QUESTION

 

Need a distinction on my maths assignment, this is the website that I need to get the data from. www.OCED.org.uk something like (Smoking and lung cancer, Annual reported crime rates and Annual police crime or Hours worked and Household spending)

I need 50 lots of data for both in two separate tables of data in them and then show the data in the forms of: standard deviation (mean, median, mode and range), scatter graphs, box plots and cumulative frequency.

It needs to look professional with a cover sheet and including, (Introduction, hypothesis, data collection, data analysis, summary, conclusion and references) Each of these needs explain underneath how you done it and your outcome. Need to have some subheadings maybe put the sum/number in red so they stand out.

ANSWER

 

INTRODUCTION

The following report tries to establish a correlation between the number of people who smoke and the number of people who die from cancer.

Smoking as we all are aware becomes a habit once a person starts getting used to it. It becomes an addiction because of the nicotine present in the tobacco. It has a lot of harmful effect and cancer is one of them. Cancer is a disease which happens because of abnormal growth in some body cells. It can happen in any part of the body. Smoking can primarily lead to lung cancer. Although with the advent of technology we have been able to cure this disease, but still large number of people die because of it.

Even today with large number of people dying because of cancer and smoking being one of its principal cause, and widely becoming a habit among the younger generation, makes it an interesting research topic.

HYPOTHESIS

Within the reach of my research I would like to prove that a relationship exists between the number of people who smoke regularly and those who die from cancer. For proving my hypothesis, I have gone for a secondary research methodology.

DATA COLLECTION

For proving my hypothesis, I have applied a secondary research methodology. This secondary data has been collected from the following website: www.OCED.org.uk. Two separate sets of data- Number of Smokers in the country (per 100000 person) and Number of deaths due to cancer (per 100000 person) were collected. For getting a proper and logical result, data of 41 countries were taken over the period of 2006-2016. The average of the 10-year data of each country was then taken for further analysis. The excel sheet attached would contain all the data which has been used in this research.

The following is the snapshot and average of the data which we have gathered and used for our research.

Number of Smokers Data

The data (average of 2006-2016 data) which has been shown below shows the number of people above 15 years of age who are addicted to smoking in these 41 countries.

Number of Smokers 15+ (2006-2016) per 100000 people
LOCATION	VALUE
AUS	14420
AUT	23800
BEL	19783
BRA	12860
CAN	15463
CHE	20450
CHL	27183
COL	16833
CRI	14422
CZE	22133
DEU	21500
DNK	21556
ESP	24950
EST	25994
FIN	18236
FRA	24560
GBR	19812
GRC	34892
HUN	26383
IND	12478
IRL	20444
ISL	14379
ISR	18389
ITA	21924
JPN	21436
KOR	22020
LTU	21167
LUX	17697
LVA	26917
MEX	10206
NLD	20855
NOR	17545
NZL	16529
POL	23550
PRT	18000
RUS	36317
SVK	21367
SVN	18917
SWE	12894
TUR	27444
USA	14439
ZAF	19667

Graphically it could be presented as:

NUMBER OF DEATHS DUE TO CANCER

The data (average of 2006-2016 data) which has been shown below shows the number of people above 15 years of age who are dying because of cancer in these 41 countries.

LOCATION	VALUE
AUS	203.04
AUT	208.84
BEL	219.30
BRA	164.25
CAN	217.80
CHE	188.50
CHL	206.45
COL	166.91
CRI	169.72
CZE	251.66
DEU	211.92
DNK	253.77
ESP	203.04
EST	258.75
FIN	185.12
FRA	215.29
GBR	231.88
GRC	205.95
HUN	302.66
IRL	240.37
ISL	209.84
ISR	189.73
ITA	213.95
JPN	192.31
KOR	202.20
LTU	251.32
LUX	211.37
LVA	266.20
MEX	121.35
NLD	241.73
NOR	212.74
NZL	222.25
POL	258.98
PRT	205.41
RUS	227.60
SVK	267.11
SVN	264.65
SWE	194.27
TUR	163.30
USA	201.03
ZAF	198.03

Graphically it could be presented as:

DATA ANALYSIS

The following statistical analysis was even carried on this data which have been explained below

MEAN

Mean tells you the average number of people in these 41 countries per 100000 person who are addicted to smoking. All calculations have been shown in the above excel sheet. For the above data the mean comes out to be 20472 people which approximately means 20.47% of the population on average are smokers.

