Data Analysis: Age-adjusted Death Rate and Average Life Expectancy

This paper analyzes age-adjusted death rate and average life expectancy data in the United States of America. The data is evaluated with the aid of Weka assessment platform and Microsoft Excel to provide a detailed and accurate data evaluation. The paper includes background and motivation, material for analysis, analysis approach, findings, interpretation, and recommendations. The subject is data analysis, and the course code, course name, and college/university are not mentioned.

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Data Analysis: Age-adjusted Death Rate and Average Life Expectancy
Type of Academic Paper
Date
Word Count
Student's Name
Affiliation/University Name

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Table of Content
Background and Motivation........................................................................................................................3
Material for Analysis...................................................................................................................................4
Analysis Approach......................................................................................................................................5
Findings.......................................................................................................................................................7
Dataset 1..................................................................................................................................................7
Dataset 2................................................................................................................................................19
Interpretation.............................................................................................................................................24
Recommendations.....................................................................................................................................27
Reference List...........................................................................................................................................28
Appendix...................................................................................................................................................29

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Data Analysis: Age-adjusted Death Rate and Average Life Expectancy
Background and Motivation
In the assessment of population health and overall well-being in a given country, there
are several factors to consider; such as, the prevalence of diseases, life expectancy, and average
death rate with regards to region, age, year, and race. The information obtained from these
assessment tests will aid with the determination of whether or not the country being evaluated
has an effective healthcare system and the government has put in place the proper regulations
and incentives to boost food availability, nutrition, and disease control1. For instance health data
collected from all States across the United States of America over the last five decades will aid in
understanding whether or not the quality of life for Americans has improved in the last ten years.
In the same way, race centric data can be used to assess inequalities of healthcare services with
regard to race. For instance, more white people could be accessing quality healthcare services
compared to black individuals with the same income per capita. Moreover, examination of data
with regard to regions and states helps with the recognition of correlation between different
variables in the diverse categories. According to most medical professionals, women are more
likely to solicit healthcare service compared to men suffering from the same physical and mental
ailment. This situation has led physicians to believe that men have a lower life expectancy
compared to women; and in addition, they are more at risk of succumbing to death as a result of
failure to secure medical treatment at the proper time2.
1 Rajendar Kumar, Research Methodology (New Delhi: APH Publishing, 2008), 34–120.
2 George A. F. Seber, and Alan J. Lee, Linear Regression Analysis (Hoboken: John Wiley &
Sons, 2012), 1–582.

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In the United States of America, organizations like the Centers for Disease Control and
Prevention (CDC) collect, compile, analysis, and present data related to difference medical
conditions. These data is then made accessible to all individuals, groups, and agencies for the
purposes of their own independent research studies. The data is normally archived in a
chronological manner to ensure that trend analysis can be easily performed to identify whether or
not advancements in healthcare delivery have positively impacted the medical profession. As
such, the study of data relating to diseases is aimed at providing a realistic scope over the current
and past healthcare trends. Once this information is obtained it can be used by the proper
stakeholders to ensure that people of all races and gender are provided with quality healthcare
across all States in future. The formulation process for an effective healthcare reform plan
normally takes into account the opinions and suggestions of medical professionals, government
agencies, NGO representatives, and given members of society. In conclusion, the data will be
analyzed with the primary objective of assessing the impact of gender, and demographic on the
life expectancy and death rate in the United States of America3.
Material for Analysis
There are two sets of possess both quantitative and qualitative data that need to be
analyzed. One data set is centered on the evaluation of different disease causation, and deaths
across all 52 States over the past 16 years. The data is classified under six categories: year, 113
cause name, cause name, state, deaths, and age-adjusted death rate. There are over 15 disease
causes that are presented with regard to the 52 states. The figures for deaths and age adjusted
death rate associated with each disease are provided in a chronological manner starting from the
3 George A. F. Seber, and Alan J. Lee, Linear Regression Analysis (Hoboken: John Wiley &
Sons, 2012), 1–582.

