Question

Detail, with very specific examples of each item, the meaning of the following concepts related to statistical data gathering: Be sure that the concept is first defined, IN YOUR OWN WORDS, and that you use a concrete example to illustrate your understanding of the statistical concept: variable; latent variables; manifest variables; operational hypothesis; constants; independent variable; dependent variable; status variable; validity; reliability; epistemic relationship; data collection; unit of analysis; population; sample; representativeness; random sample; discrete variable; dichotomus variable; continuous variable; discriptive statistics; inferential statistics; inference; statistical significance; measures of association.

Answer 1

variable : it can hold all possible values of some process that may be discrete or continuous. eg. x = 1, 2, 3, 4....

latent variable : variable that can not be directly observed but infered from another variable. eg. voting score.

manifest variable: variable that can be directly observed or measured and used in statistical model of latent variable. eg. iq test score.

operational hypothesis: it is a research that clearly defines the variables of intrest and explain the corelation of variables with each other. eg. yield of crops.

constants: it is a value that cannot be changed. eg: number of minutes in an hour.

independent variable: it is a variable that can be changed and controlled in an experiment. eg: amount of mannure used in a field.

dependent variable: it is a variable taht is being effected by independent variable. eg: height of plant

status variable: variable that are stored directly into the variable table in the database.

validity: defines as a creditibility or believability of a research. eg: marks of an assignment predict the final score.

reliability: it mention the consistency of measuremnet. eg: study of human behaviour.

epistemic relationship: defined as a relation which holds between a reason and belief. eg: anlysis of truth

data collection: it is way of survey measuring and gathering information through organised sources like survey, magazine, eg: census data.

unit of analysis: it is a specific operation to be analysed. eg: GDP growth of a country.

population: it is a group of organism of same species in a particular habitat. eg: population of goat

sample: a single unit taken from a populaton to study the characteristic of whole population. eg: average income.

representativeness: representatve sample is taken by selection process which is reproducible as well as an accurate. this is identified as an absence of biasness and acceptable variance. eg: mean, median, mode.

random sample: selecting a sample from a population in such that every sample has an equal probability. eg: selection of fish from a pond.

discrete variable: it is a characters which is different from each other. it is a qualitative variable eg: nationality.

continuous variable: variable that can take any value within a specific range. it is a quantitave variable. eg: height, temperature.

dichotomus variable: they are the categorical variable with 2 categories or labels. eg: head or tail.

discriptive statistics: defined as a summary of data set or representative of data set. eg: quatrile, range.

inferential statistics: from a very large population we can draw inference of the characteristics of population or conclude the behaviour of data set. eg: height of students.

inference: on the basis of evidence we can rech to the conclusion. eg: ram is taller than shyam. shyam is taller then mohan. therefore we can draw the inference that ram is taller than mohan.

statistical significance: the probaability of type 1 error or quality of its occurrence purely by chance. eg: chance of head or tail.

measures of association: concern with how one variable is related to other variable, eg: demand and supply