Hand Out Chapter 1-2.PDF

7/31/2019 Hand Out Chapter 1-2.PDF

1/18

DIRECTORS OF A COMPANY


2/18

GOVERNMENT


3/18

ACADEMIC INSTITUTES


4/18

INDIPENDENT SURVEYORS


5/18

1. Statistics is concerned with scientific procedures and

methods for collecting, organizing, summarizing,

presenting and analyzing data, as well as obtaining

useful information, drawing valid conclusion and

making effective decisions based on the analysis.

2. The basic steps in statistical problem-solving are

outlined below.

a. Identifying the problem or opportunity.b. Gathering available facts.

c. Gathering new data.

d. Classifying and organizing data.

e. Data presentation.

f. Making decision.

3. Statistical techniques can be divided into two

categories:

a. Descriptive statistics

b. Inferential (or inductive) statistics.

4. Descriptive statistics: involves data that are

compiled, organized, summarized and presented in

suitable visual forms which are easy to understand and

suitable for use.Various tables, graphs, charts and diagrams are used to exhibit

the information obtained form the data.


6/18

5. Inferential statistics: In inferential statistic, we take

generalizations about a population by analyzing

samples. If a sample is a good representation of a

population, accurate conclusions about the populationcan be inferred from the analysis of the sample.

{inferred = guess or interpretation}

6. Population and sample

Population: is used to designate the complete setof items that are of interest in the research.

Sample: The term Sample is used to designate a

subset of items that are chosen from the population.

A set of items selected

form the population

(sample). Hence, the

sample is a subset of t

population.A set of all items

(population)


7/18

7. Statistic: A summary measure such as mean,

median, mode, or standard deviation, computed form

sample data is called a statistic.

9. Parameter: A summary measure for the entire

population.

10. Census: A study that measures a variable for every

unit in the population is called Census.

11. A sample survey : involves a subgroup (or sample)

of a population being chosen and questioned on a set of

topics.

12. A pilot study is a study done before the actual field

work is carried out.


8/18

DATA

Data is a measure on variables of interest obtained

from a sample.

Example: researchers may collect data on the amount

of money spent by secondary school students on

textbooks, the brand of detergents most preferred by

housewives in Seremban, the monthly income of rubber

smallholders in Malaysia, the average length of stay of

foreign tourists in Malaysia and their favorite places of.

Data can be obtained from primary as well as secondary

sources.

PRIMARY DATA Researchers collect primary data from

primary sources or from samples. For example, a

researcher interviews the respondents and records their

responses. A researcher may go to a supermarket and

observe the buying habits of the public during festive

seasons.


9/18

SECONDARY DATA Secondary data is normally

published data collected by other parties. For example

government agencies such as Bank Negara, the

Department of statistics, Ministry of International Trade

and Industry and other agencies publish their data

regularly and provide secondary sources of data to

many researchers.

In addition bulletins, journals, newspapers and other

publications also provide useful secondary data to

researchers.

VARAIBLES

A variable measures a characteristic of the population

that the researcher wants to study. For example,variables of interest may be the monthly income of

respondents, respondents age, gender, level of

education, number of children and type of house owned

by respondents.

VARAIBLES

Quantitative or Numerical Qualitative or AttributiveMeasure on Numerical scale Measured with non-numerical scale

Yields numerical response Yields categorical response


10/18

SCALE OF MEASUREMENT

Data can be divided into numerical and categorical data.

Numerical data contains numbers that we can

manipulate using ordinary arithmetical operations. For

example, counting the number of cars that pass through

a toll-booth for three consecutive days.

Categorical data can be sorted into categories. For

example, data on the marital status of respondents canbe classified into single married, widow or widower, or

divorced.

Usually, data are classified as nominal, ordinal, interval

or ratio.

Nominal- level date Ordinal- level data Interval- level data Ratio- level data

Zip codeGrade

(a,b,c,d)IQ Height

Gender(male,femaele)

Judging

(first place, second

place)

TemperatureaWeight

Eye color (blue, brown,

green, hazel)

Rating scale (poor,

good, excellent)SAT score Time

Major field( mathematics,

computer,etc)

Ranking of players ----------- Salary

Nationality ------------- ------------ Age


11/18

SAMPLING AND DATA COLLECTION METHODS

Sampling is the process of selecting a sample from a

population. The sample must be selected in such a way

so that it will accurately represent its population.