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year 1999 to 2015. Lately, the data is comprehensive and large enough for proper inferences to
be made with regard to the overall medical system observed in individual States across the
United States of America. As such, the results produced after analysis can be indicated as the
accurate representation of the healthcare and morbidity situation in America. The other data set
evaluates life expectancy and age-adjusted death rate data will regard to gender and race. The
data is classified under five distinct categories: year, race, sex, average life expectancy, and age-
adjusted death rate. Similarly, this data set is also arranged in a chronological order with data
ranging from 1900 to 2015; roughly 115 years. This data set is therefore significantly more
comprehensive and inclusive compared to the latter, simply because it employs a wide-time
frame of data collection. Nevertheless, this does also present problems because of significant
differences in lifestyle and healthcare services present between 1900 and 2015. For instance,
diseases like HIV/AIDS were not present for the better part of the 1900s; this disease has become
a critical cause of 400,000 deaths annually in the United States of America. This information can
be ignored or be cancelled out by the fact that deaths attributed to smallpox and polio have
almost vanished over the past three decades4.
Analysis Approach
The two data sets can be easily analyzed to check for outliners, correlation, skewness,
and other crucial information. As such, a quantitative research method will be employed that will
allow for establishment of relationships between different variables in the data sets. A
descriptive approach is used in the process because the data was measured and collected once;
4 Debra Wetcher-Hendricks, Analyzing Quantitative Data: An Introduction for Social
Researchers (Hoboken: John Wiley & Sons, 2011), 1–398.

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unlike in an experimental approach where the data is collect before and after treatment. The
mainly reason for using a quantitative research method is because: the sample sizes are large; the
data was gathered using structured collection media; there is need to use a statistical
tool/software; there are defined research objectives; and lastly the data is in numerical form or
can be assigned numerical equivalents5. As such, the analysis will take into account a lot of
diagrammatical and graphical representation to aid in the explanation of various analysis results.
Assumptions will be made where necessary to ensure that the information presented is governed
within given parameters. Lastly, the results of the analysis will be employed in the development
of recommendation; independent of the researcher's own research expectations.
Missing data will be treated in different ways depending on the statistical analysis being
run. For instance, in a correlation assessment all variables being evaluated need to have the same
frequency; therefore, if two variables have 155 and the three has 154, the row with the missing
data for all three will be disregarded. Hence, all three will have 154 items to be employed in the
correlation analysis. On the other hand, if the data is being evaluated for descriptive statistics e.g.
mean, median, variances, and skewness; the missing values will be ignored entirely but will not
affect the assessment of the next variable. For instance, if we are to find the mean for each of two
variables where one has 155 rows of data but 7 of those rows are empty, and another has 155
data items in every row. Then, the assessment of mean will be performed on only 148 items for
the first variable; but, on 155 items for the second. Lastly, in a regression analysis the empty data
cells can be treated as being occupied by zeros or be disregarded. For this assessment the cells
will be ignored; As such, the entire row will be ignored for both the dependent and independent
variables regardless of which has missing cell data.
5 Sarah E. Kemp, Joanne Hort, and Tracey Hollowood, Descriptive Analysis in Sensory
Evaluation (Hoboken: John Wiley & Sons, 2018), 594–744.

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The Data will be assessed with the aid of Weka assessment platform and Microsoft Excel
to provide a detailed and accurate data evaluation. Important analysis outputs will be included in
the discussion segment of the research; while, other results (tables, charts, and graphs) will be
added to the appendix. Weka is a powerful analytical tool that can generate a wide range results
through the use of in-built statistical analysis algorithms. Microsoft Excel is a user-friendly
software that allows for easy compilation and evaluation of data sets. The descriptive statistics
(e.g. mean, Standard deviation, and variance) of the data will be compiled together; moreover,
regression analysis be performed to clarify relationships between different variables. A
hypothesis analysis maybe included for data variables with considerably similar results and
outputs. Hypothesis will be employed (if necessary) to answer question about the association
between variable statistics; for instance, is the mean for age-adjusted death rate equal for both
men and women6.
Findings
Dataset 1
It is important to note that the following variable identities were given by Weka to the 18
variables; which are influenced by race and sex factors with regard to the life expectancy and
death rate in the dataset.
Indicated
Variable
Meaning
AverageAllBoth Average Life Expectancy: All Races for Both
6 Ning-Zhong Shi, and Jian Tao, Statistical Hypothesis Testing: Theory and Methods (Singapore:
World Scientific, 2008), 1–307.

