1. *This denotes that there were respondents in the data who did not respond and the inference procedure
was performed without the respondents accounted for.
July5, 2016
To: GrameenAustraliaPhilippines
From: AlexandraNolan;EducationandCapacityBuildingIntern
RE: Analysisof Rise inHouseholdIncome of Centers
1. Project Description:This report will show the impact on household income of
beneficiaries in the past 6 months that Grameen Australia Philippines has made via
microfinance loans.
The first tests which will be run are correlations and linear regression analyses,
and the results will determine if any statistically significant augmentation in average
household income of each center exists. These tests will also determine what specific
variables contribute to the augmentation, should one exist.
The second test will be a chi-squared test for goodness of fit, which will
determine if there is a difference in the success rates of the loans in augmenting
household income amongst the centers. If there is a difference, this test will show which
center(s) is (are) yielding higher results and which is (are) yielding a lower income
augmentation.
The final tests will be a T test for means and a T confidence interval for means,
which will determine if the change in average household income of the beneficiaries is
different from the national average change in household income. It will also determine
if the average household income of the beneficiaries is growing at a faster or slower rate
than the national average, if in fact there is a difference. This will provide a wider scope
of analysis of Grameen Australia Philippines’ impact.
2. Recommendations
2.1.1 Question1: Are the loans of Grameen Australia Philippines providing a statistically
significant augmentation in household income in the centers?
2.1.2 Exploratory DataAnalysis:
To precisely and accurately answer the proposed research question, it is important to
analyze the descriptive statistics, along with the visual of a box plot comparison. Both box plots
are skewed right, however the “Average Income After 6 Months” is much higher on the scale
than the box plot of “Average Baseline Income”. Perhaps the most important indication of the
augmentation in average household income of beneficiaries is the values from the descriptive
statistics in comparison to each other. The “Average Baseline Income” box plot has a MEDIAN
income of just 12,200 PHP, whereas 12,000 PHP is the MINIMUM value of “Average Household
Income After 6 Months”. This means the previous average amount that the beneficiary could
earn, has become the minimum amount of household income a beneficiary could earn; thus
representing the overall shift from one equilibria to the next. The maximum value of average

2. *This denotes that there were respondents in the data who did not respond and the inference procedure
was performed without the respondents accounted for.
household income also augmented from 27,418 PHP to 32,977 PHP in just 6 months. That is an
impressive growth rate of 20%. Based on this information, we may predict that the inference
procedures of correlation and regression will be statistically significant.
Descriptive Statistics: Average Baseline Income, Average Income After 6 Months
Variable N N* Mean SE Mean StDev Minimum Q1 Median Q3
Average Baseline Income 5 0 14769 3734 8349 7467 7800 12200 23022
Average Income After 6 M 5 0 19043 4098 9164 12000 12485 13391 28426
Variable Maximum
Average Baseline Income 27418
Average Income After 6 M 32977
2.1.3 Inference Procedures:
To precisely and accurately answer the research question, the correlation tests as well
as linear regression analyses were run. Part A consists of tests run on Average Household
Incomes and Average PHP of donations, whereas Part B explores the Overall Household
Incomes as well as the Total Amount of PHP loaned. For these tests, the alpha level of
significance will be 0.05, or 5%. The “moderately significant” alpha level of significance will be

