PSC 211 Midterm Stud..
... descriptive statistics: methods for summarizing information so that it is more intelligible, more useful or can be communicated more effectively o i.e. calculating averages, graphing techniques (baseball stats) o can understand what the data actually means inferential statistics: procedures used to ...
... descriptive statistics: methods for summarizing information so that it is more intelligible, more useful or can be communicated more effectively o i.e. calculating averages, graphing techniques (baseball stats) o can understand what the data actually means inferential statistics: procedures used to ...
disc8
... Now here is the reason I spent so much time last class to tell you that an estimator is a function of sample/data x. If its a function of x , say T (x) , then we know how to nd its mean and variance. So intuitively we take say 100 such samples x and calculate 100 such estimators T (x) and we want t ...
... Now here is the reason I spent so much time last class to tell you that an estimator is a function of sample/data x. If its a function of x , say T (x) , then we know how to nd its mean and variance. So intuitively we take say 100 such samples x and calculate 100 such estimators T (x) and we want t ...
Moghadam
... logarithmic functions. Apply the knowledge of functions to business applications such as simple, compound or continuous compound interest, ordinary annuities, finding the maximum or minimum for quantities which are quadratic functions. Use geometric method to solve linear programming problems. Inter ...
... logarithmic functions. Apply the knowledge of functions to business applications such as simple, compound or continuous compound interest, ordinary annuities, finding the maximum or minimum for quantities which are quadratic functions. Use geometric method to solve linear programming problems. Inter ...
Exam Tips File
... 13. Type I and Type II Error: Type I error is the probability of rejecting a true null hypothesis. Type II error is the probability of failing to reject a false null hypothesis. 14. Power: 1-Type II error, the probability of rejecting a false null hypothesis. As sample size and type I error increase ...
... 13. Type I and Type II Error: Type I error is the probability of rejecting a true null hypothesis. Type II error is the probability of failing to reject a false null hypothesis. 14. Power: 1-Type II error, the probability of rejecting a false null hypothesis. As sample size and type I error increase ...
Statistics 501 Methods of Applies Statistics Using MINITAB
... and their probabilities, random sampling • Random variables and their distributions, • Expected value and variance of a random variable • (discrete or continuous), binomial, hypergeometric, and normal distributions • Sampling distribution -- random samples, statistic, sampling error • Sampling distr ...
... and their probabilities, random sampling • Random variables and their distributions, • Expected value and variance of a random variable • (discrete or continuous), binomial, hypergeometric, and normal distributions • Sampling distribution -- random samples, statistic, sampling error • Sampling distr ...
Least squares
The method of least squares is a standard approach in regression analysis to the approximate solution of overdetermined systems, i.e., sets of equations in which there are more equations than unknowns. ""Least squares"" means that the overall solution minimizes the sum of the squares of the errors made in the results of every single equation.The most important application is in data fitting. The best fit in the least-squares sense minimizes the sum of squared residuals, a residual being the difference between an observed value and the fitted value provided by a model. When the problem has substantial uncertainties in the independent variable (the x variable), then simple regression and least squares methods have problems; in such cases, the methodology required for fitting errors-in-variables models may be considered instead of that for least squares.Least squares problems fall into two categories: linear or ordinary least squares and non-linear least squares, depending on whether or not the residuals are linear in all unknowns. The linear least-squares problem occurs in statistical regression analysis; it has a closed-form solution. The non-linear problem is usually solved by iterative refinement; at each iteration the system is approximated by a linear one, and thus the core calculation is similar in both cases.Polynomial least squares describes the variance in a prediction of the dependent variable as a function of the independent variable and the deviations from the fitted curve.When the observations come from an exponential family and mild conditions are satisfied, least-squares estimates and maximum-likelihood estimates are identical. The method of least squares can also be derived as a method of moments estimator.The following discussion is mostly presented in terms of linear functions but the use of least-squares is valid and practical for more general families of functions. Also, by iteratively applying local quadratic approximation to the likelihood (through the Fisher information), the least-squares method may be used to fit a generalized linear model.For the topic of approximating a function by a sum of others using an objective function based on squared distances, see least squares (function approximation).The least-squares method is usually credited to Carl Friedrich Gauss (1795), but it was first published by Adrien-Marie Legendre.