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Gender
AverageAllFem Average Life Expectancy: All Races for Females
AverageAllMal Average Life Expectancy: All Races for Males
AverageBlackB Average Life Expectancy: Black Race for Both
Gender
AverageBlackF Average Life Expectancy: Black Race for Females
AverageBlackM Average Life Expectancy: Black Race for Males
AverageWhiteB Average Life Expectancy: White Race for Both
Gender
AverageWhiteF Average Life Expectancy: White Race for
Females
AverageWhiteM Average Life Expectancy: White Race for Males
AgeadjAllBot Aged-adjusted Death Rate: All Races for Both
Gender
AgeadjAllFem Aged-adjusted Death Rate: All Races for Females
AgeadjAllMal Aged-adjusted Death Rate: All Races for Males
AgeadjBlackB Aged-adjusted Death Rate: Black Race for Both
Gender
AgeadjBlackF Aged-adjusted Death Rate: Black Race for
Females
AgeadjBlackM Aged-adjusted Death Rate: Black Race for Males
AgeadjWhiteB Aged-adjusted Death Rate: White for Both Gender
AgeadjWhiteF Aged-adjusted Death Rate: White Race for

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Females
AgeadjWhiteM Aged-adjusted Death Rate: White Race for Males
Descriptive statistics for dataset specified above are as follows in Weka.
Variable Mean Median Minimum Maximum
AverageAllBoth 66.547 69.6 39.1 78.9
AverageAllFem 69.199 72.9 42.2 81.3
AverageAllMal 64.016 66.6 36.6 76.5
AverageBlackB 58.355 63.6 30.8 75.6
AverageBlackF 60.963 66.1 32.5 78.5
AverageBlackM 55.789 60.5 29.1 72.5
AverageWhiteB 67.34 70.5 39.8 79.1
AverageWhiteF 70.044 73.9 43.2 81.4
AverageWhiteM 64.803 67.4 37.1 76.7
AgeadjAllBot 1477.9 1336.5 724.6 2541.6
AgeadjAllFem 1305.1 1113.6 616.7 2410.4
AgeadjAllMal 1682.7 1611.7 855.1 2740.5
AgeadjBlackB 1881 1561.3 849.3 3586.2
AgeadjBlackF 1694.5 1336.2 710.8 3362.4
AgeadjBlackM 2112.9 1861.1 1034 3845.7
AgeadjWhiteB 1446.9 1310.2 725.4 2501.2
AgeadjWhiteF 1275.2 1085.8 617.6 2394
AgeadjWhiteM 1650.5 1587.3 853.4 2680.7

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Variable Std.
Dev.
AverageAllBoth 9.631
AverageAllFem 10.161
AverageAllMal 9.0355
AverageBlackB 13.113
AverageBlackF 14.072
AverageBlackM 12.117
AverageWhiteB 9.563
AverageWhiteF 10.042
AverageWhiteM 9.0227
AgeadjAllBot 553.98
AgeadjAllFem 569.5
AgeadjAllMal 520.25
AgeadjBlackB 780.51
AgeadjBlackF 822.3
AgeadjBlackM 716.99
AgeadjWhiteB 545.14
AgeadjWhiteF 559.6
AgeadjWhiteM 512.95
Looking at the Mean for average life expectancy; it is clear that white females have the
highest life expectancy while black males have the lowest. The mean for age-adjusted death rate
we see that black males have the highest figure and the lowest is taken by white females.

