EPIDEMIOLOGY AND STATISTICS

Epidemiology and Statistics for Public Health



Epidemiology and Statistics for Public Health

Task 1

Mortality and Life Expectancy in Manchester

There are different rates of mortality, but all indicate the proportion of a particular population group or of the entire population dying during a given period, usually one year. For example, what is called gross annual mortality or crude death rate is the total annual number of deaths divided by the population size. The result is usually expressed per thousand inhabitants. In 1990 the crude death rate was 11.1 deaths in Greater Manchester area per thousand inhabitants, while in the U.S. was only 8.6 per thousand. That means that the proportion of Americans who died that year was less than the people of Manchester. Since death is "the worst symptom of poor health," mortality rates are often seen as indicators (inverse) health, but that only served with many limitations when comparing crude death rates. Because, for example, could be that in Greater Manchester area there are a lot older than in the U.S. and, like the old die more than the young, it makes crude mortality is higher in Greater Manchester area. In fact, that's what happens. So what we can do is to calculate the specific death rate at a certain age and if we want, in a single sex. For example, in Greater Manchester area in 1990 killed 2,573 women in the age group 55 to 64 years, which had a total of 430,000 women. Simple division tells us that the mortality rate in this group of Manchester women aged between 55 and 64 that year was 5.9 per thousand. In the U.S., the same year mortality in women in this age group was 8.8 per thousand that is much larger than the corresponding Manchester group. And that yes it can be taken as reliable data, at least in this age group, the health of Americans is worse than the people of Manchester.

Task 2

Descritive Statistics

The purpose of the descriptive statistics is the processing of empirical data, their classification, graphic representation in the form of graphs and tables, as well as their quantification by means of key statistical indicators. In contrast to inductive statistics, descriptive statistics do not make conclusions about the population based on the results of the study of particular cases. Inductive same statistics on the contrary suggests that the properties and laws identified in the study of objects of the sample, as are inherent in the general population.

Descriptive Summary of the Data

 

Age

Statistic exam mark

Hours spent practicing for exam

Mean

32.54166667

61.5

12.45833333

Standard Error

1.560354808

2.310185387

0.807526117

Median

33

60

12

Mode

34

55

12

Standard Deviation

7.644146193

11.31755082

3.956053882

Sample Variance

58.43297101

128.0869565

15.65036232

Kurtosis

-1.413723812

-0.384188311

0.187229005

Skewness

0.137327472

0.311552716

-0.340333705

Range

23

43

17

Minimum

22

42

3

Maximum

45

85

20

Sum

781

1476

299

Count

24

24

24

Confidence Level(95.0%)

3.227839829

4.778982555

1.670495038

Once you have collected the values ??taken by variables in our study (data), proceed to the descriptive analysis of them. For categorical variables such as sex or staging, we want to know the number of cases in each of the categories, usually reflecting the percentage they represent of the total, and expressing it in a frequency table.

Frequency Distribution of Student's Gender and Status

Gender

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

Female

15

62.5

62.5

62.5

Male

9

37.5

37.5

100.0

Total

24

100.0

100.0

The measures of centralization are to answer the first ...