Intro to Statistics (Terminologies) PDF

INTRO TO STATISTICS (TERMINOLOGIES) ACTIVITY: JUMBLED WORDS Arrange the letters to form a word. OPPIONTLAU POPULATION SONPERSTEND RESPONDENTS AIRLVBEA VARIABLE LEAMSP SAMPLE FNREINCEE INFERENCE IUNAYTTQ QUANTITY  POPULATION Refers to a large collection of people, objects, places, or...

INTRO TO STATISTICS (TERMINOLOGIES) ACTIVITY: JUMBLED WORDS Arrange the letters to form a word. OPPIONTLAU POPULATION SONPERSTEND RESPONDENTS AIRLVBEA VARIABLE LEAMSP SAMPLE FNREINCEE INFERENCE IUNAYTTQ QUANTITY  POPULATION Refers to a large collection of people, objects, places, or things. PARAMETER- any numerical value that represents population. Example: There are 8,458 examinees for the upcoming board exam. St. Paul University QC has  SAMPLE Small portion or part of a population; a representative of the population in a research study. STATIC- any numerical value which describes a sample. Example: Of the 8,458 examinees, 3,400 came from NCR. There are 1,001 ABM senior high school students in St. Paul RESPONDENTS Refer to the samples who are actual participants in the research. DATA Facts, or set of information gathered or under study. QUALITATIVE DATA Attributes or characteristics which cannot be subjected to meaningful arithmetic. QUANTITATIVE DATA Numerical in nature; can be obtained by counting. DISCRETE DATA- assume exact values only and can be obtained by counting. CONTINUOUS DATA- assume infinite values within a specified interval and can be obtained by CONSTANT Characteristics or property of a population or sample which makes the member similar to each other. VARIABLE Characteristics or property of a population or sample which makes the member different to each other. DEPENDENT- variable which is affected by another variable. Example: number of customers in a restaurant a day INDEPENDENT- variable which affects the dependent variable. Example: set of good food in the menu  LEVELS OF MEASUREMENTS/VARIABLES NOMINAL/CATEGORICAL- it refers to categories such as gender, civil status, name of schools, etc. ORDINAL- similar to nominal, the difference between the two is that ordinal is in a clear order.  LEVELS OF MEASUREMENTS/VARIABLES INTERVAL- it is between the values of the interval variable are equally spaced. (it can be negative number) RATIO- it has all the similar properties of interval variable. (it is always a positive number)  MAKING INFERENCES FINDINGS- are results of an investigation. CONCLUSION- an opinion based on findings; a generalization on population based on the result of the investigation on samples. INFERENCE- an educated guess or a meaningful prediction based on findings and conclusions.  FINDINGS EXAMPLE Example: 64% of the 100 sample service crews in randomly selected Jollibee outlets are 21 years old and below. CONCLUSION Example: 1. I therefore conclude that 64% of all Jollibee employees nationwide are 21 years old and below. 2. I therefore conclude that Jollibee prefers employees who are 21 years old and below. INFERENCE Example: If this trend continues, then job  EXAMPLE: FINDINGS Example: Survey says that 66% of the Senior High School Students agreed to conduct a Junior-Senior Prom this year. CONCLUSION Therefore, I would like to conclude that most SHS Students agreed to conduct a JHS Prom this year. INFERENCE There will be JHS Prom this year.  SAMPLE Sampling- act or process of selecting appropriate sample or representative part of the population.  SAMPLING TECHNIQUES Probability Sampling- these are samples which are chosen in such a way that each member of the population has a known though not necessarily equal chance of being included in the sample; known as unbiased samples. Non-probability Sampling- each member of the population does not have a known chance of being included in the sample. Instead, personal judgment plays a very TYPES OF PROBABILITY SAMPLING SIMPLE RANDOM SAMPLING- all members of the population have a chance of being included in the sample. example: Lottery sampling and using the table of random numbers. TYPES OF PROBABILITY SAMPLING SYSTEMATIC SAMPLING- It selects every nth member of the population with the starting point determine at random. example: Selecting the 4th member of N= 100 with 5 random samples. TYPES OF PROBABILITY SAMPLING STRATIFIED RANDOM SAMPLING- used when the population can be subdivided into several smaller groups or strata, and then samples are randomly selected from each stratum. TYPES OF PROBABILITY SAMPLING CLUSTER SAMPLING- also known as area sampling. Usually used when the population is very large. In this technique groups or clusters instead of individuals are randomly picked. TYPES OF NON-PROBABILITY SAMPLING CONVENIENCE SAMPLING- process of getting the sample in a convenience way; like getting the sample thru phone or email. TYPES OF NON-PROBABILITY SAMPLING QUOTA SAMPLING- this is very similar to the stratified random sampling. The only difference is that the quota sampling has its target number only. example: To get the most popular noontime show, each field researcher is given a TYPES OF NON-PROBABILITY SAMPLING PURPOSIVE SAMPLING- this is used when the number of population is less than 100.  DATA GATHERING TECHNIQUES DIRECT OR INTERVIEW METHOD- has a direct contact with the respondents, one person to another; researcher can get more accurate information, yet this is costly and time consuming.  DATA GATHERING TECHNIQUES INDIRECT OR QUESTIONNARE/SURVEY METHOD- this method can gather a lot of information, and saves time and money. It can be gathered personally or thru email/mail, yet researchers can’t expect that all the questionnaires where  DATA GATHERING TECHNIQUES REGISTRATION METHOD- method of gathering data is governed by laws. Samples are: number of registered cars, number of registered voters. This is the most reliable method.  DATA GATHERING TECHNIQUES EXPERIMENTAL METHOD- used to find out the cause and effect.