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Overall using the minimum and maximum statistics it is easy to observe that black males have
the lowest life expectancy ever documented within the 115 year period; while, white women
have the highest life expectancy between 1900 and 2015. In addition, the greatest figure for age-
adjusted death rate was taken up by black males while the minimum number of deaths was
witness in females from both races. The average life expectancy data for all variables is
negatively skewed; on the other hand, age-adjusted death rate data for all 9 variables is positively
skewed.
The graph above indicates average life expectancy for all races and gender groups. The general
trend is that average life expectancy has increased substantially for all individuals between 1900
and 2015. It as showcases the age-adjusted death rate for all individuals regardless of race or

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gender. The overall feel in the movement of the lines indicates that age-adjusted death rate has
decrease significantly over the past 115 year for all race and both gender groups.
The correlation matrix below is for all average life expectancy variables. The correlation
between all variables is positive and very strong. However, there are those with stronger
correlation with each compared to others; for instance, the correlation between the average life
expectancy data for black males and all females can be considered the weakest. Nevertheless, all
nine variables have strong associations with each other.
AverageAllB
oth
AverageAllFe
m
AverageAllM
al
AverageBlac
kB
AverageBlack
F
1 0.9977 0.9975 0.992 0.9922 AverageAllBo
th
1 0.9906 0.9911 0.9944 AverageAllFe
m
1 0.9887 0.9854 AverageAllM
al
1 0.9981 AverageBlack
B
1 AverageBlack
F
AverageBlack
M
AverageWhit
eB
AverageWhit
eF
AverageWhite
M
0.9864 0.9994 0.9971 0.9965 AverageAllBo

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th
0.9822 0.997 0.9992 0.9895 AverageAllFe
m
0.9871 0.9972 0.9904 0.9994 AverageAllM
al
0.9973 0.9919 0.9914 0.9879 AverageBlack
B
0.9909 0.9915 0.9937 0.984 AverageBlack
F
1 0.9874 0.984 0.9871 AverageBlack
M
1 0.9977 0.9975 AverageWhite
B
1 0.9906 AverageWhite
F
1 AverageWhite
M
The correlation matrix below shelters all variables for age-adjusted death rate. All 9 variables
have strong positive correlation. Amongst these the "weakest" correlation is between white males
and black females. A correlation matrix of all 18 variables indicates there is a strong negative
correlation between age-adjusted death rate variables and average life expectancy variables.
AgeadjAllBo AgeadjAllFe AgeadjAllMa AgeadjBlack AgeadjBlackF

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t m l B
1 0.9966 0.9928 0.9905 0.9899 AgeadjAllBot
1 0.9799 0.9921 0.9956 AgeadjAllFem
1 0.979 0.9716 AgeadjAllMal
1 0.9976 AgeadjBlackB
1 AgeadjBlackF
AgeadjBlack
M
AgeadjWhite
B
AgeadjWhite
F
AgeadjWhite
M
0.9792 0.9997 0.9958 0.9924 AgeadjAllBot
0.9762 0.9966 0.9997 0.9795 AgeadjAllFem
0.9765 0.9921 0.9786 0.9996 AgeadjAllMal
0.9946 0.9906 0.9922 0.9782 AgeadjBlackB
0.9853 0.9901 0.9958 0.971 AgeadjBlackF
1 0.9791 0.9764 0.9754 AgeadjBlack
M
1 0.9964 0.9923 AgeadjWhite
B
1 0.9788 AgeadjWhiteF
1 AgeadjWhite
M
The X-Y scatter plot below takes into consideration that average life expectancy (for all races
and both sexes) is on the X-axis and age-adjusted death rate (for all races and both sexes) is on

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the y-axis. The best line of fit indicates that there is an inverse relationship between the two
values: Where increment in one variable will result in a decrement in the other. Below the X-Y
scatter plot is a table that contains the output for OLS estimation or regression analysis. We are
using average life expectancy for all races and both sexes as the dependent variables and the
independent variables are average life expectancy for all races males and females; hence there
are two predictor variables (AverageAllFem and AverageAllMal) and one explanatory variable
(AverageAllBoth).

