Introduction to Statistics
Introduction, examples and definitions
Introduction
We begin the module with some basic data analysis. Since Statistics involves
the collection and interpretation of data, we must first know how to
understand, display and summarise large amounts of quantitative information,
before undertaking a more sophisticated analysis.
Statistical analysis of quantitative data is important throughout the pure and
social sciences. For example, during this module we will consider examples
from Biology, Medicine, Agriculture, Economics, Business and Meteorology.
Examples
Survival of cancer patients: A cancer patient wants to know the probability
that he will survive for at least 5 years. By collecting data on survival
rates of people in a similar situation, it is possible to obtain an empirical
estimate of survival rates. We cannot know whether or not the patient will
survive, or even know exactly what the probability of survival is. However,
we can estimate the proportion of patients who survive from data.
Car maintenance: When buying a certain type of new car, it would be useful
to know how much it is going to cost to run over the first three years from
new. Of course, we cannot predict exactly what this will be — it will vary
from car to car. However, collecting data from people who bought similar
cars will give some idea of the distribution of costs across the population
of car buyers, which in turn will provide information about the likely cost
of running the car.
Definitions
The quantities measured in a study are called random variables, and a
particular outcome is called an observation. Several observations are
collectively known as data. The collection of all possible outcomes is called
the population.
In practice, we cannot usually observe the whole population. Instead we
observe a sub-set of the population, known as a sample. In order to ensure
that the sample we take is representative of the whole population, we usually
take a random sample in which all members of the population are equally
likely to be selected for inclusion in the sample. For example, if we are
interested in conducting a survey of the amount of physical exercise
undertaken by the general public, surveying people entering and leaving a
gymnasium would provide a biased sample of the population, and the results
obtained would not generalise to the population at large.
Variables are either qualitative or quantitative. Qualitative variables have
non-numeric outcomes, with no natural ordering. For example, gender,
disease status, and type of car are all qualitative variables. Quantitative
variables have numeric outcomes. For example, survival time, height, age,
number of children, and number of faults are all quantitative variables.
المادة المعروضة اعلاه هي مدخل الى المحاضرة المرفوعة بواسطة استاذ(ة) المادة . وقد تبدو لك غير متكاملة . حيث يضع استاذ المادة في بعض الاحيان فقط الجزء الاول من المحاضرة من اجل الاطلاع على ما ستقوم بتحميله لاحقا . في نظام التعليم الالكتروني نوفر هذه الخدمة لكي نبقيك على اطلاع حول محتوى الملف الذي ستقوم بتحميله .
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