Exam for chapter 2+1 Study Guide
Exam for chapter 2+1 Study Guide STAT 239
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This 3 page Study Guide was uploaded by Brittany Wittrock on Friday September 11, 2015. The Study Guide belongs to STAT 239 at St. Cloud State University taught by Ernst, Michael in Fall 2015. Since its upload, it has received 61 views. For similar materials see Statistics for the Biological and Physical Sciences in Statistics at St. Cloud State University.
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Date Created: 09/11/15
Statistics 239 summary of notes for final exam Study Guide Chapter 1 Statistics science of collecting describing and analyzing data Variable any characteristic that is recorded for each case Categorical divides into groups placing each in one of two or more categoriesoptions Quantitative measuring numerically for each case Population all individuals or objects of interest Sample subset of population Statistical Inference process of using data from a sample to gain information about the population Sampling Bias occurs when the method of selecting the sample causes the sample to differ from the population Simple Random Sampling n units all groups of size n in the population have the same chance of becoming sample Bias exists when the method of collecting data causes the sample data in inaccurately re ect the population Explanatory Variable what is being tested Response Variable what result is looked for cannot get a random by picking and choosing Associated two variables are associated if values of one variable tend to relate to the values of the other Causally Associated changing the value of one variable in uences the value of the other variable Confounding VariableFactorLurking Variable is a third variable that is associated with both the explanatory and response variables Plausible association between two variables of interest Direct In uence Experiment is a study where the researcher controls one or more explanatory variables Observational Study Researcher has no control over any variablesimply observes rarely used to establish causality Randomized Experiments value of explanatory variable for each unit is determined randomly before the response variable is measured Randomized Comparative Experiment randomly assign cases to different treatment groups then compare results of response variables Matched Pairs Experiment each case gets both treatments in random order and we examine individual differences in the response variable between the two treatments Chapter 2 Summary Statistics visualizing data into graphs and see how to summarize key aspects of the data using numerical quantities Proportion proportionnumber in that categorytotal percentage for a sample it is represented by phat xtotaly population prho xtotaly Twoway Table used to show the relationship between two categorical variables Dotplot common way to visualize the shape of a moderately sized data set Outlier is an observed value that is notably distinct from other values in a data set Shapes for distribution Symmetric two sides match when folded down the center Skewed Right data is piled to the left and tail extends to the right Skewed Left data is piled to the right and tail extends to the left BellShaped data is symmetric and looks like a bell Mean meansum of all data valuesnumber of data values sample xbar population mue Mediansummarize the center of a set of numbers middle entry if odd average of two numbers if even of entries sample m Resistance related to the impact of outliers on a statistic median is resistant mean is not resistant is relatively unaffected by extreme values Standard Deviation for quantitative variables measures the spread of the data in a sample s pth percentile value of quantitative variable which is greater than P percent of the data Five Number Summary minimum Q1 Median Q3 Max Qquartiles Range rangemaximumminimum Interquartile Range IQRQ3Q1 Boxplot See page 91 in Statistics Unlocking the Power of Data Outlier smaller than Q115IQR or greater than Q315IQR Sidebyside graphs used to visualize the relationship between a quantitative variable and a categorical variable 2 score fancy zXXbars calculating outliers Scatter Plot graph showing the relationship between two quantitative variables Properties of the correlation The sample correlation r has the following properties Always between 1 and 1 inclusive Values close to either side show a strong linear relationship Numbers close to zero show little to no linear relationship The correlation r has no units and is independent of the scale of either variable The correlation is symmetriccorrelation between X and y are same as that of y and X The population correlation p also ful ls these properties Sample r Population p Cautions for Correlations 1 strong or relationships don t necessarily imply a cause and effect relationship 2 near zero correlation doesn t necessarily mean the variables are not associated only measures linear relationship 3 correlation can be heavily in uenced by outliersplot your data Regression lineslope for graphs YhatabX Response a bEXplanatory
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