Basic terms in Statistics Statistics and Probability-1.pptx

Statistics and Probability 11

1. BASIC TERMS IN STATISTICS

2. At the end of the lesson, the learner should be able to: Define universe and differentiate it with population; and Define and differentiate between qualitative and quantitative variables, and between discrete and continuous variables (that are quantitative)

3. The collection of respondents from whom one obtain the data is called the universe of the study The set of students of this Statistics and Probability class is our universe A universe is not necessarily composed of people (plants, animals, buildings, vehicles, farms, etc )

4. Universe- the collection or set of units or entities from whom we got the data. A variable is a characteristic that is observable or measurable in every unit of the universe The set of all possible values of a variable is referred to as a population A subgroup of a universe or of a population is a sample

5. Broad Classification of Variables A variable takes on several values. But occasionally, a variable can only assume one value, then it is called a constant . Variables can be broadly classified as either quantitative or qualitative, with the latter further classified into discrete and continuous types

6. Qualitative Variables express a categorical attribute, such as sex (male or female), religion, marital status, region of residence, highest educational attainment. Qualitative data answer questions “what kind.” Sometimes, there is a sense of ordering in qualitative data,

7. Qualitative Variables Data on sex or religion do not have the sense of ordering, as there is no such thing as a weaker or stronger sex, and a better or worse religion. Qualitative variables are sometimes referred to as categorical variables .

8. Quantitative Variables (Numerical data) answer questions such as “how much” or “how many” Have actual units of measure

9. Quantitative Variables Examples of quantitative variables include the height, weight, number of registered cars, household size, and total household expenditures/income of survey respondents

10. Quantitative data may be further classified into: Discrete —data that could be counted. Examples: the number of days for cellphones to fail, the ages of survey respondents measured to the nearest year, and the number of patients in a hospital Continuous —data that can be measured

11. Quantitative data may be further classified into: Continuous —Examples of Continuous data includes: the exact height of a survey respondent the exact volume of some liquid substance The possible values are uncountably infinite.

12. QUESTION : If quantitative variables are numerical data, is the zip code of Palompon , which is composed of numbers, considered as quantitative variable?

13. ANSWER : No. Even though a variable may take numerical values, it does not make the corresponding variable quantitative. The issue is whether performing arithmetical operations on these data would make any sense. It does not make sense to sum two zip codes. Therefore, it is not a quantitative data

14. KEY POINTS • A universe is a collection of units from which the data were gathered. • A variable is a characteristic we observed or measured from every element of the universe. • A population is a set of all possible values of a variable.

15. KEY POINTS • A sample is a subgroup of a universe or a population. • In a study there is only one universe but could have several populations. • Variables could be classified as qualitative or quantitative, and the latter could be further classified as discrete or continuous.

16. The Engineering Department of Palompon did a listing of all buildings in their locality. If you are planning to gather the characteristics of these buildings, What is the universe of this data collection activity? What are the crucial variables to observe? Classify whether it is qualitative or quantitative. Furthermore, classify if discrete or continuous