IPadThe iPad is a brand of iOS and iPadOS-based tablet computers that are developed by Apple Inc, first introduced on January 27, 2010. The iPad range consists of the original iPad lineup and the flagship products iPad Mini, iPad Air, and iPad Pro. The iPhone's iOS operating system (OS) was initially used for the iPad but in September 2019, its OS was switched to a fork of iOS called iPadOS that has better support for the device's hardware and its user interface is customized for the tablets' larger screens.
IPad ProThe iPad Pro is a premium model of Apple's iPad tablet computer. It runs iPadOS, a tablet-optimized version of the iOS operating system. The original iPad Pro was introduced in September 2015, and ran iOS 9. It had an A9X chip, and came in two sizes: 9.7-inch and 12.9 inch. The second-generation iPad Pro, unveiled in June 2017, had an upgraded A10X Fusion chip and swapped the 9.7-inch screen for a larger 10.5-inch display. The third-generation iPad Pro, announced in October 2018, eliminated the home button, and featured Face ID; it came in 11-inch and 12.
IPad MiniThe iPad Mini (branded and marketed as iPad mini) is a line of mini tablet computers designed, developed, and marketed by Apple Inc. It is a sub-series of the iPad line of tablets, with screen sizes of 7.9 inches and 8.3 inches. The first-generation iPad Mini was announced on October 23, 2012, and was released on November 2, 2012, in nearly all of Apple's markets. It featured similar internal specifications to the iPad 2, including its display resolution.
Linear regressionIn statistics, linear regression is a linear approach for modelling the relationship between a scalar response and one or more explanatory variables (also known as dependent and independent variables). The case of one explanatory variable is called simple linear regression; for more than one, the process is called multiple linear regression. This term is distinct from multivariate linear regression, where multiple correlated dependent variables are predicted, rather than a single scalar variable.
Linear least squaresLinear least squares (LLS) is the least squares approximation of linear functions to data. It is a set of formulations for solving statistical problems involved in linear regression, including variants for ordinary (unweighted), weighted, and generalized (correlated) residuals. Numerical methods for linear least squares include inverting the matrix of the normal equations and orthogonal decomposition methods. The three main linear least squares formulations are: Ordinary least squares (OLS) is the most common estimator.