Sampling techniques:

Sampling techniques can be classified broadly into two

categories:

1. Non-probability sampling techniques.

2. Probability sampling techniques.

Non-probability sampling

techniques

Probability sampling

techniques.

Convenience

sampling

Judgmental

sampling

Simple random

sampling

Systematic

sampling

Snowball

samplingQuota sampling Cluster sampling

Stratified

sampling


12/18

Data collection methods

After the sample is identified and selected by using the

appropriate sampling technique is to determine the

best way to reach the respondents in order to obtain

the required data. There are several methods of

collecting data and each has its own advantages and

disadvantages. A researcher must choose the methods

that provide the most information at minimum cost.

The common method of collecting data is as follows.

1. Face to face interview.

2. Telephone interview.

3. Direct questionnaire.4. Mail or postal questionnaire.

5. Direct observation

6. Other methods (e-mail, video recording)


13/18

CHAPTER 3

DATA PRESENTATION

Survey show that the majority of shareholders prefer to

look at visual presentations of the companys

performance instead of listening to verbal reports to

evaluate performances before giving their votes.

Data presentation is an essential step before further

statistical analysis is warranted. Data are summarized

and displayed enabling researchers, managers and

decision-makers to observe important features of the

data.

Some common data presentations include frequency

table, bar chart, pie chart, histogram, frequency curve,

line graph, pictograph, stem-and-lead display, box plot

and ogive.

SEEING IS BELIEVING


14/18

Qualitative data can be classified intocategories or classes. They can best be presented in the

form of frequency distribution, bar chart, pie chart and

contingency table.

1. Frequency distribution or frequency table

{Frequency (occurrence, incident, rate, regularity)}One simple way of presenting qualitative data is by

frequency distribution. A frequency distribution is a

table consisting of columns and rows.

For example a car dealer in Kuala Lumpur makes the sales for

the flowing types of cars in the month of January 2010.

The first

column of the

table is qualitative variables that is the car models, while the

second column Is the frequency of the types of cars sold.

Car model Number of cars

Waja 66

Wira 50

Saga 39

Gen-2 25

Total 180


15/18

2. Pie chart

A pie chart can be used to represent Categorical data. It

consist of one or two circles that are divided intosectors. The sectors show the number of objects or

percentage of each group or category.

Guidelines for constructing a pie chart are as follows:

a. Choose a small number of categories (say 3-10)

b. Partition the circle to match the percentages for each

of the categories.

Example: NZ holdings current assets in RM (million) for

the year 2010 are given in the table below

Current asset RM (million)Stocks 1520

Cash 720

Others 860

A pie chart for the information above is constructed asfollows


16/18

Solution:

a. Number of categories = 3

b. Percentages ( to nearest whole number).

c. i). Stock= 1520/ (1520+720+860)x100=49%

ii). Cash = 720/(1520+720+860)x100=23%

iii). Others= 860/(1520+720+860)x100=28%

Convert the % in Degrees (total 360 ) before drawing.

49 %=176.4 , 23%=82.8, 28%=100.8

49%

23%

28%

RM (million)

Stocks Cash Others


17/18

3.Bar chart: A bar chart is another graphical method for

describing data that can be divided into categories. Bar

charts are frequently used in newspapers, magazines,

companies annual reports and other presentations to

convey and highlight information.

Guidelines for constructing a bar chart:

a. Label the vertical axis with the number of objects

that fall into each category; and label the categoriesalong the horizontal axis.

b. Construct a rectangle over each category with the

height of the rectangle equal to the number of objects

in that category. The base of each rectangle should be

of the same width.

c. Leave space between each category on the horizontal

axis to distinguish between the categories and to clarify

the presentation.

0

20

40

60

1ST 2ND 3RD 4TH

PROFIT RM (MILLLION)

PROFIT RM (MILLLION)


18/18

ORGANISING AND GRAPHING QUNATITATIVEDATA

Documents

Hand Out Chapter 1-2.PDF