This document summarizes research analyzing relationships between maternal and birth characteristics. It examines three research questions: whether smoking impacts birth weight, if birth weight differs between white and non-white mothers, and the relationship between maternal weight gain and birth weight. For each question, the document reviews two studies and conducts statistical analysis. It finds smoking may affect auditory senses but not birth weight, birth weight is lower for non-white mothers, and greater maternal weight gain correlates with higher birth weight.

1. 1
Data Analytics and Research 2
Data Analytics and Research:
4.2 Assignment
Data Analytics & Research
Research Question Selection
1) Is there a relationship between whether a mother smokes or
not and the birth weight of her child?
2) Is the birth weight of babies from white moms different than
those from non-white moms?
3) Is there a relationship between the mother’s weight gain
during pregnancy and the birth weight of her child?
Question 1
Is there a relationship between whether a mother smokes or not
and the birth weight of her child?
For the first part we find two articles dealing with the above
research question and provide a summary of each article:
Article 1
The behaviour of infants whose mothers smoke in pregnancy
The behavior of infants whose mothers smoke in pregnancy by
David Saxton
In this study, the author compares the infants of mothers who
had smoked more than 15 cigarettes/day throughout their
pregnancy and the infants whose mothers did not smoke. The

2. author observes that all the infants were spontaneously
delivered at the end of the pregnancy term and they were of
normal birth weight. The author also matches the two groups for
maternal age, social class and parity. The author observes that
the duration of labour and analgesia during labour were similar
for mothers who smoked and the mothers who were not
smokers. Although the author does not observe any significance
effect in the birth weight of infants due to smoking, he finds
evidence to suggest the child’s auditory senses could be
affected by the mother smoking during pregnancy (Saxton,
1978).
Article 2
Maternal smoking and birthweight
The study was aimed to find if mother’s smoking during
pregnancy affected the birthweight of the child. The data
collected and analysed was of 1016 pairs of births that occurred
in 1946- 1963 in Washington County, Maryland. The author
examines the mean birth weight of first and second members of
pairs of consecutive births of the same mother with respect to
her smoking habits. He concludes no significant difference in
the birth weight of the infants in which the mother was a
smoker in both or either of the two pregnancies (Silverman,
1977). But the author observes that the birth weights of the
infants of women who were non-smokers throughout both
pregnancies was higher than that of infants of women who
smoked during both pregnancies. He also observes that the
infants of first members of pairs in which the mothers smoked
only during the second pregnancy tended to have birth weights
which were lower than that of infants of non-smokers and
higher than that of infants of smokers (Silverman, 1977).
Hence, he concludes that these findings neither confirm nor
deny the hypothesis that smoking during pregnancy causes a
reduction in birth weight of the infant.
Analysis
Now for analysing our data we find the mean, median, mode,

3. variance and the standard deviation of our data in the table
below:
Birth weight of the child
Mother Smoking
Birth weight of the child
Mother Non-Smoking
Mean
Median
Mode
Variance
Standard Deviation
7.144273
7.31
7.44
2.303749
1.517811
6.82873
7.06
7.31
1.906244
1.380668
Now we do the statistical tests to see if there is any correlation
between the two populations. Let the populations non-smoking
mother and Smoking Mother be A and B respectively. Let and
be the total number in both the populations.
Now,
Null Hypothesis: The difference in the population means, .
Alternate Hypothesis: .
We are going to conduct the test in the 0.05 level of
significance. The degrees of freedom for our population, df = n-
1 where n is the minimum of and . Here we are going to
conduct the two tailed student’s t test. Firstly, for our
populations A and B we calculate the t- value using where is
the mean of the population A and is the mean of the population

4. B, is the variance of population A and is the variance of the
population B and is the total number of values in population A
and is the total number of values in population B.
After calculation, we get t = 1.809311. We use a t- distribution
calculator to find the p- value for the null hypothesis to be true,
p = 0.072807 which is greater than our significance value 0.05.
Hence, we conclude that our alternate hypothesis is not
significant at the 0.05 significance level. We do not claim that
our null hypothesis to be true. We do not have sufficient data to
conclude that.
Question 2
Is the birth weight of babies from white moms different than
those from non-white moms?
For the first part we find two articles dealing with the above
research question and provide a summary of each article:
Article 1
Birth weight of US biracial (black-white) infants: regional
differences
The author examines the prevalence of low birth weight among
biracial infants (black mother-white father vs. white mother-
black father) in different regions of United States using the data
base in 1991. He observes that the rate of low birth weight was
31% higher in the black mother and white father group than in
the white mother and black father group (Polednak & King,
1998). He also observes that the difference was smaller in the
Northeast United States, is larger in the Western United States.
He concludes that there is prevalence of low birth weight among
biracial infants and that more studies are needed to identify the
maternal factors involved in the regional difference.
Article 2
Diverging associations of maternal age with low birthweight for
black and white mothers
In United States, studies suggest a risk of low birth weight

