NYU Quantitative Analysis of GDP and Obesity Report

User Generated

yrbFzvgu

Business Finance

Description

report editing

report editing

report editing

report editing
report editing
report editing

report editing




Unformatted Attachment Preview

T34_Group_643 A: I know technically it wasn’t asked for in the assignment brief but include a proper title to add realism to the report B: Solid introduction, but I would give a bit of 1-2 sentence background to the pandemic too to sort of introduce the topic a bit more. C: One thing that stands out to me about this report is that you’re missing any visuals and tables. It’s really important to include these so the reader gets an idea of what the data looks like! I see you put them in an excel file, but please include them in the main report for the final submission. Example tables you can use for summary statistics (you NEED these for the final report): World Africa GDP Asia Europe North America … Mean Median Standard deviation Min Max … And then for correlations, something like this: Correlations between GDP and Obesity Continent Africa Asia Europe … Correlation And remember to include the graphics like histograms and scatter plots!!! These will help a lot with showing the reader what the data looks like, and to be honest most of the lost marks for you will be because these were missing. D: Some decent commentary but would be a lot better if you had the table of statistics and graphics to refer to. I would also try to interpret this in the context of the data; i.e what does positive skewness mean for the wealth of the world? Etc. E: I would probably add headings to better break up where in the document you’re talking about CFR, GDP, Obesity, etc. Visuals like histograms and tables can also serve as good separators. - Additionally, talk about the differences by continent when discussing these variables too. E.g what continent has the highest mean GDP? Etc etc. F: I wouldn’t say it’s that weak. Additionally, explain whether this result make sense or not. Would you expect obesity/GDP to have a high correlation? - For the final report, definitely looking for more discussions and interpretations of the statistics you find. G: I’m actually not sure this is true. The correlation between CFR and Obesity is quite low. Further, why not just look at the correlation coefficient to determine this? H: No need to describe how you generated the statistics I trust you are very capable. I: Don’t usually talk about skewness in scatter plots; it’s more of a histogram thing. J: Also not sure this is true; I believe the relationship in this dataset is low GDP > low obesity (because of the positive correlation). K: In the conclusion section, try to sum up more of your report and provide some recommendations Overall: - - Good job! A solid effort For the final report please include the scatter plots and histograms with the main report! I was a bit confused as to which plots you were referring to during your report. o If you don’t put the plots etc in the final report, you will be penalized. I didn’t do it for the draft, but just make sure you’re including them on the final report. Try to include some more discussion of your statistics that you find. Do they make sense to you? What do they imply? I look forward to reading the final report Running head: QUANTITATIVE ANALYSIS 1 QUANTITATIVE ANALYSIS Name Institution Tutor QUANTITATIVE ANALYSIS 2 Introduction Quantitative methods allow the analysis of collected data to interpret the trends and relationships between them using certain statistical tests. This analysis report has data from five categories i.e., country, continent, the GDP per capita in USD, the percentage population classified as obese, and case fatality rate due to covid 19. Country Level Distribution of GDP per capita From the statistical summaries carried out for the GDP as shown in table 1 below, the data includes central tendency, dispersion, distribution properties, sum, and count. The generated statistic of the country-level GDP indicated that the mean and median of GDP are $14792.48 and $6072.2. This data is skewed to the right because value of skewness being greater than 0 and as a result a good measure of central tendency would be done using median. This is because, for skewed data distribution, the median is minimally affected even with extremities of some values. The minimum and maximum GDP are $381.30 from Malawi and $81734.5 from Luxembourg. For measures of dispersion the standard deviation, range and sample variance are $19330.27, $81353.2, and 3.74 respectively. QUANTITATIVE ANALYSIS 3 Summary statistics of GDP per capita Mean Standard Error Median Mode Standard Deviation Sample Variance Kurtosis Skewness Range Minimum Maximum Sum Count 14792.4813 1742.95153 6072.2 #N/A 19330.26757 373659244.2 2.258169831 1.758094744 81353.2 381.3 81734.5 1819475.2 123 Table 1:Summary Statistics of GDP per capita Country Level distribution of rates of Obesity From table 2, obesity has a mean percentage of 18.78% with a median and mode of 21.2% and 20.2% respectively. The minimum and maximum percentages of obesity recorded were 2.1 % and 37.9% from Vietnam and Kuwait respectively. In this case, the data is skewed to the left and the best way to measure central tendency would be to use median. QUANTITATIVE ANALYSIS 4 Summary statistics of Obesity Mean Standard Error Median Mode Standard Deviation Sample Variance Kurtosis Skewness Range Minimum Maximum Sum Count 18.78211 0.823354 21.2 20.2 9.131436 83.38312 -1.07573 -0.11193 35.8 2.1 37.9 2310.2 123 Table 2:Summary statistics of rates of obesity in different countries. Relationship between GDP per capita and obesity rate. To figure out the relationship between these two data groups, the data was represented graphically using a scatter plot. From the scatter plot in fig 1 below, it is noticed that for very low GDP, there was a high cluster of data collected for obesity but they were distributed between the greater percentages of obesity and the lesser percentages. However, as the GDP per capita increased, there was a high percentage of people with obesity rather than a low percentage. This means that obesity rate is increased in high prosperity countries. QUANTITATIVE ANALYSIS 5 Scatter plot of country level GDP against Obesity 40 35 Obesity 30 25 20 15 10 5 0 0 10000 20000 30000 40000 50000 60000 70000 80000 90000 GDP per Capita Figure 1:Scatter plot showing the relationship between GDP per Capita and Obesity rate Relationship between GDP per capita and obesity rates A correlation coefficient was calculated to find out how strongly the two variables relate to each other; the correlation coefficient was found to be 0.4028. This shows a weak positive correlation. Meaning that as the GDP increases, the