

5. What is the conclusion for t-test concerning if the mean child mortality rate for countries in South East Asia is more than the mean Child mortality rate for countries in Western Europe by more than 10 per thousand?

A. There is evidence that the mean child mortality rate for countries in South East Asia is more than the mean Child mortality rate for countries Western Europe by more than 10 per thousand.

B. There is insufficient evidence that the mean child mortality rate for countries in South East Asia is more than the mean Child mortality rate for countries Western Europe by more than 10 per thousand.

C. There is insufficient evidence that the mean child mortality rate for countries in South East Asia is equal to Child mortality rate for countries Western Europe by more than 10 per thousand.

D. There is conclusive evidence that the mean child mortality rate for countries in South East Asia is more than the mean Child mortality rate for countries Western Europe by more than 10 per thousand.

Country	Region	CMR1998	CMR2008
Afghanistan	2	257	257
Algeria	5	49.4	37
Angola	5	213.9	158
Argentina	6	23.6	16.2
Australia	7	6.6	5.7
Bahrain	8	14.1	10.2
Belarus	4	19.8	13.4
Belize	6	29.9	25.4
Benin	5	154.3	123.4
Bhutan	2	117.2	84
Bolivia	6	96.6	57.4
British Virgin Islands	6	26.6	16
Brunei	1	8.92	8.8
Burkina Faso	5	191.96	190.7
Canada	9	6.5	5.7
Chad	5	203.1	209
Congo, Dem. Rep.	5	186.8	161.4
Congo, Rep.	5	112.2	125.4
Costa Rica	6	15.2	11.5
Cote d'Ivoire	5	140.4	126.5
Cuba	6	9.7	6.5
Denmark	3	5.9	4.4
Dominica	6	16.8	11.4
Dominican Rep.	6	39.7	37.6
Egypt	5	58.8	36.2
Equatorial Guinea	5	192.2	206
Estonia	3	13.5	5.6
France	3	5.9	4.3
Gabon	5	91	91
Gambia	5	142.5	108.6
Germany	3	5.9	4.4
Ghana	5	110.5	115.2
Guatemala	6	59.6	39
Guinea	5	202.6	150.3
Guinea-Bissau	5	227.4	198
Guyana	6	75.4	60.2
Honduras	6	46.5	23.9
Iceland	3	4.8	2.5
India	2	102.3	72.07
Indonesia	1	58.8	31.2
Ireland	3	7.5	4.2
Israel	8	7.8	5
Italy	3	6.6	3.7
Kiribati	7	74.2	63
Korea, Dem. Rep.	1	55	55
Latvia	4	17.5	8.6
Lebanon	8	33	29.4
Libya	8	25.6	17.8
Liechtenstein	3	7.2	2.5
Luxembourg	3	4.4	2.8
Macedonia, FYR	4	22	16.6
Mali	5	227.44	195.6
Mexico	6	42.07	34.72
Micronesia, Fed. Sts.	7	49.7	40.2
Moldova	4	27.5	18.2
Montserrat	6	13.08	9.2
Myanmar	1	114.2	103
New Zealand	7	8	5.8
Nigeria	5	220.8	188.8
Poland	4	11.8	6.8
Portugal	3	8.9	3.8
Qatar	8	20.52	14.9
Russia	4	26.3	14.5
Saint Kitts and Nevis	6	28	18
Samoa	7	38.16	27
Saudi Arabia	8	32	24.8
Seychelles	7	15.4	12.8
Singapore	1	4.9	2.7
Slovak Republic	4	10.8	7.8
Somalia	5	174.9	141.5
Suriname	6	43.2	28.5
Swaziland	5	123	90.9
Switzerland	3	6	4.9
Thailand	2	16.5	7
Timor-Leste	7	144.7	96.8
Tonga	7	28.14	23.1
Turkey	3	54.4	26.2
United Arab Emirates	8	11.5	7.9
Uzbekistan	4	71.1	40.8
Vanuatu	7	49.2	34.1
Venezuela	6	26.1	19.3
Vietnam	1	39.1	15
Zimbabwe	5	125.4	90