5. increases more quickly with maternal age for black women than
it does for white women. The aim was to study the above
statement. They take the birth data in Chicago from 1994 to
1996. They study the link between maternal age with risk of low
birth weight with respect to maternal race/ethnicity, marital
status, education, and neighbourhood poverty of the mothers
(Rich-Edwards et al., 2003). In their findings, they see the risk
of low birthweight rose steeply with maternal age for black, but
not white, mothers. After making the data bit adjusted by
considering various other factors, they conclude that the risk of
low birth weight rises more quickly with maternal age for
women who are disadvantaged, regardless of their race and
ethnicity. They conclude that the particularly steep increase in
risk of low birth weight with increasing maternal age for black
women is due to the high prevalence of disadvantaged women in
this population (Rich-Edwards et al., 2003).
Analysis
Now for analysing our data we find the mean, median, mode,
variance and the standard deviation of our data in the table
below:
A
Birth weight of child
White mother
B
Birth weight of child
Non-white mother
Mean
Median
Mode
Variance
Standard Deviation
7.172539683
7.47
7.56
2.575158629

6. 1.60473008
6.75404762
6.845
6.75
2.94240346
1.71534354
We will use the student’s t test to see if there is any correlation
between the two populations.
Our Null Hypothesis will be: The difference in the population
means is 0, that is
And the Alternate Hypothesis will be:
We are going to conduct the test in the 0.05 level of
significance. Hence The degrees of freedom for our population,
df = n-2 where n is the minimum of and . Here we are going to
conduct the two tailed student’s t test. Firstly, for our
populations A and B we calculate the t- value using the
following formula where is the mean of the population A and
is the mean of the population B, is the variance of population A
and is the variance of the population B and is the total number
of values in population A and is the total number of values in
population B. After putting in the values in the formula, we get
t =. Now, the next step is to calculate the probability value or p
value for the null hypothesis to be true. We use the t-
distribution calculator to find the p- value which turns out to be
less than significance value.
Now we do the statistical tests to see if there is any correlation
between the two populations. Let the birth weights of the white
mother and non-white mother be A and B respectively. Let and
be the total number in both the populations.
Now,
Null Hypothesis: The difference in the population means, .
Alternate Hypothesis: .
We are going to conduct the test in the 0.05 level of
significance. The degrees of freedom for our population, df = n-
1 where n is the minimum of and . Here we are going to

7. conduct the two tailed student’s t test. Firstly, for our
populations A and B we calculate the t- value using where is
the mean of the population A and is the mean of the population
B, is the variance of population A and is the variance of the
population B and is the total number of values in population A
and is the total number of values in population B.
After calculation, we get t = 2.58579949. We use a t-
distribution calculator to find the p- value for the null
hypothesis to be true, p = 0.010218 which is which is less than
our significance value 0.05. Hence, we reject our null
hypothesis. And we conclude that our alternate hypothesis is
significant at the 0.05 significance level. We see that our results
match with the results of the articles we reviewed. It could be
because we got our p-value around 0.01 which is very much
lesser than our significance value 0.05.
Question 3
Is there a relationship between the mother’s weight gain during
pregnancy and the birth weight of her child?
For the first part we find two articles dealing with the above
research question and provide a summary of each article.
Article 1
The association between pregnancy weight gain and
birthweight: A within-family comparison
The authors aim to examine the association between maternal
weight gain and birthweight using state-based birth registry
data. They use the data of births in Michigan and New Jersey,
USA, between Jan 1, 1989, and Dec 31, 2003. From their data
they excluded the following data points, 1) gestation less than
37 weeks or 41 weeks or more 2) maternal diabetes 3)
birthweight less than 500 g or more than 7000 g and 4) those
women whose data for pregnancy weight gain was missing. On
their analysis, they find a consistent link between pregnancy
weight gain and birthweight. They observe that the infants of
women who gained more than 24 kg during pregnancy were