percentage of people living with obesity increases but the correlation is weak meaning this relationship has other variables. To investigate the average rates of distribution of obesity and GDP per capita in each continent, the below histograms were constructed. QUANTITATIVE ANALYSIS 6 Average of GDP per capita by Continent Average GDP per capita 40000 35000 30000 25000 20000 15000 10000 5000 0 Africa Asia Europe North America Oceania South America Continents Figure 2: Average distribution of GDP in different continents Average rate of obesity per continent Average of obesity 30 25 20 15 10 Total 5 0 Africa Asia Europe North America Oceania South America Continents Figure 3:Average distribution of Obesity in different continents Variation of the relationship among continents To investigate how the relationship between GDP and obesity rate varies among continents, the correlations of the different continents were calculated and graphically represented in figure 2. Oceania, north America and south America had the strongest positive QUANTITATIVE ANALYSIS 7 correlation between the variables. Asia and Africa had weak positive correlations of 0.42 and 0.28 respectively. Lastly Europe had a weak negative correlation coefficient of -0.06 meaning that as GDP increases, obesity rates increase. Relationship between GDP per capita and Obesity rates for different continents 1 0.8 0.6 0.4 0.2 0 Africa -0.2 Asia Europe North America Ocenia South America Figure 4: Histogram comparing relationship between GDP per capita and obesity rate in different cotinents Country Level Variations of COVID 19 Fatality Rates. The mean, median, and mode for the COVID 19 mortality rates are 2.022, 1.7, and 1.3 respectively. The data is skewed to the right meaning more data falls on the right of the distribution curve. QUANTITATIVE ANALYSIS 8 Descriptive summary statistics of CFR Mean Standard Error Median Mode Standard Deviation Sample Variance Kurtosis Skewness Range Minimum Maximum Sum Count 2.022764228 0.120628618 1.7 1.3 1.337836093 1.789805411 5.057993562 1.816339689 8.6 0 8.6 248.8 123 Table 3: Overall descriptive summary statistics of CFR for all GDP per capita Because of the skewness of the data, it is best analyzed using the median. So, a summary statistic of the data that falls above and below the median of the GDP were found as shown in table 3 below: CFR summary statistics of data below GDP median Mean Standard Error Median Mode Standard Deviation Sample Variance Kurtosis Skewness Range Minimum Maximum Sum Count 2.2 0.159541693 1.9 1.5 1.246060459 1.552666667 1.162319144 1.16315905 5.6 0.5 6.1 134.2 61 CFR summary statistics of data above GDP median Mean Standard Error Median Mode Standard Deviation Sample Variance Kurtosis Skewness Range Minimum Maximum Sum Count Table 4: CFR summary statistics of data below and above GDP median 1.848387097 0.179159297 1.6 2.5 1.410701713 1.990079323 8.772546224 2.44579067 8.6 0 8.6 114.6 62 QUANTITATIVE ANALYSIS 9 Both tables show that all the data on COVID 19 fatality rates both below and above the median of the GDP per capita in a country level is skewed to the right and the range between the smallest value and the largest is greater below the GDP. CFR Scatter plot of CFR against GDP per Capita 10 9 8 7 6 5 4 3 2 1 0 0 10000 20000 30000 40000 50000 60000 70000 80000 90000 Gdp Per Capita Figure 5: Scatter plot of CFR against GDP per capita on a country level From the data, a relationship between COVID 19 fatality rates and obesity in each country was observed and there were more fatalities for higher percentages of obesity in a particular country. A histogram of the same data further highlighted that the data was bimodal. QUANTITATIVE ANALYSIS 10 CFR Scatter plot of CFR against percentage of obesity in a country 10 9 8 7 6 5 4 3 2 1 0 0 5 10 15 20 25 30 35 40 Obesity Figure 6: Scatter plot of CFR against obesity rates on a country level Figure 7: A histogram of CFR against obesity Variations per continent From the histogram, South America, North America and Europe had medians above the CFR of all the 123 countries combined while Africa, Oceania and Asia were below that value. South America had the highest median with 2.5, followed by north America with 2.3 and Europe with 1.8. Africa, Asia and Oceania had 1.6, 1.3 and 1.1 respectively. The data is shown below: QUANTITATIVE ANALYSIS 11 CFR variations per continent 3 Median of CFR 2.5 2 1.5 1 0.5 0 Africa Asia Europe North America Oceania South America Continents Figure 8: Histogram of CFR variations per continent To show the relationship between the pre-existing conditions and Covid 19 fatality rates a histogram of correlation coefficients was constructed for each continent. In the figure below it can be seen that only Oceania has a positive correlation of 0.7 between GDP and CFR while the rest have a negative correlation. Correlation between GDP and CFR 0.8 0.6 Correlation 0.4 0.2 0 Africa -0.2 Asia Europe North America Ocenia -0.4 -0.6 Continents Figure 9: Correlation between prosperity and CFR across different continents South America QUANTITATIVE ANALYSIS 12 From the correlation plot below, Africa, north America and Oceania had positive correlation coefficients of 0.15, 0.06 and 0.34 respectively. These positive correlations are weak. Asia, Europe, and South America has negative correlations of -0.252873016, -0.172106188 and 0.489511111 respectively. Correlation between Obesity and CFR 0.4 Correlation coefficient 0.3 0.2 0.1 0 -0.1 -0.2 Africa Asia Europe North America Ocenia South America -0.3 -0.4 -0.5 -0.6 Continents Figure 10:Correlation between obesity and CFR across different continents Conclusion With all the analysis done above, it is recommended that more comprehensive tools are used to look determine the relationships between each data category and the variables that can affect these relationships which are not accounted for. Tips for the final submission: All described in the draft submission plus, Include a section with inferential statistical analyse (hypothesis tests, confidence intervals) to guide your investigation. Note that you are not allowed to make significant changes to the topic of your report. Slight changes to the topic are acceptable however with approval from your tutor. The guidelines below integrate many writing instructions to shows how they all fit together. Note that these guidelines only explain you the expectations. You should pay attention to the structure of a written report and decide how you would like your report divided into sections. Each section should serve a distinct purpose.
Purchase answer to see full attachment
User generated content is uploaded by users for the purposes of learning and should be used following Studypool's honor code & terms of service.

