First Lecture for Business Stat

1. Statistics The branch of mathematics that deals with the collection, organization, analysis, and interpretation of numerical data. Statistics is especially useful in drawing general conclusions about a set of data from a sample of the data. A scientific study of knowledge that deals with Collection of data Organization/presentation of data Analysis and interpretation of data

2. Branches of Statistics Descriptive Statistics is a statistical procedure concerned with describing the characteristics and properties of a group of persons, places or things. It is based on easily verifiable facts. Descriptive Statistics can answer questions such as: How many students have failed MATH30-2 thrice? What are the highest and lowest scores in the final exam? What insurance policies/products have appealed the public most? What proportion of Filipinos will vote for Noynoy? How many passed in the recent nursing licensure exam?

3. Branches of Statistics Inferential Statistics draws inferences about the population based on the data gathered from the samples using the techniques of descriptive statistics. Remark: DS is the backbone of IS. Inferential Statistics can answer questions like: Is there a significant relation between the amount of election expenses and popularity among voters? Is there a significant correlation between the amount spent in studying and final grade in a Math course? Is there a significant correlation between the height of a player and his total points in a basketball game? Remark: In IS, one tries to arrive at conclusions that extend beyond the immediate data alone.

4. Population and Sample Population – a large collection of objects, places, or things. Parameter – any numerical value that describes a population. Example: There are 5,786 students enrolled in MATH10-1. Population: students of MATH10-1 Parameter: 5,786 Sample – a small portion or part of a population; a representative of the population in a research study.

5. Population and Sample Statistic – any numerical value that describes a sample. Example: Of the 5,786 students enrolled in MATH10-1, 3,456 are female. Population: students of MATH10-1 Parameter: 5,786 Sample: Female students in MATH10-1 Statistic: 3,456 Issues in sample: How to choose the sample? How large the sample should be? Does the sample reflect the entire population?

6. Data Data are facts (a set of infomation) gathered or under study. Types of Data Primary Data – refer to information which are gathered directly from an original source or which the researcher gathered himself. Secondary Data – refer to information which are taken from published or unpublished data previously gathered by other individuals or agencies. Quantitative Data – numerical in nature and therefore, meaningful arithmetic can be done. Qualitative Data – attributes which cannot be subjected to meaningful arithmetic.

7. Examples: Classify as QN/QL Weekly allowance Income of parents Gender Civil Status Religion Age Address Educational attainment Jobs Schools attended

8. Types of Quantitative Data Discrete data – assume exact values only and can be obtained by counting. Example: Number of students Continuous data – assume infinite values within a specified interval and can be obtained by measurement. Example: Height

9. State whether discrete or continuous. The number of hair-transplant sessions undergone in the past year. The time since the last patient was grateful for what you did. The amount of weight you’ve put on in the last year. The number of hairs you’ve lost in the same time.

10. Variable A variable is simply what is being observed or measured. A property of a population/sample which makes the members different Example: Gender of students in Mapua Dependent variable – the outcome of interest, which should change in response to some intervention. Independent variable – the intervention, or what is being manipulated. Example: Number of hours spent in studying and test scores

11. Constant A property of a population/sample which makes the members similar Example: Gender in a class of all boys

12. Variables According to Scale of Measurement Nominal Variable - has no meaning (e. g. SSS No.) - consists of named categories, with no implied order among the categories Ordinal Variable - used to label rank - consists of ordered categories, where the differences between categories cannot be considered to be equal Example: A student evaluation rating consisting of Excellent/Satisfactory/Unsatisfactory has three categories.

13. Variables According to Scale of Measurement Interval Variable - has no true zero. - has equal distances between values, but the zero point is arbitrary. Examples: Temperature IQ (difference between 70 and 80 is same as 120 and 130; an IQ of 100 does not mean twice the IQ of 50) Ratio Variable - has true zero. - has equal intervals between and a meaningful zero point. Examples: Physical characteristics (height and weight)