8. 148·9 g heavier at birth than were infants of women who gained
8-10 kg (Ludwig & Currie, 2010). They conclude that the
maternal weight gain during pregnancy increases birthweight
independent of the genetic factors.
Article 2
Gestational weight gain and its relationship with the birthweight
of offspring
The authors aim to study the relationship between the weight
gain during pregnancy ang the birthweight of the offspring.
Data of child births from Beijing Obstetrics and Gynecology
Hospital and Haidian Maternity and Child Health Care Hospital
in 2010 were used for the study. And they observed only
singleton pregnancies. All the pregnant women were divided
into underweight, normal weight and overweight group (Wang
et al., 2013). They defined the Birthweight between 2500 g and
4000 g as normal birthweight, and 2900 g to 3499 g as
appropriate birthweight. They divided the population into
different groups and explored were used it to analyse the
gestational weight gain (GWG). After researching the data for
all the diferent groups using appropriate statistical analysis,
they conclude the mother’s weight gain during pregnancy is
positively related to the child’s birth weight (Wang et al.,
2013).
Analysis
Now for analysing our data we find the mean, median, mode,
variance and the standard deviation of our data in the table
below:
Mother’s weight gain during pregnancy
(x)
Birth weight of the child
(y)
Mean
Median

9. Mode
Variance
Standard Deviation
30.3258
30
30
202.6061
14.23398
7.119507
7.31
7.44
2.220635
1.49018
Here we are going to conduct the two tailed student’s t test. We
will use the student’s t test to see if there is any correlation
between the two populations.
Null Hypothesis: The population correlation coefficient
Alternate Hypothesis: The population correlation coefficient
The degrees of freedom for our population, df = n-2 = 998 -2 =
996. Firstly, for our variables x and y, we calculate the
population correlation coefficient,
.
By calculating, we get r = 0.154171555.
Now for the t-value, we use the formula
where r is the sample correlation coefficient we computed in the
previous step and n is the total number in the sample. Now the
next step is to calculate the probability value or. By using t-
distribution calculator we find that the p value for the null
hypothesis to be true is, p < .00001. We can see that it is less
than our significance value 0.05. Hence we reject our null
hypothesis. And we conclude that our alternate hypothesis is
significant in our given significance level. We see that our
results match with the results of the articles we reviewed. It
could be because we got our p-value around 0.01 which is very

10. much lesser than our significance value 0.05.
References
Ludwig, D. S., & Currie, J. (2010). The association between
pregnancy weight gain and birthweight: a within-family
comparison. The Lancet, 376(9745), 984-990.
Polednak, A. P., & King, G. (1998). Birth weight of US biracial
(black-white) infants: regional differences. Ethnicity &
disease, 8(3), 340-349.
Rich-Edwards, J. W., Buka, S. L., Brennan, R. T., & Earls, F.
(2003). Diverging associations of maternal age with low
birthweight for black and white mothers. International journal
of epidemiology, 32(1), 83-90.
Saxton, D. W. (1978). The behaviour of infants whose mothers
smoke in pregnancy. Early human development, 2(4), 363-369.
Silverman D.T.
(1977). Maternal smoking and birth weight. Am J Epidemiol,
105, 513-21.
Wang, W. P., Chen, F. F., Mi, J., Teng, Y., Zhao, J., Wu, M. H.
& Teng, H. H. (2013). Gestational weight gain and its
relationship with the birthweight of offspring. Zhonghua fu
chan ke za zhi, 48(5), 321-325.
2
Agenda Comparison Grid Assignment Template for Part 1 and
Part 2
Part 1: Agenda Comparison Grid
Use this Agenda Comparison Grid to document information
about the population health/healthcare issue you selected and
the presidential agendas. By completing this grid, you will
develop a more in depth understanding of your selected issue
and how you might position it politically based on the

11. presidential agendas.
You will use the information in the Part 1: Agenda Comparison
Grid to complete the remaining Part 2 and Part 3 of your
Assignment.
Identify the Population Health concern you selected.
Describe the Population Health concern you selected.
Administration (President Name)
Explain how each of the two presidential administrations
approached the issue.
Identify the allocations of resources that the current and
previous presidents dedicated to this issue.
Part 2: Agenda Comparison Grid Analysis
Using the information you recorded in Part 1: Agenda
Comparison Grid, complete the following to document
information about the population health/healthcare issue you
selected
Administration
Which administrative agency (like HHS, CDC, FDA, OHSA)
would most likely be responsible for helping you address the
healthcare issue you selected? Why is this agency the most
helpful?

