chap02 modify.pptdd

WEEK #2Prepared by :

Nani Kurniati, MT

Nani Kurniati, MT RI 1351 / Gasal 0809

ContentDescribing variationImportant continuous distributionProbability plotInference process qualitySampling distributionInference for single sampleInference for two sampleInference for more than two population


Easy to find percentiles of the data; see page 43Stem-and-Leaf Display


Plot of Data in Time Order dot plotAlso called a run chartMarginal plot produced by MINITAB


Histograms Useful for large data sets




Numerical Summary of DataSample averageSample varianceSample standard deviation


The Box Plot(or Box-and-Whisker Plot)


Comparative Box Plots


Probability Distributions


Will see many examples in the textSometimes called a probability mass functionSometimes called a probability density function


Characteristics probability distribution MeanvarianceStandard deviation



Standard normal distribution


Example


Example


Practical interpretation the sum of independent random variables is approximately normally distributed regardless of the distribution of each individual random variable in the sum


Determining if a sample of data might reasonably be assumed to come from a specific distributionProbability plots are available for various distributionsEasy to construct with computer software (MINITAB)Subjective interpretation


Normal Probability Plot



Other Probability PlotsWhat is a reasonable choice as a probability model for these data?



Inference process quality



t Distribution



F Distribution


Point EstimationTable c4







Inference for single sampleIn some situations we may wish to reject Ho only if the true mean is larger than oThus, the one-sided alternative hypothesis is H1: >o, and we would reject Ho: =o only if Zo>ZIf rejection is desired only when t,n 1, and If H1: 1 < 0, reject H0 if t0 < t,n 1

One could also compute the P-value for a t-testUnkonwn variance



































chap02 modify.pptdd

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