Business Statistics Lecture Notes 01

Business Statistics Lecture Notes 01 - MN1025 – Business...

Info iconThis preview shows pages 1–2. Sign up to view the full content.

View Full Document Right Arrow Icon

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
This is the end of the preview. Sign up to access the rest of the document.

Unformatted text preview: MN1025 – Business Statistics 1 Lecture 1—Friday 11/1/2008 SAMPLES and POPULATIONS: DESCRIPTIVE STATISTICS Reference: Lind et al. , Chapters 1,2,3,4 (see reading list on moodle.rhul.ac.uk for full book title). 1.1 Practical matters Lecturer: Prof. R. Schack, office C245 See Moodle (http://moodle.rhul.ac.uk) for • General course information • Office hours • Workshop times and registration • Reading list • Past exam papers • Lecture notes and worksheets • How to get Minitab Each week, the new worksheet will be sent to your college email address . Please keep the deadlines, as they will be strictly enforced. You should always attend the workshop for which you are registered. 1.2 Sampling STATISTICS: this means analysing data to obtain information with a view to making decisions on the basis of the data. So we have DATA-→ INFORMATION-→ DECISION The basic idea: data obtained are usually a sam- ple which (if chosen randomly) is representative of a larger population. We need to know how to make statements about the population based on what we see in the sample. Having got a large sample we must first organise the data to see the information clearly. So: first we deal with the topic of Descriptive Statistics—setting out data in a way that reveals as much information as possible. 1.3 Example: TV viewing figures A sample of persons/households is examined and for each programme a deduction is made about the total number of viewers, their social class, etc. (for exam- ple, for advertising purposes). Here the population is all persons/households in the UK; the sample is a manageable number chosen in some way so as to be representative. This is how TV viewing figures are obtained in practice. 1.4 Example: Quality control Every 50th item off a production line is removed. Together these provide a daily sample which is ex- amined for quality. From the results we wish to make an estimate of the quality of the day’s production (= population) as a whole. 1.5 Reasons for sampling (i) Sometimes examining a whole population is im- practical: for example we might want to get people’s opinions—if we tried to interview everyone it would take so long that those first interviewed might have changed their minds. A sample lets us get a “snap- shot” of opinions. (ii) Sometimes examining a whole population is too expensive. (iii) Some tests destroy the object being tested (safety tests on cars in crashes, lifetimes of light bulbs), so we must sample. 1.6 Example: R&D expenditure The data in this example are percentages of revenue spent on research and development by US computer firms (from McClave et al. , Statistics for Business and Economics). We get a jumble of numbers which we will organise and set out in ways which display more information....
View Full Document

This note was uploaded on 04/17/2008 for the course MN 1025 taught by Professor Schack during the Spring '08 term at Royal Holloway.

Page1 / 4

Business Statistics Lecture Notes 01 - MN1025 – Business...

This preview shows document pages 1 - 2. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online