INTRODUCTION TO

STATISTICS

DO WE NEED STATISTICS?

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WHAT IS MEANT BY STATISTICS?

Statistics is the science of collecting, organizing, presenting, analyzing, and interpreting numerical data to assist in making more effective decisions.

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TO COLLECT DATA FROM A SMALL

PART OF A LARGER GROUP

SO THAT WE CAN LEARN ABOUT

THE LARGER GROUP

GOAL OF STATISTICS

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•Observations that have been collected (e.g. measurements, survey responses)

Data

Statistics •A collection of methods for planning studies and experiments, obtaining data, and then organizing, summarizing, presenting, analyzing, interpreting, and drawing conclusions based on the data.

DEFINITIONS

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WHO USES STATISTICS?

Statistical techniques are used

extensively by marketing,

accounting, quality control,

consumers, professional sports

people, hospital administrators,

educators, politicians, physicians,

etc...

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•DESCRIPTIVE STATISTICS

•INFERENTIAL STATISTICS

Describe about sample’s characteristics

Make inferences about population from sample statistics

TYPES OF STATISTICS

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DEFINITIONS

Descriptive Statistics - methods of organizing, summarizing, and presenting data in an informative way.

EXAMPLE 1: A Gallup poll found that 49% of the people in a survey knew the name of the first book of the Bible. The statistic 49 describes the number out of every 100 persons who knew the answer.

EXAMPLE 2: According to Consumer Reports, General Electric washing machine owners reported 9 problems per 100 machines during 2001. The statistic 9 describes the number of problems out of every 100 machines.

Inferential Statistics: A decision, estimate, prediction, or generalization about a population, based on a sample.

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Descriptive statistic

method of organizing, summarizing and presenting data in a

convenient and informative way.

Use numerical data and graphical techniques.

Collect data

e.g., Survey

Present data

e.g., Tables and graphs

Characterize data

e.g., Sample mean =

iX

n

DESCRIPTIVE STATISTICS

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Method used to draw conclusions and inferences

about characteristics of population based on sample

data.

Estimation

e.g., Estimate the population mean weight using the sample mean weight

Hypothesis testing

e.g., Test the claim that the population mean weight is 120 pounds

INFERENTIAL STATISTICS

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There are 3 Key Concepts in Statistics

Population

Sample

Statistical Inference

KEY CONCEPTS IN STATISTICS

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Statistical inference

Is a process of making an estimate, prediction, or decision making about population based on sample data.

Population is very large, so statistic is used to investigate characteristic of the population.

Estimation from sample is not 100% correct.

To measure this, confidence level and significance level are used to determine reliability of the measurement.

STATISTICAL INFERENCE

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a b c d

ef gh i jk l m n

o p q rs t u v w

x y z

Population

Sample b c

g i n

o r u

y

Measures used to describe

the population are called

parameters

Measures computed from

sample data are called

statistics

POPULATION AND SAMPLE

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•is the complete collection of all elements (e.g. measurements, scores, people, etc.) to be studied.

A population

A census •is the collection of data from every member of the population.

A sample •is the subcollection of members selected from a population.

DEFINITIONS

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A population is a collection of all possible individuals, objects, or

measurements of interest.

A sample is a portion, or part, of the population of interest

POPULATION versus SAMPLE

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•is a numerical measurement describing a particular characteristic of a population.

A parameter

A statistic •is a numerical measurement describing a particular characteristic of a sample.

DEFINITIONS

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Population

Is a complete collection of all items (people, or things) of interest.

Measures of population are called parameters (characteristics of interest).

It is very large and sometime infinite.

POPULATION

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Sample

Is a set of data drawn from a population.

Measures of sample are called statistics.

Since populations are very big and sometime infinite, we make use of statistics to make inferences about parameters.

SAMPLE

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To systematically present information

To make conclusions about population

based on sample information

To make reliable forecasts

Information obtained are use to improve

system and processes

WHY USE STATISTICS?

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CAN YOU INTERPRATE THIS

GRAPH?

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HOW DO WE OBTAIN DATA?

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RESULT OF A MEASUREMENT

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A raw data is the data obtained

before it is being processed or

arranged.

RAW DATA

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78, 74, 65, 74, 74, 67, 63, 67,

80, 58 74, 50, 65, 74, 86, 78,

63, 65, 80, 89

The raw scores for 20 students in a

test

COLLECT DATA

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Score

(X)

Frequency

(f)

50

58

63

65

67

74

78

80

86

89

1

1

2

3

2

5

2

2

1

1

Total

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Frequency distribution table for

ungrouped data

SUMMARIZING DATA

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0

1

2

3

4

5

6

1 2 3 4 5 6 7 8 9 10

Skor

Kekera

pan

0

1

2

3

4

5

6

1 2 3 4 5 6 7 8 9 10

Score

Fre

qu

en

cy

50 60 70 80 90

PRESENTING DATA

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