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Osteoarthritis (OA) is a type of degenerative joint disease that results from breakdown of joint cartilage and underlying bone.[5][6] It is believed to be the fourth leading cause of disability in the world, affecting 1 in 7 adults in the United States alone.[7] The most common symptoms are joint pain and stiffness.[1] Usually the symptoms ...
- Based on symptoms, supported by other testing
- Over years
The medial collateral ligament ( MCL ), also called the superficial medial collateral ligament ( sMCL) or tibial collateral ligament ( TCL ), [1] is one of the major ligaments of the knee. It is on the medial (inner) side of the knee joint and occurs in humans and other primates. Its primary function is to resist valgus (inward bending) forces ...
- Ligamentum collaterale tibiale
- D017888
9400 West Higgins Road, Rosemont, Illinois, 60018-4976. Location. United States. Website. www .aaos .org. The American Academy of Orthopaedic Surgeons ( AAOS) is an orthopedic organization. Founded at Northwestern University in 1933, as of 2015 AAOS had grown to include about 39,000 members. [1] The group provides education and practice ...
Alzheimer's disease ( AD) is a neurodegenerative disease that usually starts slowly and progressively worsens, [2] and is the cause of 60–70% of cases of dementia. [2] [15] The most common early symptom is difficulty in remembering recent events. [1] As the disease advances, symptoms can include problems with language, disorientation ...
- Over 65 years old
- History
- Regression Model
- Underlying Assumptions
- Linear Regression
- Nonlinear Regression
- Prediction
- Power and Sample Size Calculations
- Other Methods
- Software
- Further Reading
The earliest form of regression was the method of least squares, which was published by Legendre in 1805, and by Gauss in 1809. Legendre and Gauss both applied the method to the problem of determining, from astronomical observations, the orbits of bodies about the Sun (mostly comets, but also later the then newly discovered minor planets). Gauss pu...
In practice, researchers first select a model they would like to estimate and then use their chosen method (e.g., ordinary least squares) to estimate the parameters of that model. Regression models involve the following components: 1. The unknown parameters, often denoted as a scalar or vector β {\displaystyle \beta } . 2. The independent variables...
By itself, a regression is simply a calculation using the data. In order to interpret the output of regression as a meaningful statistical quantity that measures real-world relationships, researchers often rely on a number of classical assumptions. These assumptions often include: 1. The sample is representative of the population at large. 2. The i...
In linear regression, the model specification is that the dependent variable, y i {\displaystyle y_{i}} is a linear combination of the parameters (but need not be linear in the independent variables). For example, in simple linear regression for modeling n {\displaystyle n} data points there is one independent variable: x i {\displaystyle x_{i}} , ...
When the model function is not linear in the parameters, the sum of squares must be minimized by an iterative procedure. This introduces many complications which are summarized in Differences between linear and non-linear least squares.
Regression models predict a value of the Y variable given known values of the X variables. Prediction within the range of values in the dataset used for model-fitting is known informally as interpolation. Prediction outside this range of the data is known as extrapolation. Performing extrapolation relies strongly on the regression assumptions. The ...
There are no generally agreed methods for relating the number of observations versus the number of independent variables in the model. One method conjectured by Good and Hardin is N = m n {\displaystyle N=m^{n}} , where N {\displaystyle N} is the sample size, n {\displaystyle n} is the number of independent variables and m {\displaystyle m} is the ...
Although the parameters of a regression model are usually estimated using the method of least squares, other methods which have been used include: 1. Bayesian methods, e.g. Bayesian linear regression 2. Percentage regression, for situations where reducing percentageerrors is deemed more appropriate. 3. Least absolute deviations, which is more robus...
All major statistical software packages perform least squares regression analysis and inference. Simple linear regression and multiple regression using least squares can be done in some spreadsheetapplications and on some calculators. While many statistical software packages can perform various types of nonparametric and robust regression, these me...
William H. Kruskal and Judith M. Tanur, ed. (1978), "Linear Hypotheses," International Encyclopedia of Statistics. Free Press, v. 1,
