Statistics

STATISTICS AND QUANTITATIVE METHODS

PRACTICAL CASE (Individual)

MBA 2010-2011

Story Name: TV, Physicians, and Life Expectancy.

Methods: Descriptive Statistics, Box and Whisker Plot, Correlation and Regression, Confidence Intervals and Hypothesis Testing.

Reference: http://mathforum.org/workshops/sum96/data.collections/datalibrary/data.set6.html

Description: Number of TV´s, number of Physicians and Male and Female life expectancy in 40 countries all over the world.

Number of cases: 40

Variable Names:

1.- Average Life Expectancy

2.- Male Life Expectancy

3.- Female Life Expectancy

4.- People per TV

5.- People per Physician

Television, Physicians, and Life Expectancy

Country Life expectancy People/TV People/ physician Female life expectancy Male life expectancy

Argentina 70,5 4 370 74 67

Bangladesh 53,5 315 6166 53 54

Brazil 65 4 684 68 62

Canada 76,5 1,7 449 80 73

China 70 8 643 72 68

Colombia 71 5,6 1551 74 68

Egypt 60,5 15 616 61 60

Ethiopia 51,5 503 36660 53 50

France 78 2,6 403 82 74

Germany 76 2,6 346 79 73

India 57,5 44 2471 58 57

Indonesia 61 24 7427 63 59

Iran 64,5 23 2992 65 64

Italy 78,5 3,8 233 82 75

Japan 79 1,8 609 82 76

Kenya 61 96 7615 63 59

Korea, North 70 90 370 73 67

Korea, South 70 4,9 1066 73 67

Mexico 72 6,6 600 76 68

Morocco 64,5 21 4873 66 63

Myanmar (Burma) 54,5 592 3485 56 53

Pakistan 56,5 73 2364 57 56

Peru 64,5 14 1016 67 62

Philippines 64,5 8,8 1062 67 62

Poland 73 3,9 480 77 69

Romania 72 6 559 75 69

Russia 69 3,2 259 74 64

South Africa 64 11 1340 67 61

Spain 78,5 2,6 275 82 75

Sudan 53 23 12550 54 52

Taiwan 75 3,2 965 78 72

Tanzania 52,5 * 25229 55 50

Thailand 68,5 11 4883 71 66

Turkey 70 5 1189 72 68

Ukraine 70,5 3 226 75 66

United Kingdom 76 3 611 79 73

United States 75,5 1,3 404 79 72

Venezuela 74,5 5,6 576 78 71

Vietnam 65 29 3096 67 63

Zaire 54 * 23193 56 52

Data Description

This data set contains the values of the life expectancy (for men, women and average) for 40 different countries. It also contains the number of people per TV and physician.

Without doing any statistic calculations, we can easily infer that there must be some kind of relation between the number of physicians per person and the life expectancy. We can also preview that there also has to be any relation between the number of TV´s per person and the number of physician per person because a higher number of TV´s per person will mean a higher number of physician per person too (both variables represent in any way the well-being state of a country).

However, we are going to develop some statistic methods in order to figure out what are the relations between the variables we have mentioned and see how strong are those relations.

Descriptive Statistics

We