PowerPoint Presentation

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Chapter(1)

The Nature of Probability and Statistics

Lecture (1)

Lecturer : FATEN AL-HUSSAIN

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Note: This PowerPoint is only a summary and your main source should be the book .

Introduction

 Statistics: is the science of conducting studies to

collect, organize, summarize, analyze, and

draw conclusions from data

.

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 A Variable: is characteristic or attribute that can assume

different values.

 Data: are the values (measurements or observations)that the

variables can assume.

 Random Variable: variables whose determined by chance.

 Data set : Collection of data values .

 Datum Or a data value Each value in the data set

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Descriptive and Inferential Statistics

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id gender age

1 male 20

2 male 25

3 female 30

4 male 23

5 female 30

For example: A data set in table

Variables

Nationality

Saudi Yemeni

Egypt Jordanian

Lebanese

Data

Set Data

Data

value

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Population Sample

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For example :

In order to study the response times for emergency 988 calls in Jeddah 50 calls are selected randomly over a six month period and the response times are recorded .

Population : all calls 988.

Sample : 50 calls .

 A population consists of all subjects (human or otherwise) that are studied.

 A sample is a subset of the population( is a group selected

from a population).

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Branches of Statistics

Descriptive

statistic Inferential

statistic

consists of the collection, organization, summarization,

and presentation of data.

For example :

-the average age of the student is 14 years.

-the median household income for people aged 25-34 is 35.888$.

consists of generalizing from

samples to populations, performing estimations and hypothesis tests, determining relationships among variables, and making predictions.

For example:

 the relationship between smoking and lung cancer.

 probability .

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Example 1-1 :

A researcher asked 80 students at KAU about their weight.

As a result of this information, the average weight of students at KAU was 59kg. Which branch of statistics was used in this survey?

a) Observational

b) Experimental

c) Inferential

d) Descriptive

Answer : d

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Example 1-1 :

"There is a relationship between IQ tests and the final score student" Which branch of statistics is

a) Observational b) Experimental

c) Inferential d) Descriptive

Answer : c

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Variables and Types of Data ,

Measurement Scales

Lecture (2)

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Variables and Types of Data

Variables

Qualitative Categorical Non numerical

Quantitative Numerical

Discrete Countable

5 , 29 , 8000 ,

etc .

Continuous Can be decimals

2.59 ,

312.1 ,

etc

.

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are variables that have distinct categories , according to

some characteristic or attribute.

For example: Gender ,Marital status ,Color……etc

are variables that can be counted or measured.

For example: Age ,Height , Weight ,temperature …..etc

Types of Variables

Qualitative

Variables: Quantitative

variables

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Discrete

Variables Continuous

Variables

Quantitative variables classified into two groups

assume values that can be counted .

For example:

 Number of children in a family ,

 Number of student in classroom,

 Number of DVDs rented each day ……etc

assume an infinite

number of values between any two specific values.

For example:

 Temperature ,

 Height

 Weight

 Time …..etc

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Measurement Scales

Qualitative

Nominal Ordinal

Quantitative

Interval Ratio

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Nominal

level Ordinal

level :

Measurement Scale of Qualitative

classifies data into mutually exclusive , exhausting

categories in which no order or ranking can be imposed on the data.

For example:

Eye color ,Gender ,

Political party , blood types …etc

classifies data into

categories can be ranked . For example:

Grade of course (A,B,C) , Size( S,M,L)

Rating scale

(Poor ,Good ,Excellent )

Ranking of tennis players …etc

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Interval

level Ratio

level

Measurement Scale of Quantitative

ranks data and precise

differences between units of measure do exist ,however there is no meaningful zero.

For example:

Temperature , IQ test…etc

possesses all the

characteristics of interval and there exist a true zero.

