Introduction to Statistics
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1. Qualitative, Nominal 2. Quantitative, Ratio 3. Qualitative, Ordinal 4. Quantitative, Nominal 5. Qualitative, Ordinal
Introduction to Statistics
- 1. -A BRANCH OF MATHEMATICS THAT DEALS WITH THE COLLECTION, ORGANIZATION, ANALYSIS, AND INTERPRETATION OF DATA. STATISTICS
- 2. TWO DIVISIONS OF STATISTICS DESCPRIPTIVE STATISTICS - concerned with classification, presentation and collection of summarizing values to describe group characteristic of data. -topics included in this study are measures of central tendency, variability and average of scores, skewness and kurtosis. - summarizing and describing without attempting to infer
- 3. Examples: Class average of examination Average salary Average return of investment
- 4. INFERENTIAL STATISTICS - methods dealing with making inference,estimates or making predictions about the large set of data using the information gathered. Examples: Determining whether the impact of the new ad of various age groups is significant or not Whether there is significant relationship between job satisfaction and performance of employees.
- 5. POPULATION VS SAMPLE POPULATION - complete set of individuals, objects, places, events and reactions having some common characteristics. Example: Ages of graduating students IQ scores of employees Number of houses A SAMPLE -a representative cross-section of elements drawn from a population.
- 6. VARIABLE - defined as a characteristic or attribute of persons or objects, which can assume different values for different persons or objects. - refers to the property that can take on different values or categories which cannot be predicted with certainty. Ex. Undergraduate major, Smoking habit, Height, Faculty ranks
- 7. CLASSIFICATION OF VARIABLES 1. According to functional relationship a. Independent variable(predictor) b. Dependent variable(criterion) Example: The Academic performance of students is dependent on the IQ of students towards Statistics.
- 8. 2. According to continuity of values a. Continuous variables - variables that can take the form of decimals. Ex. Prices of commodities, weight, height, average grades in school b. Discrete variables - variables that can not take the form of decimals. Ex. Number of students, number of houses
- 9. CLASSIFICATIONS OF DATA QUALITATIVE DATA - categorical data taking the form of attributes or categories. They have labels or names assigned to their respective categories. Examples: Sex - male, female Year level - 1st yr, 2nd yr, 3rd yr, 4th yr Course - BSCrim., BSED Religion - INC, Born Again, Catholic
- 10. QUANTITATIVE DATA - data that consist of numbers obtained from counts or measurements like weights, heightsm ages, temperatures, scores, IQ, prices, and other measurable quantities. Examples: weight - 100 lbs, 215 kgs height - 34 in., 5cm ages - 5 y/o, 21 y/o
- 11. RAW VS ARRAYU RAW data in its original form ARRAY data arranged either from highest to lowest or from lowest to highest. Examples: Exam Grades RAW : 18 22 17 18 25 30 35 21 10 11 ARRAY: 10 11 17 18 18 21 22 25 30 35
- 12. MEASUREMENT SCALES -qualitative data may be converted to quantitative data by the process called measurement. *numbers are used to code subjects or items so that they can be treated statistically. Example: 1 – very hot 3 – warm 2 – hot 4 – cold
- 13. (Classification of data) 3. According to the Level of Measurements a. Nominal Scale - numerical values are used to classify objects, person or characteristic to identify groups to which they belong - numbers are used for identification/classification purposes only. - not arranged in ordering scheme(unordered)
- 14. Solution Examples Cause of death Gender Transportation Occupation Types of court Religion : 1 - Roman Catholic 2- INC 3 – Born again 4 - Protestant
- 15. b. Ordinal Scale - categorical data having ordered sclae - ranked or ordered in some low-to- high manner Examples: Pain level : 1- none 2- mild 3- moderate 4- severe Beauty contest Ranks Educational attainment
- 16. c. Interval Scale - having interval(of known sizes) - based on unit or interval that is accepted as common standard - o(zero) does not imply the absence of characteristic under consideration Example: Temperature 0 degrees – cold!
- 17. d. Ratio Scale - has true zero point - it indicates the absence of the characteristic under investigation Examples: height in meters age in years
- 18. EXERCISES # 1 (1/4) DETERMINE whether the given data is quantitative or qualitative. Indicate too the level of measurement. 1. Type of Case(Civil, Criminal, etc) 2. Years in service as a teacher in Cronasia. 3. Design and layout of the room(poor,fair,good,very good, excellent) 4. Court employee number 5. Court personnel are competent(strongly agree, agree, disagree)