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Dataset 2
The results below were generated by analyzing the prevalence of these diseases will regard to
summary statistics or descriptive statistics like mean, median, and variance. The results for all
the other disease causes will be included in the appendix. Mean deaths attributed to cancer are
considerably high as indicated below in the table.
All Causes Alzheimer's Disease Cancer
Deaths Age-
adjusted
Death Rate
Deaths Age-
adjusted
Death Rate
Deaths Age-
adjusted
Death Rate
Mean 95409.0905 803.956334
8
2844.59 24.2484162
9
21824.6561
1
181.180769
2
Median 37031 791.6 1088.5 23.7 8171.5 181.2
STD 337913.683 96.9783791
3
10343.19 6.84224998
1
77292.5998
4
20.3201034
9
Variance 1.14186E+1
1
9404.80601
9
1.07E+0
8
46.8163848 5974145990 412.906606
Skewness 6.808841259 0.41480188
1
7.269754 0.41900982
6
6.79566480
1
-
0.06232752
2
Maximu
m
2712630 1087.3 110561 47.1 595930 241.4
Minimum 2708 584.9 24 7 633 124.7
Range 2709922 502.4 110537 40.1 595297 116.7

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5%
Percentile
5050.7 663.145 126.15 14.315 1154.55 148.015
95%
Percentile
170777.8 981.34 4828.45 36.285 40050.8 212.685
The following is a correlation table for some of the cause in the dataset. There is a moderately
strong correlation between the aged-adjusted death rate for all causes and that of cancer.
Likewise, three is a very strong positive correlation between deaths linked to Alzheimer's and all
causes. However, majority of the data variables have weak negative correlation with each other.
All Causes Alzheimer's Disease Cancer
Death
s
(AC)
Age-adjusted
Death Rate
(AC)
Death
s
(AD)
Age-adjusted
Death Rate
(AD)
Death
s (C)
Age-
adjusted
Death Rate
(C)
All
Causes
Deaths (AC) 1
Age-adjusted
Death Rate
(AC)
-
0.032
05755
5
1
Alzheim
er's
Disease
Deaths (AD) 0.919
62561
5
-
0.045800368
1
Age-adjusted - - 0.016 1

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Death Rate
(AD)
0.035
16741
4
0.006130004 97472
7
Cancer Deaths (C) 0.937
76719
1
-0.02271747 0.977
79608
9
-
0.038233286
1
Age-adjusted
Death Rate
(C)
-
0.035
47614
8
0.697064827 -
0.050
32985
3
-
0.057645321
-
0.015
96413
4
1
The table below is a representation of regression analysis where the dependent variable is deaths
associated with all causes; and the independent variables are deaths and age-adjusted death rates
for both Alzheimer's and Cancer. The adjust R square is considerably high at 87.94% and the
significant of F is equivalent to zero. Deaths linked to both Alzheimer's and cancer has a positive
impact on the value of the dependent variables. On the other hand, age-adjusted death rates for
both Alzheimer's and cancer have a negative impact on the explanatory variable.

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SUMMARY OUTPUT
Regression Statistics
Multiple R 0.938044651
R Square 0.879927768
Adjusted R Square 0.879381364
Standard Error 117358.077
Observations 884
ANOVA
df SS MS F Significance F
Regression 4 8.87195E+13 2.21799E+13 1610.398368 0
Residual 879 1.21064E+13 13772918232
Total 883 1.00826E+14
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept 66871.04935 39014.61816 1.713999842 0.086881296 -9701.6319 143443.7306 -9701.6319 143443.7306
Deaths (AD) 1.631295004 1.910223093 0.853981407 0.393347958 -2.11783573 5.380425738 -2.11783573 5.380425738
Age-adjusted Death Rate (AD) -154.9171718 598.2136675 -0.258966286 0.795721884 -1329.01105 1019.17671 -1329.01105 1019.17671
Deaths (C) 3.884504053 0.255503087 15.2033547 1.61422E-46 3.383036723 4.385971383 3.383036723 4.385971383
Age-adjusted Death Rate (C) -315.2861885 197.1350265 -1.599341295 0.110104141 -702.196487 71.6241097 -702.196487 71.6241097
1999
1999
1999
1999
1999
1999
1999
1999
1999
1999
1999
1999
1999
0
500000
1000000
1500000
2000000
2500000
3000000
Deaths and Age-adjusted Death Rate for 1999
All Causes Deaths
All Causes Age-adjusted Death Rate
Alzheimer's Disease Deaths
Alzheimer's Disease Age-adjusted
Death Rate
Cancer Deaths
Cancer Age-adjusted Death Rate
Year
Number of People
By taking a one year assessment of deaths and age-adjusted death rate, we are able to recognize
the presence of outliners. This outliners are considerably large which means they have a
significantly impact on any regression model devised for the purposes of prediction and
estimation. If there is an assumption that states that the means for cancer and Alzheimer's are
equivalent across the 16 year interval. By performing a hypothesis analysis this statement can
either be rejected or not reject.
H0 (null hypothesis): The means for Alzheimer's and Cancer are equal i.e. μ1=μ2