Similarly, from the second set of data we see that the mean comes out to be 215.14 which approximately means 0.215% of the population on average die from cancer.

MEDIAN

Median gives us a true picture about the data than the mean as it is the middle value of the data. In the above data the median comes out to be 20447 people which approximately means 20.44% of the population on average are smokers.

Similarly, from the second set of data we observe that the median comes out to be 211.37 which approximately means 0.211% of the population on average die from cancer.

STANDARD DEVIATION

Standard Deviation is one of the commonly used statistical tool and provides a great understanding of the data. It helps in understanding the dispersion in the data. Standard deviation of this data is 5512, which implies that the values in the data are very far from each other. This implies that in some countries the number of smokers is more as compared to other countries.

From the data on deaths due to cancer we see that the standard deviation comes out to be 34.72 which implies that the data value of deaths due to cancer in most countries is quite close to each other.

RANGE

The range gives us the difference between the highest and lower values in the data. The larger the range value tells us that the data is spread through a wide region. From the first set of data the range value comes out to be 23839, which is a huge value.

From the second set of data we get the range to be 145, which is fairly a low value. Thus, the data is very congested together in this case.

CORRELATION

In statistics, correlation is a technique which helps us to understand the relationship or dependence of one variable with another variable. In this number of smokers is one variable and deaths due to cancer is the other variable. A correlation test led to the following result:

	Smokers	Deaths
Smokers	1	0.413215
Deaths	0.413215	1

This test helps us in understanding that there seems to be a linear relationship between both the variables: number of smokers and deaths due to cancer. A correlation of .413 implies that as the number of smokers increase, the death due to cancer will also increase and vice versa.

REGRESSION

In statistics regression analysis is the most commonly used technique in understanding the association between the data variables. For regression analysis, there is one data which is dependent and the other is said to be independent. So here we have assumed the number of smoker data to be independent and the deaths due to cancer to be a dependant variable. The following results got displayed with the above assumptions.

SUMMARY OUTPUT
Regression Statistics
Multiple R	0.413215147
R Square	0.170746757
Adjusted R Square	0.149483854
Standard Error	32.0180677
Observations	41
ANOVA
	df	SS	MS	F	Significance F
Regression	1	8232.279948	8232.279948	8.030265299	0.007248193
Residual	39	39981.10971	1025.156659
Total	40	48213.38966
	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%	Lower 95.0%	Upper 95.0%
Intercept	160.6487338	19.86762485	8.085955669	7.27106E-10	120.4626694	200.8347982	120.4626694	200.8347982
X Variable 1	0.002636511	0.000930389	2.833772274	0.007248193	0.000754621	0.0045184	0.000754621	0.0045184

The following line plot was also observed:

From the above result we observe that R square value comes out to be 0.17 which is a very small number which tells us that the actual data is very far from the regression line. This small value indicated a weak relationship between the two variables. Although this may not be true because regression has its own set of limitations.

CONCLUSION

From the results of the different statistical tools it has been very hard to establish that a strong correlation exists between the above two data. Since data has been collected from a secondary source there can some data issues as well. So, data collection has also been one of the limitations of this research. Even the statistical methods which have been employed all suffer from their own respective limitations.

The data that has been taken has a lot of variation because of which it has been very hard to prove our above hypothesis. Although we observed that there exists a positive correlation between the two data sets but still the R square value was very less which forces us to negate our hypothesis. So, we can conclude that although smoking is a bad habit but there is only a small percentage of people who are dying because of it.

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