# Statistics ch.1.2

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 Explanatory Variable quantity of something that varies and can be used to explain increases and decreases in another variable. Explanatory variables for recreational waters include rainfall, wave height, and turbidity, and are used to explain increases and decreases in E. coli concentrations. Response variable The response variable, y , is a quantity that varies in a way that we hope to be able to summarize and exploit via the modeling process. … Observation study measures the value of the response variable without attempting to influence the value of either the response or explanatory variables. that is, an observational study, the researcher observes the behavior of the individuals in the study without trying to influence the outcome of the study. – there are three major categories of observational studies: cross-sectional studies, case-control studies and cohort studies. -observational studies do not allow a researcher to claim causation, only association. Designed Experiment if a researcher assigns the individuals in a study to a certain group, intentionally changes the value of the explanatory variable, and then records the value of the response variable for each group, the researcher is conducting a designed experiment. Confounding confounding in a study occurs when the effects of two or more explanatory variables are not separated. therefore, any relation that may exist between an explanatory variables not accounted for in the study. – confounding is potentially a major problem with observational studies. often the cause of confounding is a lurking variable. Lurking Variable A lurking variable is an explanatory variable that was not considered in a study, but that affects the value of the response variable in the study. in addition, lurking variables are typically related to explanatory variables considered in the study. Cross-sectional studies these are observational studies that collect information about individuals at a specific point in time or over a very short period of time. -for example: a researcher might want to assess the risk associated with smoking by looking at a group of people, determining how many are smokers and comparing the incidence rate of lung cancer of the smokers to the nonsmokers advantage of cross-sectional study is that they are cheap and quick to do downfall of cross-sectional study have limitations such as for our lung cancer study it could be that individuals develop cancer after the data are collected or our study will not give a full picture

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