3. *This denotes that there were respondents in the data who did not respond and the inference procedure
was performed without the respondents accounted for.
0.10, or 10%. If the P-Value, which stands for probability, is less than these alpha values, it is
significant data.
For the correlation analyses, a strong correlation is one that is closer to the absolute
value of 1. A weak correlation is closer to 0. A strong correlation will be defined as |0.9| or
|0.8|. A fairly strong correlation will be defined as |0.7| or |0.6|. A medium strength
correlation will be defined as |0.5|, and a weak correlation will be defined as |0.4| or |0.3|.
Part A: Averages
Correlation: Average Baseline Income, Average Income After 6 Months
Pearson correlation of Average Baseline Income and Average Income After 6 Months =
0.984
P-Value = 0.002
Correlation: Avg PHP/Person Loaned Cycle 1, Average Income After 6 Months
Pearson correlation of Avg PHP/Person Loaned Cycle 1 and Average Income After 6 Months
=
-0.037
P-Value = 0.952
Above are the results of the correlation analyses ran on average incomes.
The first test shows the strength of the correlation between the variables “Average
Baseline Income” and “Average Income After 6 Months”. The Null Hypothesis here would be
that there is no correlation between the two variables. The Alternative Hypothesis would be
that there is a correlation between the two variables. Based on the results, the P-Value is 0.002,
which is much less than the 0.05 alpha significance level. The correlation is a positive direction
of 0.984, which is very close to 1 and therefore very strong. Thus, we may conclude that the
data is significant. We may reject the Null hypothesis in favor of the Alternative hypothesis.
There is a strong correlation between “Average Baseline Income” and “Average Income After 6
Months”. In other words, the “Average Baseline Income” heavily determines the “Average
Income After 6 Months”.
The second test shows the strength of the correlation between the variables “Average
PHP/Person Loaned in Cycle 1” and “Average Income After 6 Months”. The Null Hypothesis
here would be that there is no correlation between the two variables. The Alternative
Hypothesis would be that there is a correlation between the two variables. Based on the
results, the P-Value is 0.952, which is much higher than both the 0.10 and the 0.05 alpha
significance levels. The correlation is in the negative direction, with a very weak correlation of
0.037. Thus we may conclude that the data is insignificant. We may not reject the null
hypothesis that there is no correlation. There is no statistically significant correlation between
the two variables, suggesting that the “Average PHP/Person Loaned in Cycle 1” does not have a

4. *This denotes that there were respondents in the data who did not respond and the inference procedure
was performed without the respondents accounted for.
statistically significant impact on “Average Income After 6 Months”. However, there may be a
weak negative correlation which has potential to grow stronger.
Regression Analysis: Average Income A versus Avg PHP/Person L, Average
Baseline
Analysis of Variance
Source DF Adj SS Adj MS F-Value P-Value
Regression 2 327834045 163917023 40.40 0.024
Avg PHP/Person Loaned Cycle 1 1 2592347 2592347 0.64 0.508
Average Baseline Income 1 327366012 327366012 80.69 0.012
Error 2 8113836 4056918
Total 4 335947882
Model Summary
S R-sq R-sq(adj) R-sq(pred)
2014.18 97.58% 95.17% 87.35%
Coefficients
Term Coef SE Coef T-Value P-Value VIF
Constant -386 4787 -0.08 0.943
Avg PHP/Person Loaned Cycle 1 0.541 0.677 0.80 0.508 1.02
Average Baseline Income 1.092 0.122 8.98 0.012 1.02
Regression Equation
Average Income After 6 Months = -386 + 0.541 Avg PHP/Person Loaned Cycle 1
+ 1.092 Average Baseline Income
Above is the linear regression analysis result of average income values. This test
determines the “Y” dependent variable of “Average Income After 6 Months”. The independent
variables used to determine the Y variable are “Average PHP/Person Loaned in Cycle 1” and
“Average Baseline Income”. As represented above, the only significant P-Value is that of
“Average Baseline Income”, which would mean that is the only independent variable which
significantly contributes to the outcome of “Average Income After 6 Months.”
However, as represented by the equation we can see that the “Average PHP/Person
Loaned in Cycle 1” does augment the “Average Income After 6 Months”; just not at a
statistically significant level. In other words, for every 1 additional PHP loaned, the beneficiary’s
“Average Income After 6 Months” will increase by 0.541. For every 1 additional PHP the
beneficiary already had in their “Average Baseline Income”, their “Average Income After 6
Months” will increase by 1.092. Therefore, we may conclude that “Average Baseline Income”
plays a larger role in determining “Average Income After 6 Months” than “Average PHP/Person
Loaned in Cycle 1”…however both independent variables do positively contribute to the
dependent variable of “Average Income After 6 Months”.

