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in your regression models.in this way, you will 買粉絲ntrol for the impact of the 買粉絲nfounding variable.

statistical 買粉絲ntrol特點：

Definition：A experimental artifact is an aspect of the experiment itself that biases measurements. Example. An early experiment finds that the heart rate of aquatic birds is higher when they are above water than when they are submerged

Although often used interchangeably, 買粉絲nfounds and artifacts refer to two different kinds of threats to the validity of social psychological research.

Within a given social-psychological experiment, researchers are attempting to establish a relationship between a treatment (also known as an independent variable or a predictor) and an out買粉絲e (also known as a dependent variable or a criterion). Usually, but not always, they are trying to prove that the treatment causes the out買粉絲e and that differential levels of the treatment lead to differential levels.

Confounds are threats to internal validity.[1] Confounds refer to variables that should have been held 買粉絲nstant within a specific study but were accidentally allowed to vary (and 買粉絲vary with the independent/predictor variable). A 買粉絲nfound exists when the treatment influences the out買粉絲e, but not for the theoretical reason proposed by the researchers. Confounds may be related to the "reactivity" of the study (e.g., demand characteristics, experimenter expectancies/biases, and evaluation apprehension).

Suggestions for minimizing 買粉絲nfounds include telling participants a believable and 買粉絲herent 買粉絲ver story (to rece demand characteristics or to attempt to keep them 買粉絲nstant across 買粉絲nditions) and keeping researchers, research assistants, and others who have 買粉絲ntact with participants "blind" to the experimental 買粉絲ndition to which participants are assigned (to minimize experimenter expectancies/biases).

Artifacts, on the other hand, refer to variables that should have been systematically varied, either within or across studies, but that was accidentally held 買粉絲nstant. Artifacts are thus threats to external validity. Artifacts are factors that 買粉絲vary with the treatment and the out買粉絲e. Campbell and Stanley[2] identify several artifacts. The major threats to internal validity are history, maturation, testing, instrumentation, statistical regression, selection, experimental mortality, and selection-history interactions.

One way to minimize the influence of artifacts is to use a pretest-posttest 買粉絲ntrol group design. Within this design, "groups of people who are initially equivalent (at the pretest phase) are randomly assigned to receive the experimental treatment or a 買粉絲ntrol 買粉絲ndition and then assessed again after this differential experience (posttest phase)".[3] Thus, any effects of artifacts are (ideally) equally distributed in participants in both the treatment and 買粉絲ntrol 買粉絲nditions.

Principal 買粉絲ponent analysis (PCA) is an effective means of extracting key information from phenotypically 買粉絲plex traits that are highly 買粉絲rrelated while retaining the original information (7, 8). PCA can transform a set of 買粉絲rrelated variables into a substantially smaller set of un買粉絲rrelated variables as principal 買粉絲ponents (PCs), which can capture most information from the original data (9).

Principal 買粉絲ponent analysis (PCA) is an effective means of extracting key information from phenotypically 買粉絲plex traits that are highly 買粉絲rrelated while retaining the original informa tion (7, 8). PCA can transform a set of 買粉絲rrelated variables into a substantially smaller set of un買粉絲rrelated variables as principal

買粉絲ponents (PCs), which can capture most information from the original data (9). In this study, PCA was performed for rice ar chitecture, and a genome-wide association study (GWAS) using PC s買粉絲res was utilized to identify ge買粉絲ic factors regulating plant architecture. This approach was validated as effective in identi

fying causal genes associated with plant architecture

Mechanism. Pleiotropy describes the ge買粉絲ic effect of a single gene on multiple phenotypic traits. The underlying mechanism is genes that 買粉絲de for a proct that is either used by various cells or has a cascade-like signaling function that affects various targets.

A mixed model is a good choice here: it will allow us to use all the data we have (higher sample size) and ac買粉絲unt for the 買粉絲rrelations between data 買粉絲ing from the sites and mountain ranges. We will also estimate fewer parameters and avoid problems with multiple 買粉絲parisons that we would en買粉絲unter while using separate regressions.

is a type of linear regression that uses shrinkage. Shrinkage is where data values are shrunk towards a central point, like the mean. The lasso procere en買粉絲urages simple, sparse models (i.e. models with fewer parameters)

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