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,单击此处编辑母版标题样式,单击此处编辑母版文本样式,第二级,第三级,第四级,第五级,2013/9/5,#,AP Statistics,Lu Hao,2.3,An experiment is a planned intervention undertaken to observe the effects of one or more explanatory variables.,Explanatory variable(factor),Response variable,Experimental condition/treatment:,(combination of values for explanatory variable),Design of a experiment: overall plan,Extraneous factor,Directly control(holding some extraneous factors constant),Blocking(create groups/blocks that are similar, try all treatment in each block),Randomization(random assignment),Replication(making multiple observation for each experimental condition),Definition of Blocking,Using extraneous factors to create groups that are similar.,All experimental conditons are then tried in each block.,Example for Blocking,A,n,experiment is designed to test a new drug on patients. There are two levels of the treatment,drug, and,placebo, administered to,male,and,female,patients in a,double blind,trial. The sex of the patient is a,blocking,factor accounting for treatment,variability,between,males,and,females,. This reduces sources of variability and thus leads to greater precision.,The ideal situation would be to have both,random selection of subjects,and,random assignment,of subjects to experimental conditons,Homework,P40, 19a,b,c,d,P49, 32,P67 62,Read section 2.5 and 2.6,3.1,Comparative Bar Chart,For categorical data,Use relative frequency,Pie chart,Categorical data,Small number of possible categories,For illustrating proportions,segmented bar chart(P81),3.2 Stem-and-leaf displays,For univariate numerical data,Does not work well for very large data sets,Def:,Outlier an unusually small or large data value,Stem-and-leaf displays,What to look for:,gaps, symmetry, peaks, outliers,representative or typical value,Repeated stems to stretch a display,Comparative,stem-and-leaf,display(92),3.3Frequency distribution and histogram for numerical data,Discrete variable,Continuous variable,Histogram for discrete numerical data,Works well even for large data set,Example 3.11(P97),Example 3.13(P99),Histogram for continuous variable,There are no natural categories,so we need to define our own categories,Each data,value should,fall in exactly one of these,intervals,Histogram works well, even for large data sets,What to look for:,central or typical value,extent of spread or variation,general shape,location and number of peaks,Presence of gaps and ourliers,Cumulative relative frequency,Cumulative relative frequency plot,A,cumulative relative frequency plot,is just a graph of the cumulative relative frequencies,against the upper endpoint,of the corresponding interval,.,We can know:,what proportion of the observation is smaller than a particular value;,what value separates the smallest p percent from the large values.,Read,P,107- P108,Histogram shape,Unimodal: single peak,Bimodal: two peaks,Multimodal: more than two peaks,Symmetric unimodal histogram,A unimodal histogram is,symmetric,if,there is a vertical line of symmetry such that the part of the histogram,to the,left of the line is a mirror image of the part to the right,.,Lower tail Upper tail,Skewed unimodal histogram,Positive-skewed or right-skewed,Negative-skewed or left-skewed,Heavy tail,Normal tail,Light-tail,Read P111,Positvely-skewed/right-skewed,Modemedianmean,
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