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2. AP Statistics - Wikipedia

   en.wikipedia.org/wiki/AP_Statistics

   Advanced Placement (AP) Statistics (also known as AP Stats) is a college-level high school statistics course offered in the United States through the College Board's Advanced Placement program. This course is equivalent to a one semester, non- calculus -based introductory college statistics course and is normally offered to sophomores , juniors ...

3. Blocking (statistics) - Wikipedia

   en.wikipedia.org/wiki/Blocking_(statistics)

   The explanatory variable is the diet pill and the response variable is the amount of weight loss. Although the sex of the patient is not the main focus of the experiment—the effect of the drug is—it is possible that the sex of the individual will affect the amount of weight lost.

4. Explanatory power - Wikipedia

   en.wikipedia.org/wiki/Explanatory_power

   Explanatory power is the ability of a hypothesis or theory to explain the subject matter effectively to which it pertains. Its opposite is explanatory impotence . In the past, various criteria or measures for explanatory power have been proposed.

5. Design matrix - Wikipedia

   en.wikipedia.org/wiki/Design_matrix

   The design matrix has dimension n-by-p, where n is the number of samples observed, and p is the number of variables measured in all samples. [4] [5]In this representation different rows typically represent different repetitions of an experiment, while columns represent different types of data (say, the results from particular probes).

6. Instrumental variables estimation - Wikipedia

   en.wikipedia.org/wiki/Instrumental_variables...

   In the first stage, each explanatory variable that is an endogenous covariate in the equation of interest is regressed on all of the exogenous variables in the model, including both exogenous covariates in the equation of interest and the excluded instruments. The predicted values from these regressions are obtained:

7. Linear regression - Wikipedia

   en.wikipedia.org/wiki/Linear_regression

   A model with exactly one explanatory variable is a simple linear regression; a model with two or more explanatory variables is a multiple linear regression. [1] This term is distinct from multivariate linear regression, which predicts multiple correlated dependent variables rather than a single dependent variable. [2]

8. Response surface methodology - Wikipedia

   en.wikipedia.org/wiki/Response_surface_methodology

   In statistics, response surface methodology (RSM) explores the relationships between several explanatory variables and one or more response variables. RSM is an empirical model which employs the use of mathematical and statistical techniques to relate input variables, otherwise known as factors, to the response.

9. Controlling for a variable - Wikipedia

   en.wikipedia.org/wiki/Controlling_for_a_variable

   This is typically done so that the variable can no longer act as a confounder in, for example, an observational study or experiment. When estimating the effect of explanatory variables on an outcome by regression, controlled-for variables are included as inputs in order to separate their effects from the explanatory variables. [1]