For example:

Height , Weight, Time, Salary , Age …etc

-

⁰

+

0

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1- Blood Type ,an example of which type of data?

a) Qualitative c) Ordinal

b) Continuous d)Nominal

2- Area of the Kingdom of Saudi Arabia, is an example of which type of data?

c) Discrete c) Qualitative

d) Nominal d)Continuous

Example 1-2 :

Answer 1 : a

Answer 2 : d

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3- Number of stores represents a

a) Quantitative variable c) Discrete variable b) Continuous variable d) Interval variable 4- Classify " Temperature in Jeddah":

a) Interval c) Qualitative b) Continuous d) Discrete

Example 1-2 :

5- If you classified the fruit in a basket as apple, orange and banana , this would be an example of which level of measurement?

a) Ordinal c) Ratio b) Nominal d) Interval

Answer 3 : c

Answer 4 : a Answer 5 : b

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6- Which of the following represents ordinal level of measurement?

a)Rating scale c) IQ score

d) Age b) Marital status

7- Which one of the following variables is Qualitative?

a) Amount of fat in a piece of cheese b) Salary of college professors

c) Favorite TV program d) Age of a person

Example 1-2 :

Answer 6 : a Answer 7 : c

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Data Collecting and

Sampling Techniques

Lecture (3)

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Data collection

Survey Observational

and

Experimental

Telephone surveys

Mailed questionnaire

surveys

Personal

interview

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Some Sampling Techniques

Random sampling

Systematic sampling

Stratified sampling

Cluster sampling

To obtain samples that are unbiased ,statisticians use four methods of sampling

:

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1 - Random sampling

A random sample is a sample in which all members of the population have an equal

chance of being selected .

 Random samples are selected by using

chance methods or random numbers

For example:

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Summer Term

Q: Select random sample of 15 subjects out of 85 subjects :

A: 12, 27, 75, 62, 57, 13, 31, 06, 16, 49, 46, 71, 53, 41, 02

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2- Systematic sampling

is a sample obtained by selecting every k th member of the population where K is

counting number.

 Systematic samples are obtained by

numbering each value in the population and then selecting the k

^th

value.

For example:

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• Decide on Sample Size: n

• Divide Frame of N individuals into Groups of k Individuals: k=N/n

• Randomly Select One Individual from the 1st Group

• Select Every k

^th

Individual Thereafter

N = 64 n = 8 k =64/8= 8

First Group

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3- Stratified sampling

is a sample obtained by dividing the population into subgroups or strata according to some

characteristic relevant to the study.

Then subjects are selected from each subgroup.

For example:

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A researcher select a random sample from each gender to check their blood pressure

Female male

Strata Select

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4- Cluster sampling

is obtained by dividing the population into sections or clusters and then selecting one or more clusters and using all members in the cluster(s) as the members of the sample.

For example:

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Kingdom of Saudi Arabia

EAST NORTH WEST SOUTH

EAST Strata

Select

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1) Random : random number generator.

2) Systematic :every k

^th

subject.

3) Stratified :divide population into group called “strata”.

4) Cluster :use intact groups.

Summary of sampling techniques

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1- A researcher wanted to do a study about doctor’s income in Jeddah. He divided hospitals into two sectors (private and public) then he took a sample from each sector.

2- A researcher wanted to know doctors opinion about herbal therapy in Jeddah. For this study he choose randomly 3 hospitals out of 20 hospitals in Jeddah, and all doctors in the 3 hospital were asked.

Example 1-3 :

A) Systematic C) Custer B) Stratified D) Random

Answer 1 : B Answer 2 : C

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3- Every 7

^th

student entering the gate is checked for

possession of university card .What type of sampling is used?

A) Systematic C) Custer B) Stratified D) Random

Example 1-3 :

Answer 3 : A Answer 4 : D

4 - Nursing supervisors are selected using random

numbers to determine annual salaries .

A) Systematic C) Custer

B) Stratified D) Random

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Types of Studies

, Uses and Misuses of statistics

Lecture (4)

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Observational

Study Experimental

Study

Types of Studies

The researcher merely observes what is happening or what has happened in the past and tries to draw conclusions based on these

observations . : For example

 In a study the researcher records the mistakes which has been made by the students in the exam.

 A researcher counts the number of people living in each house in

specific a street .

The researcher manipulates one of the variables and tries to

determine how the manipulation influences other variables

. For examples:

 Patients were randomly assigned to two groups was given drug A and the other group was given drug B to

determine if the drug has an effect on patient’s blood pressure.

 An Instructor has Three Teaching method ,he want to apply a best method by seeing students grades.

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 In a true experimental study, the subjects should be assigned to groups randomly. If this is not possible

and a researcher uses intact groups, then he is

performing a quasi-experimental study.

