Statistics

  Sampling & Sampling Distribution Population Parameter  :                                 A measure of location or a measure of dispersion is called a parameter if it describes a  population. One seldom has data for entire population and hence one rarely is able to calculate population  parameters. Characteristics of the part infers Characteristics of the whole. Standard Notation :    Sample statistics :   It refers to a characteristics of  a sample. It is denoted by lower case roman  letter. Population Parameter :  It  refers to a characteristics of as sample. It is denoted by lower case Greek letters.                         Selection Bias :                         Selection bias when some part of target population is not in the sampled population. Measurement Bias :                          Measurement bias occurs when measuring instrument has a tendency to differ from the true  vale in one direction.   Sampling Error :                          Sampling error result from takin

 SAMPLING AND SAMPLING DISTRIBUTIONS Introduction Sampling is pre-requisite of statistical inference. Sample data is used to find value of the sample  statistics which is used to estimate corresponding unknown population parameter. Sampling is  basically of two types namely probability sampling and non probability sampling. sampling design  comprises two things namely sampling technique and size of the sample. Basic Terms population:  The population is the entire group of individuala or objects under consideration. Target population :   The complete collection of observstions we want to study is called target  population. Sampled population :  The population from which the sample is taken is called sampled population. Element : An element is a single member of a population. Sample : Sample is a portion chosen from the population. The sample is a part or subset of population.  Information is obtained only from the cases included in the sample. A subset of the total number of  measuremen