12. How do you think your selected healthcare issue might get on
the presidential agenda? How does it stay there?
Who would you choose to be the entrepreneur/
champion/sponsor (this can be a celebrity, a legislator, an
agency director, or others) of the healthcare issue you selected?
An example is Michael J. Fox a champion for Parkinson’s
disease.
© 2021 Walden University, LLC
6/11/22, 8:09 AM Preview Rubric: 4.2 Assignment: Is There a
Difference? (75 points) - 3SU2022 Data Analytics & Research
(BADM-707-01B) - Indiana Wesley…
https://brightspace.indwes.edu/d2l/lp/rubrics/preview.d2l?ou=16
5060&rubricId=518401&originTool=quicklinks 1/4
4.2 Assignment: Is There a Difference? (75 points)
Course: 3SU2022 Data Analytics & Research (BADM-707-01B)
Criteria Excellent Competent
Needs
Improvement

13. Inadequate/Faili
ng
Criterion Score
Determination
of test
hypothesis
and selection
of test
/ 20
Statistical
tools
/ 20
20 points
Demonstrated
clear, insightful
critical thinking
in determining
the test

14. hypothesis and
selection of
test.
19 points
(16-19 points)
Demonstrated
competent
critical thinking
in determining
the test
hypothesis and
selection of
test
15 points
(12-15 points)
Demonstrated
limited critical

15. thinking in
determining
the test
hypothesis and
selection of
test
11 points
(0-11 points)
demonstrated
little to no
critical thinking
in determining
the test
hypothesis and
selection of
test
20 points
Statistical tools

16. were
constructed
correctly and
strongly
supported the
discussion.
18 points
(16-18 points)
Statistical tools
were
constructed
correctly and
generally
supported the
discussion.
15 points
(12-15 points)

17. Statistical tools
were
constructed
correctly and
but did not
support the
discussion.
11 points
(0-11 points)
Statistical tools
were not
constructed
correctly but
did not
support the
discussion.

18. 6/11/22, 8:09 AM Preview Rubric: 4.2 Assignment: Is There a
Difference? (75 points) - 3SU2022 Data Analytics & Research
(BADM-707-01B) - Indiana Wesley…
https://brightspace.indwes.edu/d2l/lp/rubrics/preview.d2l?ou=16
5060&rubricId=518401&originTool=quicklinks 2/4
Criteria Excellent Competent
Needs
Improvement
Inadequate/Faili
ng
Criterion Score
Discussion of
your findings
and comparing
it to published
research
/ 2020 points
Demonstrated
clear, insightful
critical thinking
in the

19. Discussion of
the findings
and comparing
it to published
research.
19 points
(16-19 points)
Demonstrated
competent
critical thinking
in the
Discussion of
the findings
and comparing
it to published
research.
15 points
(12-15 points)

20. Demonstrated
limited critical
thinking in
Discussion of
the findings
and comparing
it to published
research.
11 points
(0-11 points)
Demonstrated
little to no
critical thinking
in the
Discussion of
the findings
and comparing

21. it to published
research.
6/11/22, 8:09 AM Preview Rubric: 4.2 Assignment: Is There a
Difference? (75 points) - 3SU2022 Data Analytics & Research
(BADM-707-01B) - Indiana Wesley…
https://brightspace.indwes.edu/d2l/lp/rubrics/preview.d2l?ou=16
5060&rubricId=518401&originTool=quicklinks 3/4
Total / 75
Criteria Excellent Competent
Needs
Improvement
Inadequate/Faili
ng
Criterion Score
Grammar,
Spelling,
Length, and
Citation
/ 1515 points
Sentence

22. structure is
complete with
correct
spelling,
punctuation,
capitalization,
varied diction,
and word
choices.
Assignment
length is
correct with
sources
correctly cited.
14 points
(12-14 points)
Sentence

23. structure has
minor errors
(fragments,
run-ons) with
correct
spelling,
punctuation,
capitalization,
and limited
diction and
word choices.
Assignment
length is
correct with
sources
correctly cited.
11 points

24. (9-11 points)
Sentence
structure has
several errors
in sentence
fluency with
multiple
fragments/run-
ons; poor
spelling,
punctuation,
and/or word
choice.
Assignment
length is
inappropriate
with several

25. format and
citation errors.
8 points
(0-8 points)
Sentence
structure has
serious and
persistent
errors in
sentence
fluency,
sentence
structure,
spelling,
punctuation,
and/or word
choice.

26. Assignment
length is
inappropriate
with several
format and
citation errors
or sources not
cited.
6/11/22, 8:09 AM Preview Rubric: 4.2 Assignment: Is There a
Difference? (75 points) - 3SU2022 Data Analytics & Research
(BADM-707-01B) - Indiana Wesley…
https://brightspace.indwes.edu/d2l/lp/rubrics/preview.d2l?ou=16
5060&rubricId=518401&originTool=quicklinks 4/4
Overall Score
Excellent
69 points minimum
Competent
62 points minimum
Needs Improvement
54 points minimum

27. Inadequate/Failing
0 points minimum