This document provides an introduction to statistics, covering several key topics: - It defines statistics as the science dealing with data collection and analysis. - Descriptive and inferential statistics are introduced, where descriptive involves summarizing data and inferential involves making conclusions beyond the sample data. - The main concepts of population, sample, parameter, and estimator are defined. - Variables are described as being either qualitative or quantitative, and quantitative variables are further divided into discrete and continuous. - The four levels of measurement - nominal, ordinal, interval, and ratio scales - are outlined. - Finally, the two main methods for data collection - primary and secondary data - are discussed.

2. Topic:
Introduction To Statistics

3. Objectives:
• Statistics
• History of statistics
• Importance of statistics
• Basic Definition
Population
Sample
Parameter
Estimator

4. Statistics:
• Latin word, ‘Status’ which means, ‘‘knowledge about state.’’
• Plural of statistics is ‘statistic’
• Branch of science that deals with the scientific method is called statistics.
• i.e. Arm forces, Population, Graphical area etc.
• A single numerical quantity computed from sample is defined as statistic.

5. Scientific Method
• A method of research in which a problem is identified, relevant data is
gathered.

6. History of Statistics
• In past, kings and rulers used Statistics.
• Information about lands and population of state
• Gottfried Achenwall (1719-1772)
• Sir Ronald Aylmer Fisher (1890-1962)
• Modern statistics
• Francis Galton
Std. deviation
Correlation
Regression

7. Applications of Statistics
• Engineering
• Economics
• Business
• Environment
• Physics
• Chemistry
• Biology
• Medical and so on.

8. Importance in daily life
• Every day we are bombarded with different type of data
• If you can't distinguish good from faulty reasoning, then you do manipulation
• Statistics provides tools that you need in order to react the information
H.G. Wells says that,
“Statistical thinking will one day as necessary for citizenship as the ability to read and
write”

9. Characteristics of Statistics
• Statistics of aggregate facts
• Statistics of numerical expressed
• Statistics are affected by variety of causes
• Statistics are collected in systematic manners
• Statistics are placed in the relation to each other

10. Some Basic Concepts
• Population
• Sample
• Parameter
• Estimator

11. Population
• Totality of objects under a particular place is called population.
• Population size denoted by ‘‘N’’
• Population mean is denoted by ‘‘µ’’

12. Examples:
• All students studying at UOG
• All registered voters in Pakistan
• All parts produced today

13. Sample
• Sub and representative part of population is called sample.
• Sample size is denoted by ‘n’
• Sample mean is denoted by ‘‘X̅’’
Examples:
• 100 voters at random for interview
• Only students of Management of Sciences Departments

14. Parameter
• The result computed from population is defined as parameter.

15. Estimator
• The result computed from sample is defined as sample.

16. Topic:
Types of Statistics

17. Types of statistics:
Statistics
Descriptive Statistics Inferential Statistics

18. Descriptive Statistics
• Collecting, summarizing, presenting and analyzing data

19. Why we need descriptive statistics?
• Visualize what the data was showing
• Present data in a more meaningful way
• Simpler interpretation of data

20. Types of Descriptive Statistics
• Measure of frequency:* Count, percent, frequency...
• Measure of Central tendency:* mean, median, mode...
• Measure of Dispersion or Variation:*Range, Variance, Standard Deviation…
• Measure of Position:* Percentile Ranks, Quartiles Ranks…

21. Inferential Statistics
• Data collecting from a small group
• draw conclusion about a larger group
Examples:
• Accounting department of a large firm will select a sample of the invoices to
check for a accuracy for all the company

22. Why we need inferential statistics?
• To infer from the sample data
• To make judgment of probability that an observe difference between groups

23. Topic:
Variables

24. What is variable?
• Values varies from one observation to another
• Also known as data item

25. Example:
• Gender
• Age
• Height
• Weight
• Area
• Grades
• Blood group
• Temperature

26. Types of variables:
• Qualitative variables
• Quantitative variables
Continuous
Discrete

27. Qualitative Variables:
• Assume only verbal response
• Also called Categorical variables
• It describes data that fits into categories
• Examples
Eye colors (blue, green, red, etc.)
Grades (A+, A, B+, B, B-, etc.)
Blood groups (O+, O-, A+, A-, etc.)
Gender (Male and Female)

28. Quantitative Variables:
• Assume only numerical response
• They represent a measureable quantity
• Examples
Height
Weight
Age
Temperature

29. Types of quantitative variables:
• Two types
Discrete variables
Continuous variables

30. Discrete variables:
• Assume only rounded digits
• Examples
Numbers of employments
Numbers of students
Numbers of siblings

31. Continuous variables:
• Assume only decimal or fractional digits
• Examples
Age
Weight
Height
Temperature

32. Topic:
Level Of Measurements

33. What is level of measurements?
• Developed by Psychologist S.S Stevens
• Describes the nature of Information within the values assigned to variables
• Also called Scales Of Measure

34. Historical Background:
• He proposed his typology in 1946 titled as “On The Theory Of Scales Of
Measurements”
• He claimed that “That all measurement in science was conducted using four
different types of scales”

35. Scales:
• There are four scales
Level of Measurements
Nominal Scale Ordinal Scale Interval Scale Ratio Scale

36. 1. Nominal Scale
• Used to measure Qualitative Data
• Differentiate b/w items or subjects based only on there names or categories
• Numbers may be used to represent variable but Numbers don’t have
numerical values

37. Examples
• Gender
• Parts of speech
• Religion
• Bacteria
• Eukarya
• Style

38. 2. Ordinal Scale:
• Ordinal Data
• Distinguish from Nominal scale by having ranking
• Can be ordered
• Differences are meaningless

39. Examples
• Race
• Grading system
• Designation
• Health
• Courts

40. 3. Interval Scale:
• Used for measurements of Quantitative data
• Doesn't include the true zero values
• Differences are meaningful

41. Examples
• Temperature
• Location
• Date (A.D or B.C)
• IQ score

42. 4. Ratio Scale
• Used for measurement of quantitative data
• Kind of interval scale
• Ratios are defined
• A ratio scale possesses a meaningful (unique and non-arbitrary) zero value

43. Examples
• Mass
• Height
• length

44. Topic:
Methods of Data Collection

45. What is data ?
• Collection of raw facts and figures
• Process of collecting relevant information

46. Types of data collection
• There are two types :
• Primary data
• Secondary data

47. Primary data
• Information collected at first round
• Did not undergo any statistical treatment
Methods include in this type are:
1. Direct personal investigation
2. By observing
3. By questioner method

48. Significance of primary data
• Reliability
• Availability of wide range of techniques
• Control
Limitations
1. Cost
2. Time
3. Large data

49. Secondary method
• Already collected
• Undergone through statistical treatment
Ways to access :
• Official government data
i.e. NADRA
• Semi-official
i.e. banks

50. Secondary Method(cont.)
• Publications
i.e. newspaper , books
• Reports
i.e. Birth , death rate etc.

51. Significance of S.D
• Economic
• Quickness
• Availability
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52. THANKS
ANY QUESTION