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The GAISE College Report begins by synthesizing the history and current understanding of introductory statistics courses and then lists goals for students based on statistical literacy. [13] Six recommendations for introductory statistics courses are given, namely: [14] Emphasize statistical thinking and literacy over other outcomes
The report includes a brief history of the introductory statistics course and recommendations for how it should be taught. In many colleges, a basic course in "statistics for non-statisticians" has required only algebra (and not calculus); for future statisticians, in contrast, the undergraduate exposure to statistics is highly mathematical.
Bert Wachsmuth "Statistics in the Classroom on Touch-based Smart Phones" The Impact of Pen and Touch Technology on Education, Part of the Human–Computer Interaction Series pp 289–296, Springer (2015) Webster West "Social Data Analysis with StatCrunch: Potential Benefits to Statistical Education" UCLA Department of Statistics (2009)
The model for a randomized block design with one nuisance variable is = + + + where Y ij is any observation for which X 1 = i and X 2 = j X 1 is the primary factor X 2 is the blocking factor μ is the general location parameter (i.e., the mean)
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.
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Interaction effect of education and ideology on concern about sea level rise. In statistics, an interaction may arise when considering the relationship among three or more variables, and describes a situation in which the effect of one causal variable on an outcome depends on the state of a second causal variable (that is, when effects of the two causes are not additive).
For example, if a researcher uses presence of a land-grant college as an instrument for college education in an earnings regression, she identifies the effect of college on earnings in the subpopulation which would obtain a college degree if a college is present but which would not obtain a degree if a college is not present.