Introduction to Statistics

1. Introduction to Statistics

2. Objectives: • Define terms • Identify types and kinds of data • Infuse the relevance of statistics

3. mathematics interpreted information collected Statistics organized techniques analyzed

4. mathematics interpreted information collected Statistics organized techniques analyzed

5. Statisticsis a branch of mathematics concerned with the techniques by which information is collected, organized, analyzed, and interpreted.

6. Two Major Divisions of Statistics 1. Descriptive Statistics– is concerned with the collection, classification, and presentation of data to be able to summarize and describe the group characteristics of the data. Ex: measures of central tendency, measures of variability, skewness, etc.

7. 2. Inferential Statistics– refers to the drawing of conclusion or judgment about the population based on a representative sample taken from the same population Ex: hypothesis testing using z-test, t-test, analysis of variance, etc.

8. Steps in Statistical Investigation Collection of data Processing of data Presentation of data Analysis of data Interpretation of data

9. Steps in Statistical Investigation 1. Collection of data– process of obtaining or gathering numerical data 2. Processing of data– organizing data to show significant characteristics 3. Presentation– in the form of tables, graphs, and charts

10. 4. Analysis of data– method of drawing from the given data relevant information from which numerical description can be formulated. 5. Interpretation of data– refers to the task of drawing conclusions from the analyzed data.

11. Data or information are obtained through interview or surveys, researches, experiments, and a lot more. It is the measured variable from a set of experimental units, or a set of measurements

12. Types of Data Primary data – information gathered directly from an original source ex: autobiographies, diaries, business entities and private and public agencies

13. Types of Data 2. Secondary data – information taken from existing records ex: published books, newspapers, magazines, theses and dissertations

14. Classification of Statistical Data Nominal data – are numerical in name only because they do not share the properties of numbers we deal with in ordinary arithmetic. ex: designation of marital status as 1, 2, 3, or 4 for single, married, widowed or divorced

15. Classification of Statistical Data 2. Ordinal data – numbers indicate rank order of measurements but they do not indicate the magnitude of interval between the measures. ex: order of finish in races, grades for achievement, body frames (small, medium, large)

16. Classification of Statistical Data 3. Interval data – numbers represent equal units between measurements ex: temperature readings

17. Classification of Statistical Data 4. Ratio data – numbers represent equal units between measurements and there is an absolute zero point. The easiest to find and they include all the usual measurements. ex: income (measured in pesos, with zero equal to no income at all)

18. Other Classification of Statistical Data Discrete data – quantifiable expressed by a whole number, an end result of counting - can only assume a finite or countable number of values ex: number of students, number of days

19. Other Classification of Statistical Data Continuous data – usually results of measurements - can assume infinitely many values that correspond to the points on a line or interval ex: height, weight, winning time

20. Variable is the characteristic that is being studied. Variable is observable characteristic that can be measured or classified. ex. height, grade of students, time, hair color

21. Two types of variables Qualitative variable – assumes values that can be categorized according to some distinct characteristics or attribute. - it has no numerical value Ex: color, type of car

22. Two types of variables 2. Quantitative variable – includes variables that assume numerical values. Ex. height, weight, length, monthly income