5. *This denotes that there were respondents in the data who did not respond and the inference procedure
was performed without the respondents accounted for.
Part B: Overall
Correlation: Overall Income After 6 Months, Overall Baseline Income
Pearson correlation of Overall Income After 6 Months and Overall Baseline Income =
0.983
P-Value = 0.003
Correlation: Total PHP Loaned Cycle 1, Overall Income After 6 Months
Pearson correlation of Total PHP Loaned Cycle 1 and Overall Income After 6 Months =
0.496
P-Value = 0.395
Above are the results of the correlation analyses on overall incomes.
The first test results show the correlation between the two variables “Overall Baseline
Income” and “Overall Income After 6 Months”. The Null Hypothesis here would be that there is
no correlation between the two variables. The Alternative Hypothesis would be that there is a
correlation between the two variables. The P-Value here is 0.003, which is much less than the
0.05 alpha significance level. The correlation value is a positive 0.983, which is very close to the
value 1. Based on these results, we may conclude that the data is significant. We may reject the
Null Hypothesis in favor of the Alternative hypothesis. There is a strong correlation between the
two variables, meaning that the “Overall Income After 6 Months” is very dependent on the
“Overall Baseline Income”.
The second test results show the correlation between the variables “Total PHP Loaned in
Cycle 1” and “Overall Income After 6 Months”. The Null Hypothesis here would be that there is
no correlation between the two variables. The Alternative Hypothesis would be that there is a
correlation between the two variables. The P-Value is 0.395, which is much higher than both
the 0.10 and 0.05 alpha levels of significance. The correlation is a positive direction with a value
of 0.496, which is a weak correlation. Based on these results, the data is not significant. This
means we may not reject the null hypothesis. There is no statistically significant correlation
between the two variables, meaning there is no significant impact on “Overall Income After 6
Months” from “Total PHP Loaned in Cycle 1”. However, there may be a weak positive
correlation which has potential to grow stronger.
Regression Analysis: Overall Income A versus Overall Baseline, Total PHP Loaned
Analysis of Variance
Source DF Adj SS Adj MS F-Value P-Value
Regression 2 2.58244E+11 1.29122E+11 150.02 0.007
Overall Baseline Income 1 1.94162E+11 1.94162E+11 225.58 0.004
Total PHP Loaned Cycle 1 1 7087282331 7087282331 8.23 0.103
Error 2 1721418732 860709366

6. *This denotes that there were respondents in the data who did not respond and the inference procedure
was performed without the respondents accounted for.
Total 4 2.59965E+11
Model Summary
S R-sq R-sq(adj) R-sq(pred)
29337.8 99.34% 98.68% 52.43%
Coefficients
Term Coef SE Coef T-Value P-Value VIF
Constant -10602 25443 -0.42 0.717
Overall Baseline Income 1.1110 0.0740 15.02 0.004 1.14
Total PHP Loaned Cycle 1 0.515 0.179 2.87 0.103 1.14
Regression Equation
Overall Income After 6 Months = -10602 + 1.1110 Overall Baseline Income
+ 0.515 Total PHP Loaned Cycle 1
Above are the results of the regression analysis on overall values. This test determines
the result of the “Y” value of the dependent variable “Overall Income After 6 Months”. The
independent variables used to determine this “Y” dependent variable are “Overall Baseline
Income” and “Total PHP Loaned in Cycle 1”. Here we see that the only significant P-Value is that
of the “Overall Baseline Income”, suggesting it is the only statistically significant variable which
contributes to the dependent variable value. Despite this, both independent variables
contribute positively to the “Overall Income After 6 Months”. When analyzing the regression
equation, we see that for every 1 additional PHP loaned, the “Overall Income After 6 Months”
increases by 0.515. For every additional PHP of “Overall Baseline Income”, the “Overall Income
After 6 Months” increases by 1.11. Both variables have the potential to significantly increase
“Overall Income” in the future.
2.1.4 Summary:
While the loans of Grameen Australia Philippines in the first 6-month cycle have not
directly impacted the augmentation of average household income or overall income of a center
in a statistically significant value, the loans of Grameen Australia Philippines in the first 6-month
cycle have contributed at a statistically insignificant value to the augmentation of average
household income as well as overall center income; with the potential to grow into a
statistically significant impact in income augmentation. Based on the result that baseline
incomes are very highly correlated to incomes after 6 months, every small augmentation in
income that Grameen Australia Philippines contributes to will have a ripple effect:

7. *This denotes that there were respondents in the data who did not respond and the inference procedure
was performed without the respondents accounted for.
1) Grameen Australia Philippines loans
2) augment baseline income after 6 months by a small value-
3) new, larger baseline income to contribute to the next 6 months at a significant value
level. 
4) Eventually, with enough funds from donors and growth amongst PHP values in loans
and members of Grameen Australia Philippines, the loans will provide a statistically
significant impact on growth in incomes of the beneficiaries.
Therefore, we may conclude that currently the loans of Grameen Australia Philippines are in
fact contributing to growth of household incomes of the beneficiaries, just not at a statistically
significant level as of yet. The incomes certainly grew, as we can see in the exploratory data
analysis section. Based on the regression analyses, we certainly see the loans as an important
factor to the positive contribution to incomes. We may safely predict that will additional
members and additional donor funds, the loans of Grameen Australia Philippines will very soon
reach a statistically significant level of impact on household income augmentation of
beneficiaries.
2.2.1 Question2: Is there a statistically significant difference in the average household
income and/ or the overall income change amongst centers which received loans from GAP?
2.2.2 Exploratory DataAnalysis:
To accurately predict the outcomes of inference procedures which will be run, it is important
to analyze the income growth in PHP of the centers compared to each other in a bar graph
format. As we can see based on the graphs of observed values, the centers all grew in both
average and overall incomes. The growth in average incomes (per member) are greater than
the overall incomes (per center), although Bagbag and Taytay exceled in both scenarios. It
seems Payatas grew the least in each category. Based on these graphs, there appears to be a
significant difference in growth amongst the centers in both average incomes and overall
incomes. There could be many factors contributing to the differences in growth. The
differences could be due to baseline income amount, internal challenges a center faces,
external challenges, or due to the amount of aid the center received. The apparent difference
in growth amongst the centers will be statistically tested in the next section.

8. *This denotes that there were respondents in the data who did not respond and the inference procedure
was performed without the respondents accounted for.
A) Average Incomes

9. *This denotes that there were respondents in the data who did not respond and the inference procedure
was performed without the respondents accounted for.
B) Overall Incomes
2.2.3
A) Inference Procedure: Chi- Squared Tests for Goodness of Fit
To more precisely and accurately answer the second research question, two separate
Chi-Squared Tests for Goodness of Fit will be run. From the results of the inference procedures
carried out in section 1, we know that the centers are growing in household incomes. These
tests are used to determine if the centers are growing at an equal rate. If the centers are
growing at an unequal rate, the tests will also tell us which centers are doing better and which
are doing worse. The alpha levels of significance will remain the same, with 0.10 being the
“moderately significant” level, and the 0.05 at the “very significant” level. The variable used in
the first Chi-Squared Test was the “Change in Average Income PHP” variable. The variable used
in the second Chi-Squared Test was the “Change in Overall Income PHP” variable.

10. *This denotes that there were respondents in the data who did not respond and the inference procedure
was performed without the respondents accounted for.
Part A: Averages (Incomes Per Member)
Chi-Square Goodness-of-Fit Test for Observed Counts in Variable: Change in
Average Income PHP
Using category names in Center Name
Test Contribution
Category Observed Proportion Expected to Chi-Sq
Taytay 4836.6 0.2 4274.04 74.05
Caloocan 5250.0 0.2 4274.04 222.86
Payatas 1191.2 0.2 4274.04 2223.63
Montalban 4533.3 0.2 4274.04 15.73
Bagbag 5559.1 0.2 4274.04 386.37
N DF Chi-Sq P-Value
21370.2 4 2922.64 0.000

11. *This denotes that there were respondents in the data who did not respond and the inference procedure
was performed without the respondents accounted for.
Above are the results of the Chi-Squared Test for Goodness of Fit run on the variable
“Change in Average Income PHP”.
The expected values would all be equal dividends of 4,274.04 PHP raised of average
income of a center. The Null hypothesis here would be that all the means are equal, in other
words, μ1=μ2=μ3=μ4=μ5. The Alternative hypothesis here would be that the mean growth in
average incomes are not all equal; at least one, or all of them are not different. In other words,
μ1≠μ2≠μ3≠μ4≠μ5. The P-Value was 0.00, which is very highly significant. This means we may
reject the Null hypothesis in favor of the Alternative hypothesis and say that the average
growth in income in PHP is not equal amongst the centers. Some centers are growing at a faster
rate than others.
More specifically, we can see that 4/5 centers had observed values larger than the
expected values, meaning they grew more than expected. That’s great news! The only center
which grew much less than expected was Payatas, with only a 1,191.20 PHP raise in average
income. The centers which grew more than expected are listed here in order from the most
growth in average income after 6 months to the least growth in average income after 6 months:
1) Bagbag
2) Caloocan
3) Taytay
4) Montalban
The fact that the centers grew at different rates in average incomes may be due to a
variety of factors such as loan amounts, participation by members (both qualitative and
quantitative), location, or social business type.