Explanation & Answer

Please view explanation and answer below.

Running head: QUANTITATIVE ANALYSIS OF GDP, AND OBESITY AND THEIR
RELATIONSHIP TO CFR

QUANTITATIVE ANALYSIS OF GDP, AND OBESITY AND THEIR RELATIONSHIP TO
CFR
Name
Institution
Tutor

1

QUANTITATIVE ANALYSIS OF GDP, AND OBESITY AND THEIR RELATIONSHIP TO
CFR
Introduction
Quantitative methods allow the analysis of collected data to interpret the trends and
relationships between them using certain statistical tests.
Covid 19 is an infectious disease that is believed to have originated in China and is
transmitted through droplets when an infected person coughs, sneezes or exhales. This virus
quickly spread around the world creating a global pandemic; crippling economies all around the
world. The extent of the how hard the different countries took the hit could be liked to how fast
the people adapted to the precautionary measures that were set but also other factors came in
play like the GDP, and obesity levels.
This analysis report has data from five categories i.e., country, continent, the GDP per
capita in USD, the percentage population classified as obese, and case fatality rate due to covid
19 and aims to investigate the relationship between them.

GDP
Country Level Distribution of GDP per capita
From the table below, the summaries show that the Oceania has the highest mean GDP
per capita of $34,368.77 and the continent with the lowest GDP per capita was Africa of
$2451.459. The other statistical measures can be seen below.
From the summaries of the entire world, it can also be seen that the minimum and
maximum GDP are $381.30 from Malawi and $81734.5 from Luxembourg.
For measures of dispersion the standard deviation, range and sample variance are $19330.27,
$81353.2, and 3.74 respectively. The generated statistic of the country-level GDP indicated that
the mean and median of GDP are $14792.48 and $6072.2. This data is skewed to the right

2

QUANTITATIVE ANALYSIS OF GDP, AND OBESITY AND THEIR RELATIONSHIP TO
CFR

3

because value of skewness being greater than 0 and as a result a good measure of central
tendency would be done using median. This is because, for skewed data distribution, the median
is minimally affected even with extremities of some values.