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Dependent Variable or

Outcome Variable

Any Experiment has 2 Variables

Independent Variable or

Explanatory Variable

The independent variable in an experimental study is the one that is being manipulated by

the researcher .

is the resultant variable

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For example

Note :Statistical studies usually include one or more independent variables and one dependent variable .

For example :

exercise , diet ( independent )

Health (dependent)

Independent temperature of

water exercise

Dependent time to cook an

egg health

Influence

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A confounding variable: is the variable that influences the

dependent or outcome variable but was not separated from the independent variable.( variable that influence

with other variable) For example:

cigarette

health

age

cigarette health

con fou ndi ng var iab le

Influence

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Example :subjects on exercise program may improve their diet and perhaps that improve their health in other ways not due to exercise alone. Then diet becomes confounding

variable .

exercise

health

diet

exercise health con fou ndi n

g v ari abl e

Influence

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Control Group

Types of group

Treatment Group

The group that received the special instruction

. The group that dose not received

the special instruction

.

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Example 1-4 :

Patients were randomly assigned to groups: one group was given

drug A and the other group was given drug B to determine if the drug has an effect on patient ’ s blood pressure. But some of the patients

were smoking.

Type of study the dependent variable the independent

a)Experimental study a) smoking a) smoking b)Quasi – experiment study b) type of drug b) type of drug c)Confounding c) blood pressure c)blood pressure d)Observational study d) patients d)patients

Answer : A Answer : C Answer : B

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Example 1-4 :

1-In the relationship between the number of studying hours and exam grade ,the number of studying hours is assumed to be

a)Independent variable b)Nominal variable c)Confounding

d)Dependent variable

2- A researcher stood at a busy intersection to see if the color of the automobile that a person drives is related to running red lights.

a) Inferential

b) Observational c) Descriptive d) Experimental

Answer 1 : A Answer 2 : B

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Uses and Misuses of statistics

Suspect sample

Ambiguous Averages

changing Subject

Detached Statistic

Implied connection

Faulty Survey Question Misleading

Graphs

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1- Suspect sample:

small samples ,convenience sample, volunteer sample

For example: if 4 doctors were surveyed from 100 doctors.

Uses and Misuses of statistics

2- Ambiguous Averages: measures that are loosely called averages are the mean, median, mode and midrange. People who know this can without

lying , select one of them to support their position.

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3- changing Subject : can occur when different values are used to represent the same data.

For example: if one political candidate say “ I will increase salaries a mere 3%”

And another one say “I will increase salaries a whopping 6,000,000 $”

And 3% =6,000,000

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4- Detached Statistic: it is the one in which no comparison is made.(Compared to what?)

For example, one may say that “Our cookies has one-third fewer calories” Here, fewer than what?

5- Implied connection : Usage of words such as may, suggest or some that imply connections but there is no guarantee

For example:

Eating fish may help to reduce your cholesterol.

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6-Misleading Graphs: if graphs are drawn

inappropriately, they can misrepresent the data and lead to false conclusions.

7- Faulty Survey Question : should be sure that the

questions are properly written since the way questions are phrased can influence the way people answer them .

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1 - An advertisement for an exercise product; “using this product will

burn 74% more calories

”

a) detached statistics b) changing the subject c) implied connections d) suspect samples

2 - An advertisement for an exercise product; Studies suggest that

using our exercise machine reduce your weight .

a) detached statistics b) changing the subject c) implied connections

d) suspect samples

Example 1-5 :

Answer 1 : A Answer 2 : C

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Chapter Definitions

1 - Statistics

2 - A variables

3 - Random Variables .

4 - Data .

5 - A data set

6 - A data value or a datum .

7 - A population

8 - A sample

9 - Descriptive

10 - Inferential

11 - Qualitative Variables :

12 - Quantitative variables

13 - Discrete Variables

14 - Continuous Variables

15 - Nominal level

16 - Ordinal level

17 - Interval level

18 - Ratio level

19 - Observational Study

20 - Experimental Study 21

- Independent Variable

22 - Dependent Variable

23 - A quasi-experimental study

24 - A confounding variable

25 - Treatment Group

26 - Control Group

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