12. *This denotes that there were respondents in the data who did not respond and the inference procedure
was performed without the respondents accounted for.
Part B: Overall (Income of Entire Center)
Chi-Square Goodness-of-Fit Test for Observed Counts in Variable: Change in
Overall Income PHP
Using category names in Center Name
Test Contribution
Category Observed Proportion Expected to Chi-Sq
Taytay 154450 0.2 73261 89974.9
Caloocan 42000 0.2 73261 13339.3
Payatas 20250 0.2 73261 38358.3
Montalban 27200 0.2 73261 28959.7
Bagbag 122405 0.2 73261 32966.1
N DF Chi-Sq P-Value
366305 4 203598 0.000

13. *This denotes that there were respondents in the data who did not respond and the inference procedure
was performed without the respondents accounted for.
Above are the results of the Chi-Square Test for Goodness of Fit run on the variable
“Overall Change in Income PHP”.
The expected values of equality are all set at 73,261 PHP in overall income
augmentation per center. The Null hypothesis would be that the means of overall income
augmentation of the centers are all equal, in other words μ1=μ2=μ3=μ4=μ5. The Alternative
hypothesis would be that the means of the overall income augmentation of the centers are not
equal; there are one or more differences amongst them. In other words, μ1≠μ2≠μ3≠μ4≠μ5. The
P-Value here is 0.00 which is very significant. This means that the means are not equal amongst
the centers. Some centers are growing faster than others in terms of overall income
augmentation of the center.
More specifically, in terms of overall income augmentation of the center, 3/5 centers
have observed values lower than the expected values. This means these 3 centers, namely
Payatas, Montalban, and Caloocan, have not met the expectation of income growth of the
center as a whole. On the other hand, Taytay and Bagbag have observed values which greatly
surpass the expected values, suggesting they are thriving in terms of overall income
augmentation. The centers are listed here in order from most growth in overall income to least
growth:
1) Taytay
2) Bagbag
3) Caloocan
4) Montalban
5) Payatas
Again; the fact that the centers grew at different rates in overall incomes may be due to
a variety of factors such as loan amounts, participation by members (both qualitative and
quantitative), location, or social business type.
2.2.4 Summary
Based on the descriptive statistics and the results from both Chi-Squared Tests for
Goodness of Fit we may conclude that the centers are growing in both terms of average income
augmentation (income per member within a center) and overall income augmentation (the
center as a whole). The tests yielded P-Values of 0.00, meaning that the centers are in fact
growing at different rates in both terms of average income augmentation and overall income
augmentation. The results of average income augmentation differ from those of overall income
augmentation. The one similarity between the tests was witnessed in the case of Payatas,
which had the lowest growth rates in both tests. Although the average incomes and overall

14. *This denotes that there were respondents in the data who did not respond and the inference procedure
was performed without the respondents accounted for.
incomes of Payatas did increase, Payatas did not meet the requirement of expected values in
either test. Therefore, it is safe to say Payatas has experienced the least amount of success
from the Grameen Australia Philippines Loan cycle. Bagbag and Taytay are the only two centers
which met their respective expected values; in fact surpassed them. It is safe to say that Bagbag
and Taytay have been the most successful in the Grameen Australia Philippines loan cycle,
however Montalban and Caloocan have also performed well.
Based on this information, we may speculate that outside factors may be impacting the
success rates in the centers. These outside factors may include (but are not limited to) loan
amounts, willingness and participation/attendance of members, number of members in a
center, the location, the weather, or the social business type. With this information, we may
explore the factors causing the unequal growth rates to provide a solution for each unique
challenge faced by a specific center.
2.3.1 Question3: Is the average household income change different than the average
household income change of the Philippines as a nation?
2.3.2 Exploratory DataAnalysis
To accurately interpret the inference procedure results below, first it is crucial to analyze
the growth rates of average and overall incomes amongst centers as a percentage in a bar
graph comparison format. Growth rates differ from PHP analysis because growth rates
standardize the comparison to an equal playing field. In this analysis, it is clear which center(s)
is (are) actually thriving and which is (are) not. Taytay remains a strong center even in growth
rates, while Bagbag has proven to be a lower growth rate than in PHP terms and Montalban has
surpassed the mediocrity of PHP measures in terms of percentage growth rate.
A) Average Growth Rate

