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Lecture (11 ,12)

Lecture (11 ,12). Parameter Estimation of PDF and Fitting a Distribution Function. How can we specify a distribution from the data?. Two steps procedure: Decide which family to use (Normal, Log-normal, exponential …, etc. This step is done by:

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Lecture (11 ,12)

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  1. Lecture (11,12) Parameter Estimation of PDF and Fitting a Distribution Function

  2. How can we specify a distribution from the data? • Two steps procedure: • Decide which family to use (Normal, Log-normal, • exponential …, etc. • This step is done by: • - Guess the family by looking at the observations. • - Use the Chi-square goodness-of-fit test to test our • guess. • 2. Decide which member of the chosen family to use. • This means specify the values of the parameters. • This is done by producing estimates of the parameters • based on the observations in the sample.

  3. Estimation Estimation has to do with the second step. 2. Decide which member of the chosen family to use. This means specify the values of the parameters.

  4. General Concept of Modelling

  5. Point Estimates • A point estimate of an unknown parameter is a number • which to the best of our knowledge represents • the parameter-value. • Each random sample can give an estimator. • So, the estimator is regarded as a random variable. • A good estimator has the following: • It gives a good result. Not always too big or always • too small. • Unbiased. The expected value of the estimator • should be equal to the true value of the parameter. • 3. The variance is small.

  6. Unbiased Estimators

  7. Method of Moments

  8. Method of Moments (Cont.)

  9. Mean of the Means

  10. Standard Deviation of the Means

  11. Confidence Intervals When we estimate a parameter from a sample the estimation can be different from different samples. It would be better to indicate reliability of the estimate. This can be done by giving the confidence of the result.

  12. E E Confidence Intervals for the mean

  13. E E Confidence Interval For the Mean (cont.) • A general expression for a 100(1-)% confidence interval for the mean is given by:

  14. Confidence Interval For the Mean (Cont.) • According to the above formula we have • 90% • 95% • 98% • 99% • These formulae apply for any population as long as the sample size is sufficiently large for the central limit theorem to hold

  15. Statistical Inference for The population Variance • For normal populations statistical inference procedures are available for the population variance • The sample variance S2 is an unbiased estimator of 2 • We assume we have a random sample of n observations from a normal population with unknown variance 2 .

  16. The Chi Square Distribution • If the population is Normal with variance 2 then the statistic • Has a Chi Square distribution with (n-1) degrees of freedom

  17. Confidence Region For The Variance • Using this result a confidence interval for 2 is given by the interval:

  18. Confidence Region For The Variance (n-1) d.f.

  19. Estimation of the Confidence Intervals of the variance

  20. Fitting a Distribution Function

  21. Goodness-of-Fit Test A goodness-of-fit test is an inferential procedure used to determine whether a frequency distribution follows a claimed distribution.

  22. Hypothesis Testing • Hypothesis: • A statement which can be proven false • Null hypothesis Ho: • “There is no difference” • Alternative hypothesis (H1): • “There is a difference…” • In statistical testing, we try to “reject the null hypothesis” • If the null hypothesis is false, it is likely that our alternative hypothesis is true • “False” – there is only a small probability that the results we observed could have occurred by chance

  23. Application of Testing hypothesis on Goodness of Fit Testing Hypothesis: Ho: the null hypothesis is defined as the distribution function is a good fit to the empirical distribution. H1: the alternative hypothesis is defined as the distribution function is not a good fit to the empirical distribution. Testing of hypothesis is a procedure for deciding whether to accept or reject the hypothesis. The Chi-squared test can be used to test if the fit is satisfactory.

  24. Testing Goodness of Fit of a Distribution Function to an Empirical Distribution Decision Unknown real situation

  25. Common Values for Significant Levels

  26. The Chi-Square Distribution • It is not symmetric. • The shape of the chi-square distribution depends upon the degrees of freedom. • As the number of degrees of freedom increases, the chi-square distribution becomes more symmetric as is illustrated in the figure. • 4. The values are non-negative. That is, the values of are greater than or equal to 0.

  27. Chi2 Degrees of Freedom • All statistical tests require the compotation of degrees of freedom • Chi2 df = (No. classes -1)

  28. Critical Values of Chi2

  29. Chi-Square Table

  30. Procedure for Chi Square Test Step 1: A claim is made regarding a distribution. The claim is used to determine the null and alternative hypothesis. Ho: the random variable follows the claimed distribution H1: the random variable does not follow the claimed distribution

  31. Procedure for Chi Square Test (cont.) Step 2: Calculate the expected frequencies for each of the k classes. The expected frequencies are Ei,i = 1, 2, …, k assuming the null hypothesis is true.

  32. Procedure for Chi Square Test (cont.) • Step 3: Verify the requirements fort he goodness-of-fit test are satisfied. • (1) all expected frequencies are greater than or equal to 1 (all Ei> 1) • (2) no more than 20% of the expected frequencies are less than 5.

  33. Procedure for Chi Square Test (cont.)

  34. Procedure for Chi Square Test (cont.)

  35. Procedure for Chi Square Test (cont.)

  36. Example 1 (Discrete Variable)

  37. Example 1 (cont.) • Observed Frequency • The obtained frequency for each category.

  38. Example 1 (cont.) • State the research hypothesis. • Is the rat’s behavior random? • State the statistical hypotheses.

  39. Example 1 (cont.) .25 .25 .25 .25 If picked by chance.

  40. Example 1 (cont.) • Expected Frequency • The hypothesized frequency for each distribution, given the null hypothesis is true. • Expected proportion multiplied by number of observations.

  41. Example 1 (cont.) • Set the decision rule.

  42. Example 1 (cont.) • Set the decision rule. • Degrees of Freedom • Number of Categories -1 • (C) –1

  43. Example 1 (cont.) • Set the decision rule.

  44. Example 1 (cont.) • Calculate the test statistic.

  45. Example 1 (cont.) • Calculate the test statistic.

  46. Do Not Reject H0 Reject H0 2 Example 1 (cont.) • Decide if your result is significant. • Reject H0, 9.25>7.81 • Interpret your results. • The rat’s behavior was not random. 7.81

  47. Example 2 (Continuous Variable) Observed frequency of 10 size classes of shale thicknesses,

  48. Example (Cont.)

  49. Example (Cont.)

  50. 16.92 Chi2 Graph

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