GDP
World

Africa

Asia

Mean
14792.4813 2451.459 14899.69
Standard
Error
1742.95153 433.2252 3615.999
Median
6072.2
1240.8
5834.2
Mode
#N/A
#N/A
#N/A
Standard
Deviation
19330.26757 2705.49
18789.28
Sample
Variance
373659244.2 7319678 3.53E+08
Kurtosis
2.258169831 3.345431 2.451552
Skewness
1.758094744 1.93936
1.783364
Range
81353.2
10827
65695
Minimum
381.3
381.3
493.8
Maximum
81734.5
11208.3
66188.8
Table 1: Summary Statistics of GDP per capita

Europe

North
America

Oceania

South
America

28347.69

16817.39

34368.77

8046.338

3958.784
20324.3
#N/A

5249.329
8821.8
#N/A

16322.28
42949.9
#N/A

1413.591
6220.85
#N/A

22741.48

18926.73

28271.02

3998.238

5.17E+08
-0.11067
0.923313
78637.7
3096.8
81734.5

3.58E+08
2.013768
1.703376
60490.7
2505.8
62996.5

7.99E+08
#DIV/0!
-1.24005
54553.6
2801.4
57355

15985909
1.128043
1.231395
12376.2
3548.6
15924.8

To show the average distribution of GDP per capita for each continent and to compare
them to each other a histogram below was drawn and as can be seen the results match the ones
from the table above with Oceania having the highest average GDP per capita.

QUANTITATIVE ANALYSIS OF GDP, AND OBESITY AND THEIR RELATIONSHIP TO
CFR

4

Average of GDP per capita by Continent
Average GDP per capita

40000
35000

30000
25000
20000
15000
10000
5000
0
Africa

Asia

Europe

North
America

Oceania

South
America

Continents

Figure 1: Average distribution of GDP in different continents

OBESITY
Country Level distribution of rates of Obesity
The continent with the highest obesity rate can be observed from table 2 to be Oceania
with a mean of 27.03333.From table 2, obesity has a mean percentage of 18.78% with a median
and mode of 21.2% and 20.2% respectively. The minimum and maximum percentages of obesity
recorded were 2.1 % and 37.9% from Vietnam and Kuwait respectively. In this case, the data is
skewed to the left and the best way to measure central tendency would be to use median.

QUANTITATIVE ANALYSIS OF GDP, AND OBESITY AND THEIR RELATIONSHIP TO
CFR
OBESITY
World

Africa

Asia

Europe

Mean
18.78211
11.86667 17.75556 22.99394
Standard
Error
0.823354
1.190081 2.348275 0.485509
Median
21.2
8.9
16.6
22.4
Mode
20.2
10.3
#N/A
23.1
Standard
Deviation
9.131436
7.432055 12.202
2.789034
Sample
Variance
83.38312
55.23544 148.8887 7.778712
Kurtosis
-1.07573
1.934492 -1.53251 2.677237
Skewness
-0.11193
1.664163 0.245067 1.182689
Range
35.8
28
35.8
14.2
Minimum
2.1
4.5
2.1
17.9
Maximum
37.9
32.5
37.9
32.1
Table 2:Summary statistics of rates of obesity in different countries

North
America

Oceania

South
America

26.36154

27.03333

23.175

1.189243
24.7
24.6

2.913379
29
#N/A

1.321491
21.2
20.2

4.287878

5.046121

3.737742

18.3859
0.885746
1.007208
15
21.2
36.2

25.46333
#DIV/0!
-1.48742
9.5
21.3
30.8

13.97071
-1.94642
0.611669
8.4
19.9
28.3

To get a visual representation of the average distribution of rates of obesity around the
world a histogram was plotted as can be seen below:

Average rate of obesity per continent

Average of obesity

30
25
20
15
10

Total

5
0

Africa

Asia

Europe

North
America

Oceania

Continents

Figure 2:Average distribution of Obesity in different continents

South
America

5

QUANTITATIVE ANALYSIS OF GDP, AND OBESITY AND THEIR RELATIONSHIP TO
CFR

6

Relati...


Anonymous
Awesome! Made my life easier.

Studypool
4.7
Trustpilot
4.5
Sitejabber
4.4

Similar Content

Related Tags