observed data can be viewed in relation to dp Definition 1 A sample is defined to be a set of random variables(X1, X2,. Xn) whose den- sity functions coincides with the"true"density function f(a; 00) as postulated by the probability model Data are generally drawn in one of two settings. A cross section sample is a sample of a number of observational units all drawn at the same point in time a time series sample is a set of observations drawn on the same observational unit at a number (usually evenly spaced) points in time. Many recently have been based on time-series cross sections, which generally consistent of the same cross section observed at several points in time. The term panel data set is usually fitting for this Given that a sample is a set of r v s related to it must have a distribution which we call the distribution of the sample The distribution of the sample x=(X1, X2, Xn), is defined to be the joint distribution of the r.v.'s X1, X2, .. Xn denoted by ∫x(x1,…,xn;)≡∫(x;) The distribution of the sample incorporates both forms of relevant informa tion, the probability as well as sample information. It must comes as no surprise to learn that f(a: 0) plays a very important role in statistical inference. The form of f(a: 8) depends crucially on the nature of the sampling model and as well as on the idea of a random experiment 2 and is called a random sanpete one based dp. The simplest but most widely used form of a sampling model is the Definition 3 A set of random variables(X1, X2,., Xn)is called a random sample from f(a: 0) if the r.v. 's X1, X2, Xn are independently and identically distributed (ii d ) Inobserved data can be viewed in relation to Φ. Definition 1: A sample is defined to be a set of random variables (X1, X2, ..., Xn) whose den￾sity functions coincides with the ”true” density function f(x; θ0) as postulated by the probability model. Data are generally drawn in one of two settings. A cross section sample is a sample of a number of observational units all drawn at the same point in time. A time series sample is a set of observations drawn on the same observational unit at a number (usually evenly spaced) points in time. Many recently have been based on time-series cross sections, which generally consistent of the same cross section observed at several points in time. The term panel data set is usually fitting for this sort of study. Given that a sample is a set of r.v.’s related to Φ it must have a distribution which we call the distribution of the sample. Definition 2: The distribution of the sample x ≡ (X1, X2, ..., Xn) ′ , is defined to be the joint distribution of the r.v.’s X1, X2, ..., Xn denoted by fx(x1, ..., xn; θ) ≡ f(x; θ). The distribution of the sample incorporates both forms of relevant informa￾tion, the probability as well as sample information. It must comes as no surprise to learn that f(x; θ) plays a very important role in statistical inference. The form of f(x; θ) depends crucially on the nature of the sampling model and as well as Φ. The simplest but most widely used form of a sampling model is the one based on the idea of a random experiment E and is called a random sample. Definition 3: A set of random variables (X1, X2, ..., Xn) is called a random sample from f(x; θ) if the r.v.’s X1, X2, ..., Xn are independently and identically distributed (i.i.d). In 2