mm5 - Statisticalinferenceuses impersonalchancetodraw

Info iconThis preview shows pages 1–12. Sign up to view the full content.

View Full Document Right Arrow Icon
   
Background image of page 1

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
    Statistical inference  uses  impersonal chance to draw  conclusions about a population or  process based on data drawn  from a random sample or  randomized experiment.   
Background image of page 2
     When data are produced by random  sampling or randomized experiment, a  statistic is a  random variable  that obeys the  laws of probability.   
Background image of page 3

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
      sampling distribution  shows how a  statistic would vary with repeated random  sampling of the same size and from the  same population.    A sampling distribution, therefore, is a  probability distribution of the results of an  infinitely large number of such samples.
Background image of page 4
       A  population distribution  of a random  variable is the distribution of its values for all  members of the population.    Thus a population distribution is also the  probability distribution of the random variable  when we choose one individual (i.e.  observation or subject) from the population at  random.
Background image of page 5

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
      Recall that a sampling distribution is a conceptual  ideal: it helps us to understand the logic of drawing  random samples of size- from the same population  in order to obtain statistics by which we make  inferences about a parameter.  Population distribution is likewise a conceptual  ideal: it tells us that sample statistics are based on  probabilities attached to the population from which  random samples are drawn.
Background image of page 6
    Counts & Sample Proportions
Background image of page 7

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
     Count: random variable X is a  count  of the  occurrences of some outcome—of some  ‘success’ versus a corresponding ‘failure’—in a  fixed number of observations.   A count is a discrete random variable that  describes  categorical   data (concerning success  vs. failure).
Background image of page 8
       Sample proportion: if the number of  observations is  n , then the  sample proportion  of  observations is X/ n  A sample proportion is also a discrete random  variable that describes  categorical  data  (concerning success vs. failure).
Background image of page 9

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
    proportions are premised on a  binomial  setting .
Background image of page 10
  The Binomial Setting 1. There are a fixed number  n  of observations. 2.  The  n  observations are all independent. 3.  Each observation falls into one of just two categories, which  for convenience we call ‘success’ or ‘failure.’ 4. The probability of a success,  p , is the same for each  observation. 5.
Background image of page 11

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
Image of page 12
This is the end of the preview. Sign up to access the rest of the document.

Page1 / 59

mm5 - Statisticalinferenceuses impersonalchancetodraw

This preview shows document pages 1 - 12. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online