math.

• STATISTICS

  -A branch of mathematics that deals with;

  Collecting

  Organizing

  Analyzing

  Interpreting
• DATA

  -individual pieces of factual information recorded and used for the purpose of analysis.

  -From which conclusions can be drawn to answer the problem
• A statistical question is a question that can be answered by collecting data that vary. For example, "How old am I?” is not a statistical question, but "How old are the students in my school?" is a statistical question.
• STATISTICAL INSTRUMENTS

  -any process that aim at describing a phenomena by using any instrument or device, however the results may be used as a control tool. are devices or tools used to gather data.
• CLOSE-ENDED QUESTIONS - ASK RESPONDENTS TO CHOOSE FROM A PREDEFINED SET OF RESPONSES, TYPICALLY ONE-WORD ANSWERS

  SUCH AS “YES/NO”, “TRUE/FALSE”, OR A SET OF MULTIPLE-CHOICE QUESTIONS.
• OPEN-ENDED QUESTIONS - START WITH “WHY?” “HOW?” AND “WHAT?” THEY ENCOURAGE A FULL ANSWER, RATHER THAN THE SIMPLE “YES” OR “NO” RESPONSE THAT IS USUALLY GIVEN TO A CLOSED-ENDED QUESTION.
• DATA - is a collection of facts or information for reference or analysis
• Qualitative data - is descriptive. It describes the data that fits into the categories. It includes things like color, texture, and taste. It does not involve any mathematical calculations. (Gender, marital status, grade level, senior high track/strand, etc)
• Quantitative data - is also known as numerical data which represents the numerical value (i.e. How much, how often, how many). Numerical data gives information about the quantities of a specific thing. Some examples of numerical data are height, length, size, weight, and so on.
• Discrete data - Discrete information contains only a finite number of possible values. Things can be counted in whole numbers.
• Continuous data - is data that can be calculated. It has an infinite number of probable values that can be selected within a given specific range.
• Ratio Data - Differences between measurements, true zero exists
• Interval Data - Differences between measurements but no true zero
• Ordinal Data - Ordered Categories (rankings, order, or scaling
• Nominal Data - Categories (no ordering or direction)
• DATA COLLECTION - Data collection is the process of gathering, measuring, and analyzing accurate data which then enables one to answer relevant questions and evaluate outcomes
• SECONDARY DATA - is the data that is obtained from the journals and other databases that are usually prepared and available as open source data.
• Interview Method – this method of collecting data is an oral or verbal communication where the interviewer asks questions in any mode (face to face, telephone, or virtual) to an interviewee. The person gathering the data is called the interviewer, while the person supplying the data is the interviewee.
• Questionnaire Method – on this method data is gathered through a set of question that is mailed or handed to respondents who are expected to read and understand them. And the respondents then, write down their responses in the space provided the accomplished questionnaire is then returned for appropriate processing.
• Observation Method – the data on this method are gathered either individually or collectively by means of observation. The person who gathers the data is called an investigator while the person being observed is called the subject.
• Experimental Method – The experimental method involves manipulating one variable to determine if changes in one variable cause changes in another variable.
• Population - The population is the entire group of people or things that are of interest to the researcher.
• Sample - A sample is a subset of the population and is used to represent it.
• RANDOM SAMPLING – is a type of probability sampling in which the researcher randomly selects a subset of participants from a population.
• SYSTEMATIC SAMPLING – is a probability sampling method in which researchers select members of the population at a regular interval
• STRATIFIED SAMPLING – is the process of creating subgroups in a dataset according to various factors, such as age, gender, income level, or education.
• CLUSTER SAMPLING – involves dividing a population into subgroups or clusters, and then randomly selecting a sample of these clusters
• CONVINIENCE SAMPLING – Convenience sampling method where units are selected for inclusion in the sample because they are the easiest for the researcher to access
• PURPOSIVE SAMPLING – Purposive sampling which units are selected because they have characteristics that you need in your sample. In other words, units are selected “on purpose”
• QUOTA SAMPLING – Relies on the non-random selection of a predetermined number or proportion of units. You first divide the population into mutually exclusive subgroups and then recruit sample units until you reach your quota.
• SNOWBALL SAMPLING – Snowball Sampling is where existing study subjects recruit future subjects from among their acquaintances. Thus the sample group is said to grow like a rolling snowball
• • FREQUENCY is the number of times the observation has occurred/recorded in an experiment or study
• FREQUENCY DISTRIBUTION TABLE - is a way to organize and summarize data so that it makes the data more meaningful
• Lower Class Limit - the smallest value in that class.
• Upper Class limit - is the highest value of the class interval
• Class boundaries - are the numbers used to separate classes.
• Class mark - is defined as the average value or mid-point value of a given interval
• Class width - is the difference between the lower limits of two consecutive classes or the upper limits of two consecutive classes.
• • CENTRAL TENDENCY is defined as “the statistical measure that identifies a single value as representative of an entire distribution. “The mean, median and mode are the three commonly used measures of central tendency.
• Mean -The average of a data set.