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H1 (alternative hypothesis): The means for Alzheimer's and cancer are not equal i.e. μ1 ≠ μ2
Using the in-built assessment tool in Microsoft Excel for Z-test of means for two samples, we
can assess the hypothesis to get the appropriate results. The assessment yielded the following
findings. We will not reject the null hypothesis given the p-value of the Z-score is less than alpha
(0.05). As such, we do not reject that the death means for Alzheimer's and cancer are equal.
z-Test: Two Sample for Means
Deaths(Alzheimer's) Deaths(Cancer)
Mean 2844.590498 21824.65611
Known Variance 106981483.1 5974145990
Observations 884 884
Hypothesized Mean
Difference
0
z -7.236553684
P(Z<=z) one-tail 2.30149E-13
z Critical one-tail 1.644853627
P(Z<=z) two-tail 4.60298E-13
z Critical two-tail 1.959963985
Interpretation
From the findings of data set 1 we can make the following observations. The fact that
black males have the lowest average life expectancy and considerably high age-adjusted death
rate indicates that they facing numerous hardships that put their lives at risk. Contrary to this,

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white females have a very high average life expectancy and significantly low age-adjusted death
rate. This indicates that this group of the American population is well protected and sheltered
from situation, events, and occurrences that would put their lives at risk or lead to death. The
lowest ever recorded average life expectancy was roughly 29 years which was observed amongst
black males7. This means at a given time in the past 115 years ; black men were exposed to so
many challenges that it was difficult for a significant proportion of them to make it into their
thirties. On the opposite extreme, the highest ever documented average life expectancy was
roughly 81.4 years amongst white females. In this case, we see that Caucasian women at some
point in the past century were privileged enough to live to the ripe old age of eighty.
It is no surprise that the highest age-adjusted death rate ever recorded according to the
data is associated with black males. This is expected given the fact that they have the lowest
average life expectancy. Similarly, the fact that the group with the lowest age-adjusted death rate
is that of all females in the United States of America disregarding race is expected give the role
played by women in the past century. Women were often restricted from working dangerous jobs
or were force to stay home to raise children where it was safe. Majority of the deaths that
followed men were mostly influenced by racial tension, wars, and hostile working conditions.
According to the analysis finding 95% of black men do not live to see the age of 71. Which is ten
and five years shorter compared to that of white women and white men respectively. The time
series plot for the average life expectancy data indicates improvement in longevity of life for
individuals of all races. This could be as a result of advancement in healthcare, improved
nutrition, better working conditions, reduced war campaigns, and overall socio-economic
7 Xin Yan, Linear Regression Analysis: Theory and Computing (Singapore: World Scientific,
2009), 1–348.

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stability. The graph does however illustrate that black men still have the lowest life expectancy
because of gang related activities, police brutality, drug-related conflict, and poor quality of life.
The graph for age-adjusted death rate indicates a reduction across the board for all individual.
Nevertheless, black males still hold the highest age-adjusted death rate as indicated by their
graph line. The death rate amongst females still remains considerably low compared to the other
groups indicating a distinction between the activities performed by men and women. This is to
mean that women tend to stay away from activities that put their lives in danger e.g. sky diving,
paragliding, and mining. The correlation findings are very informative they prove that all
population were to a degree subjected to the same social, political, and economic situations with
only minute differences. As such, we do not see an increment in women death being
accompanied by a decrement in male deaths. There is however an inverse relationship between
average life expectancy and age-adjusted death rate. When individuals are put in situations and
provided with means that allow them to better their quality of life they will undoubtedly increase
their life expectancy which will in turn diminish age-adjusted deaths. Lately we can conclude
based on our regression analysis; that an increment in the average life expectancy of women and
men or either party will positive impact that of the entire population8.
From the findings of data set 2, we can see that diseases of the heart are responsible for
majority of the mean deaths observed in the United States of America between 1999 and 2015.
This could be as a result of poor nutrition habits or increased causative agents in the localities of
susceptible Americans. The second highest cause of deaths is cancer, with average figures of
above 21,000. More and More people in the United States of America are being diagnosed with
8 Books Llc, Regression Analysis: Linear Regression, Autocorrelation, Least Squares, Linear
Model, Overfitting, Optimal Design, Linear Least Squares (Memphis: general books llc
publisher, 2011), 1–152.