15. *This denotes that there were respondents in the data who did not respond and the inference procedure
was performed without the respondents accounted for.
B) Overall GrowthRate
2.3.3 Inference Procedure: T testformeans
To more preciselyandaccuratelyanswerthe question,aseriesof One-SampleT-Testswill be
run. The firstOne-SampleT-Testwillbe runusinganoverall rate of growth of the centerscombined,
comparedto the national average growthrate.The following5 testswill be runon the individualcenter
growthrates comparedtothe national average growthrate. The alphalevelsof significance will againbe
0.10 for “moderatelysignificant”and0.05 for“verysignificant”.
A) Overall Beneficiary Average Income:
One-Sample T: Change in Average Income as a %
Test of μ = 0.0479 vs > 0.0479
Variable N Mean StDev SE Mean 95% Lower Bound T P
Change in Average Income 5 0.356 0.232 0.104 0.135 2.97 0.021
Above are the results of the One-Sample T-Test run on “Change in Average Income as a
%” of the centers combined as a whole. The Null hypothesis here would be that there is not a
statistically significant difference between the average growth rate of beneficiaries of Grameen
Australia Philippines and the national average growth rate of the Philippines, which is 0.0479 or
4.79%. More specifically, this test was tailored so that the Null hypothesis states that the means
are equal in other words, μ=μ when “1” represents Grameen Australia Philippines and “2”

16. *This denotes that there were respondents in the data who did not respond and the inference procedure
was performed without the respondents accounted for.
represents the national average. The Alternative hypothesis would state that the growth rate of
the beneficiaries’ household income of Grameen Australia Philippines is faster than that of the
national average growth rate of household income, in other words μ1>μ2 when “1” represents
Grameen Australia Philippines and “2” represents the national average.
The P-Value here is 0.021, which is much less than the 0.05 “very significant” alpha level.
Since the data is significant, we may reject the Null hypothesis in favor of the Alternative
hypothesis that Grameen Austraia Philippines is providing a faster household income growth
rate than the national average. The national average growth rate, as calculated from the
growth in GNI per capita information between 2013 and 2014 from the World Bank, is 0.0479,
or 4.79%. The growth rate of the household income of Grameen Australia Philippines centers
combined is 0.356, or 35.6%. This means that at a holistic level, Grameen Australia Philippines is
significantly impacting household income growth more than the national augmentation rate.
B) The following One-Sample T-Tests were carried out by hand due
to the nature of the data; the following equation was used:
t= (x̄-μ)/s/√n
Where:
x̄= the sample mean (average growthrate of center)
μ is the populationmean(national average growthrate)
s= the sample standarddeviation
n= sample size
df= “degreesof freedom”=n-1
The Null hypothesesforeachwill be:The sample meanisequal tothe populationmean.
(μ=0.0479)
The Alternative hypothesesforeachwill be:The sample meanisgreaterthanthe population
mean.(μ>0.0479).
The alpha level of significance is0.05 or 5%.
Taytay:
μ= 0.0479
x̄ = 0.347
s= 0.325

17. *This denotes that there were respondents in the data who did not respond and the inference procedure
was performed without the respondents accounted for.
n= 32
df= 31 (so 30)
T-Value= 5.35
P-Value= 0.00
The P-Value<alphalevel.The dataissignificant.We rejectthe Null hypothesisinfavorof the
Alternativehypothesis.Taytay’shouseholdincome perpersonisgrowingata statisticallysignificant
fasterrate thanthe national average growthrate due to GrameenAustraliaPhilippinesloans.
Caloocan:
μ= 0.0479
x̄ = 0.314
s= 0.193
n= 8
df= 7
T-Value= 3.902
P-Value= 0.0029
The P-Value<alphalevel.The dataissignificant.We rejectthe Null hypothesisinfavorof the
Alternativehypothesis.Caloocan’shouseholdincome perpersonisgrowingatastatisticallysignificant
fasterrate thanthe national average growthrate due to GrameenAustraliaPhilippinesloans.
*Payatas:
μ= 0.0479
x̄ = 0.3
s= 0.245
n= 15
df= 14
T-Value= 4.0
P-Value= 0.0007

