BASIC CONCEPTS OF STATISTICS

• Statistics  is the branch of mathematics which deals with collection, organization, presentation, analysis, and interpretation of data. 
• • Descriptive Statistics – focuses on the task of collecting, processing, and presenting data 
• Inferential Statistics - focuses on the analysis and interpretation of data
• Inferential Statistics - makes conclusion
• USES OF STATISTICS

  1. Precise description of data
  2. Predict outcome of experiment
  3. Test a hypothesis
• POPULATION – a complete collection of all elements to be studied.
• POPULATION – usually represents all subjects under study.
• SAMPLE – a sub-collection of elements drawn from a population.
• The sample will be basis of generalization on behalf of the population.
• In obtaining the sample, you should consider every element in the population and the scope of the study.
• Data refers to any facts or information a researcher works. 
• QUALITATIVE DATA – represents differences in quality, character, or kind.
• Gender (categorized as Male or Female), Civil Status (Single, Married, etc.), Color of skin, eyes, hair are all examples of Qualitative Data
• QUANTITATIVE DATA – numerical in nature
• QUANTITATIVE DATA – variables which yield numeric values.
• QUANTITATIVE DATA - These are data that can be measured and counted.
•  Height of a person (we represent height in numerical values.), Age, Daily Allowance (in Php) are all examples of Quantitative Data
• DISCRETE – Quantitative values can be counted using integral values.
• Quantitative data can be further classified as Discrete or Continuous.
• CONTINUOUS – Quantitative values assume over an interval or intervals.
• DISCRETE - Results from either a finite number of possible values or countable number of possible values.
• CONTINUOUS - Result from infinitely many possible values that can be associated with points on a continuous scale in such a way that there are no gaps or interruptions. 
• Money in the Bank is an example of CONTINUOUS Qualitative Data
• Number of students in a class is an example of DISCRETE Qualitative Data
• NOMINAL 

  • Categorical data and numbers that are simply used as identifiers.
• NOMINAL

  • Classifies data into names, labels or categories in which no order or ranking can be imposed.
• Gender, Jersey number, and ID number are examples of NOMINAL level of measurement
• Performance Evaluation, Socio-economic status, and Pain scale are examples of ORDINAL level of measurement
• ORDINAL 

  • Classifies data into categories that can be ordered or ranked, but precise differences between the ranks do not exist. 
• INTERVAL 

  • Have a precise difference between measures but the zero value is arbitrary and does not imply an absence of the characteristic being measured. 
• Temperature is an example of INTERVAL level of measurement
• RATIO 

  • Based on a standard scale which have a fixed zero point in which the zero value denotes the complete absence of the characteristic being measured. 
• Money is an example of RATIO level of measurement
• Primary data sources include information collected and processed directly by the researcher, such as observations, surveys, interviews, and focus groups. 
• Secondary data sources include information that you retrieve through pre-existing sources such as research articles, Internet or library searches.
• SURVEYS – this method solicits information from the respondents.
• Interviews and Questionnaires are examples of Survey method of obtaining data
• Interview – This method is referred to as the direct method of gathering data because this requires a face – to – face inquiry with the respondents. 
• Questionnaires – This method is referred to as the indirect method of gathering data because this makes use of written questions to be answered by the respondents.
• OBSERVATION – method is done by using the five senses.