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cancer. This has led people to believe that the ailment is tried in with nutrition, quality of water,
proximity to industrial plants, and usage of some modern gadgets. Deaths associated with
unintentional injuries are considerably indicating increased carelessness and disregard for human
life amongst the American population. The results of the correlation matrix of all the diseases (in
appendix) indicate that Deaths for different disease causes has strong positive correlation with
each other. As such, an increment in Cancer deaths will be accompanied by increment in
Alzheimer deaths. Aged-adjusted death rates for different disease cause have very weak positive
correlations with each other; so much so, that their relationship can be ignore. Likewise a very
weak negative correlation is observed between deaths and age-adjusted death rate between
different diseases9.
The regression analysis proves that the death figure of one disease (Alzheimer's) can be
used to predict another disease's deaths numbers. The regression model used to assess this
assumption showed that the model was significant at alpha equivalent to 0.05; moreover, the
adjusted R square was considerably large. This implies that 87% of the values of the explanatory
variable can be predicted using the independent variable. It is however necessary to mention that
the presence of outliners does indeed damage the reliability and accuracy of these regression
models. The hypothesis test did not affirm that the means of the two (cancer and Alzheimer's)
were equal but laid a basis that they were not significantly different10. As such, this finding
provides a foundation on which to assess whether there is significant correlation between the two
diseases.
9 George A. F. Seber, and Alan J. Lee, Linear Regression Analysis (Hoboken: John Wiley &
Sons, 2012), 1–582.
10 Leonard Gaston, Hypothesis Testing Made Simple (New York: Leonard Gaston Ph.D, 2014),
1–167.

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Recommendations
Based on the findings from data set 1, I would advocate for the empowerment of black
males through provision of quality education, access to affordable healthcare, and opportunities
to work high paying jobs. By so doing, numerous individuals will be turned away from activities
that led to gang related violent, unemployment, and considerable interactions with law
enforcement agencies. Moreover, the government can streamline policies in place to better the
lives of black people without stripping away resources from white people. This ensures that both
racial groups enjoy a win-win situation that will foster peace coexistence. Lately, I propose for
the improvement of gender roles through the presentation of job recruitment opportunities to all
individuals regardless of gender or race. Women are still shielded from undertaking in given
activities simple because they are predominately performed by men. One major recommendation
I would make with regards to results presented through the analysis of data set 2; would have to
be the increment of awareness campaigns to educate people on the dangers of heart related
complication and informative school programs to teach kids on everyday habits and activities
that put them at risk of developing heart diseases in future.
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First Name: 27
Appendix
Cancer Chronic Liver Disease and Cirrhosis
Deaths Age-adjusted Death
Rate
Deaths Age-adjusted Death Rate
Mean 21824.6561
1
181.1807692 1180.62669
7
9.49479638
Median 8171.5 181.2 417 9.05
STD 77292.5998
4
20.32010349 4243.32389
2
2.359505501
Variance 597414599
0
412.906606 18005797.6
6
5.56726621
Skewness 6.79566480
1
-0.062327522 6.93273348
3
1.569940229
Maximum 595930 241.4 40326 24.8
Minimum 633 124.7 37 5
Range 595297 116.7 40289 19.8
5% Percentile 1154.55 148.015 60 6.7
95% Percentile 40050.8 212.685 2637.6 13.67
CLRD Diabetes Diseases of Heart
Deaths Age-adjusted
Death Rate
Deaths Age-adjusted
Death Rate
Deaths Age-adjusted
Death Rate