18. *This denotes that there were respondents in the data who did not respond and the inference procedure
was performed without the respondents accounted for.
The P-Value<alphalevel.The dataissignificant.We rejectthe Null hypothesisinfavorof the
Alternativehypothesis. Payatas’ householdincomeperpersonisgrowingata statisticallysignificant
fasterrate thanthe national average growthrate due to GrameenAustraliaPhilippinesloans.
Montalban:
μ= 0.0479
x̄ = 0.812
s= 0.1.14
n= 6
df= 5
T-Value= 3.069
P-Value= 0.0139
The P-Value<alphalevel.The dataissignificant.We rejectthe Null hypothesisinfavorof the
Alternativehypothesis. Montalban’shouseholdincome perpersonisgrowingata statisticallysignificant
fasterrate thanthe national average growthrate due to GrameenAustraliaPhilippinesloans.
*Bagbag:
μ= 0.0479
x̄ = 0.196
s= 0.255
n= 21
df= 20
T-Value= 2.68
P-Value= 0.0072
The P-Value<alphalevel.The dataissignificant.We rejectthe Null hypothesisinfavorof the
Alternativehypothesis. Bagbag’shouseholdincome perpersonisgrowingatastatisticallysignificant
fasterrate thanthe national average growthrate due to GrameenAustraliaPhilippinesloans.
2.3.4 Summary
Based on the exploratory data analysis and the inference procedures which were carried
out, we may conclude that the centers of Grameen Australia Philippines beneficiaries are all
growing at a much faster rate in average household income than the nationalaverage growth
rate of the Philippines. Each test resulted in a statistically significant P-Value, including the
overall test and the individual center tests. The centers with the most significant growth rate to
the least significant growth rate are listed here:

19. *This denotes that there were respondents in the data who did not respond and the inference procedure
was performed without the respondents accounted for.
1) Taytay: P-Value= 0.00 and 34.7% growth rate
2) *Payatas:P-Value= 0.0007 and a growth rate of 30%
3) Caloocan:P-Value=0.0029 and 31.4% growth rate
4) *Bagbag: P-Value=0.0072 and growth rate of 19.6%
5) Montalban:P-Value=0.0139 and 81.2% growth rate
Unfortunately, the data is somewhat tainted because some respondents chose not to disclosecertain
information regardingincome. For example, we know based on past inference procedures that Payatas grew the
leastin household income, however in this particular procedureitis regarded as the second most growth in
household income because of a lack of respondent data. This also shows the influenceone or two data points
have on the entire data set. The removal of these respondents’ data points dramatically increased the significance
level of the growth of Payatas.
3. Conclusions andDiscussions
In the analysis of the change in average and overall household incomes of beneficiaries
of Grameen Australia Philippines over the course of 6 months, we may conclude three main
points:
1) Each center is growing in average and overall income levels, though not yet at a
statistically correlated rate to the loans from Grameen Australia Philippines. However,
the correlation may become statistically significant with larger donor funds and larger
loan amounts given to beneficiaries.
2) Each center is growing at a different rate. These rates are different between average
income augmentation and overall income augmentation amongst the centers. Payatas
consistently performed the worst in income augmentation, meeting neither of the
expected value requirements. Meanwhile, Bagbag and Taytay consistently performed
well in augmentation of both types of income. Almost all expected values were met in
terms of average income augmentation, however the overall income augmentation
experienced more challenges in meeting the expected values.
3) Each centers’ average income (per member) is growing at a significantly faster rate than
the national average growth rate in GNI per capita of the Philippines. This is crucial
because it shows the standardized growth of each center to compare their successes.
Using this information, Grameen Australia Philippines may
A) Seek more and larger funds from donors to loan out to beneficiaries and meet targeted
amounts of income augmentations
B) Explore outside factors which impact the success of centers and members within
centers. For instance: willingness/effort of a member and/or center, education levels,

20. *This denotes that there were respondents in the data who did not respond and the inference procedure
was performed without the respondents accounted for.
location, climate, access to technology, type of social business, marketing techniques,
savings habits, etc. The identification of these outside contributing factors will allow
Grameen Australia Philippines to aid in overcoming center and/or member weaknesses
and highlighting center and/or member strengths. It will also enable Grameen Australia
Philippines, as well as the donors, to safely predict the return on their investment will be
rewarding both financially and socially. This will ensure a prosperous future full of
growth and expansion for Grameen Australia Philippines, its donors, and its
beneficiaries.
If you have any further questions regarding this analysis, please do not hesitate to contact me.
Thank you for choosing to work with me in analyzing the impact of Grameen Australia
Philippines.