1. Introduction to Probability and Statistics Consultation time: Ms. Chong

3. OUTLINE 1-1Introduction 1-2Descriptive and Inferential Statistics 1-3Variables and Types of Data 1-4Data Collection and Sampling Techniques 1-5Computers and Calculators

4. OBJECTIVES Demonstrate knowledge of all statistical terms. Differentiate between the two branches of statistics. Identify types of data. Identify the measurement level for each variable.

5. Identify the four basic sampling techniques. Explain the importance of computers and calculators in statistics.

6. 1-1 Introduction What is statistics? Number ? Value ? Data ?

7. Statistics consists of conducting studies to collect, organize, summarize, analyze, and draw conclusions from data. Data are the values (measurements or observations) that the variables can assume. Variables whose values are determined by chance are called random variables.

8. 1-2 Descriptive and Inferential Statistics Descriptive statistics consists of the collection, organization, summarization, and the presentation of data. Inferential statistics consists of performing estimations and making decision about the whole population based on the information gained from limited samples.

9. Involve the use of “statistical terms” for the data descriptions. “Describe” the data that have been collected. Descriptive Statistics

10. Examples of Descriptive Statistics Six out of ten students in UCSI are local students. Average score for Biology test is 75.8. The average annual salary for staffs in Company A is RM 2500.

11. Draw conclusions and make decisions Make predictions Inferential Statistics

12. Examples of Inferential Statistics The population in Malaysia will increase by 5% in the coming five years. It was estimated that the number of cars sold in next month is 50. Making predictions and estimations

13. Relief constipation problem. Patients with constipation problem. Eat diet high in fiber. Examples of Inferential Statistics Diet high in fiber can help to reduce constipation problem. Making Conclusion

14. A population consists of all subjects (human or otherwise) that are being studied. A sample is a group of subjects selected from a population. Population Sample

15. Generally, researchers generalize their findings on population based on the sample selected. The sample must be representative enough to reflect the whole population. The sample is considered representative if:  The sample size was large enough.  The sample was selected randomly (no bias).

16. 1-3 Variables and Types of Data Data Qualitative Quantitative Discrete Continuous

17. Qualitative Variables  Qualitative variables are variables that can be placed into distinct categories, according to the attribute or characteristic.  It also can be known as non-numerical variables.  Example: Gender Nationality Marital status

18. Quantitative Variables  Quantitative variables are numerical and can be ordered or ranked.  It also can be known as numerical variables.  Example: Height Age Blood Pressure

19. Discrete Variables  Discrete variables assume values that can be counted.  Example: Number of student, pairs of shoes, price, etc.

20. Continuous Variables  Continuous variables can assume all values between any two specific values.  They are obtained by measuring instead of counting.  Example: Weight, height, volume, pressure, etc.

21. Level of Measurement  Classify data according to how they are categorized, counted, or measured.  There are four level of measurement.

22. Nominal Interval Ratio Ordinal

23. Norminal level of measurement  Classify data into mutually exclusive categories in which no order or ranking can be imposed on the data.  Example: Course studied (Medicine/Food Science, etc.) Eye colour (brown/blue, etc.) Gender (Male/Female)

24. Ordinal level of measurement  Classify data into categories that can be ranked; however, no precise differences exist between the ranks.  Example: Grade (A, B, C, D) Performance (poor, fair, good, excellent) Judging (first place, second place, etc.)

25. Performance PoorFair GoodExcellent Is the difference between poor and fair same as the difference between good excellent? Are the differences measurable?

26. Interval level of measurement  Ranks data, and precise differences between the units of measure do exist.  There is no true / meaningful zero.  Example: IQ (109, 110, etc.) Temperature (- 5 o C, 0 o C, 8 o C, 10 o C, etc.)

27. Ratio level of measurement  Possesses all characteristics of interval measurement.  A true zero do exists.  Additionally, true ratios exist for the same variable.  Example: Salary (RM0, RM1000, RM3000, etc.) Weight (0kg, 35kg, 70kg, etc.)

28. 1-4 Data collection and Sampling Techniques Data can be collected via different methods. Survey is one of the most commonly used methods. Surveys can be done through telephone, mail questionnaire, personal interviews, etc.

29. Sampling Methods  There are four basic sampling methods are used by statisticians to obtain samples that are unbiased.

30. Random Sampling  The samples are selected by chance methods or random numbers.

31. Systematic Sampling  The samples are obtained by numbering each value in the population and then selecting every k th value.  E.g: Every tenth sample 1 st sample 2 nd sample 3 rd sample 1 2 3 4…10…15…20…25.…30…35

32. Cluster Sampling  The population is divided into many clusters.  Samples are selected by using some intact groups that are representative of the population.

33. POPULATION AC D B E A C E Samples

34. Stratified Sampling  Population is divided into many groups (strata) according to some characteristics.  The subjects within groups are randomly selected as samples for study.

35. POPULATION A C B D A B C D 50 samples

36. 1-5 Computers and Calculators Computers and calculators make numerical computation easier. There are many statistical packages available such as Microsoft Excel, MINITAB, SPSS, Unscrambler, etc. Data must still be understood and interpreted.