First Name: 28
Mean 5158.665 44.73472851 2797.106 23.58812217 24750.96 201.7141403
Median 1947.5 45.15 1108 23.2 9097.5 195.1
STD 18310.85 9.070813888 9914.207 4.937907233 87925.06 44.60735423
Variance 3.35E+08 82.27966459 98291498 24.38292784 7.73E+0
9
1989.816051
Skewness 6.872368 -0.154256079 6.802208 0.591224557 6.817574 0.562139288
Maximu
m
155041 75.6 79535 42.3 725192 347.4
Minimum 126 15.6 67 11.4 563 116.5
Range 154915 60 79468 30.9 724629 230.9
5%
Percentile
280.45 30.5 152 15.6 1250.6 142.23
95%
Percentile
9646.4 59.84 5232.45 32 50214.8 285.325
Essential Hypertension Homicide Influenza and
Pneumonia
Deaths Age-
adjusted
Death Rate
Deaths Age-
adjusted
Death Rate
Deaths Age-
adjusted
Death Rate
Mean 954.7083 7.317680827 676.516
6
5.797924298 2234.009 18.66346154
Median 343 7.1 200 5.4 778.5 18.4
STD 3445.602 2.070495447 2394.90 3.695676862 7955.689 4.868116833

First Name: 29
8
Variance 11872173 4.286951397 5735585 13.65802747 63292984 23.6985615
Skewness 7.010448 1.176113568 6.70476
2
3.315690403 6.838919 0.220554322
Maximum 32200 17.6 20308 36.3 65681 32.5
Minimum 12 2.6 10 1.4 36 7
Range 32188 15 20298 34.9 65645 25.5
5% Percentile 41 4.5 17 2 99.75 10.7
95% Percentile 2001 11.1 1415.4 10.9 4693 27.1
Kidney Disease Parkinson's Disease
Deaths Age-adjusted Death
Rate
Deaths Age-adjusted Death
Rate
Mean 1714.783 14.20294118 787.9163 6.791762014
Median 601.5 13.9 287 6.8
STD 6091.974 4.755535888 2837.694 1.276647549
Variance 37112147 22.61512158 8052505 1.629828965
Skewness 6.910614 0.228928439 7.143097 0.163153441
Maximum 50476 27.8 27972 12
Minimum 21 2.6 15 3.1
Range 50455 25.2 27957 8.9
5% Percentile 56 6.8 35 4.765
95% Percentile 2979.45 22.3 1563.85 8.9

First Name: 30
Pneumonitis (S/L) Septicemia Stroke
Deaths Age-adjusted
Death Rate
Deaths Age-adjusted
Death Rate
Deaths Age-adjusted
Death Rate
Mean 667.94 5.84368482 1344.986 11.05283447 5515.833 46.89909502
Median 255 5.8 458 10.7 2147.5 44.7
STD 2362.779 1.472644244 4775.082 4.652703303 19638.08 11.52316094
Variance 5582726 2.16868107 22801404 21.64764803 3.86E+08 132.7832381
Skewness 6.870515 0.199228242 6.846153 0.361643909 6.899617 0.544315908
Maximu
m
19803 11 40773 24.8 167661 83.4
Minimum 10 1.3 17 2.7 157 25.6
Range 19793 9.7 40756 22.1 167504 57.8
5%
Percentile
29 3.4 43.15 4.7 252.15 31.215
95%
Percentile
1120.5 8.4 2353.2 19.295 10213.5 68.04
Suicides Unintentional Injuries
Deaths Age-adjusted Death
Rate
Deaths Age-adjusted Death
Rate
Mean 1368.502 13.09966063 4562.24 42.27217195
Median 526 12.6 1826 41.4
STD 4885.381 3.852999581 16232.89 9.92093748
Variance 23866951 14.84560577 2.64E+08 98.42500049

First Name: 31
Skewness 6.99762 0.622847377 6.94754 0.412862331
Maximum 44193 29.6 146571 77.9
Minimum 23 3.8 145 19.6
Range 44170 25.8 146426 58.3
5% Percentile 89.15 7.1 289.15 26.615
95% Percentile 2853.95 20.3 9086.9 59.755