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Thiyagu normal probability curve

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THIS SLIDES EXPLAIN ABOUT THE CONCEPT OF NORMAL PROBABILITY CURVE, SKEWNESS AND KURTOSIS.

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Thiyagu normal probability curve

  1. 1. K.THIYAGU thiyagusuri@gmail.com surithiyagu@gmail.com
  2. 2. Carl Gauss • The normal probability distribution or the “normal curve” is often called the Gaussian distribution, Carl Gauss
  3. 3. The shape of the curve is like that of a bell.
  4. 4.  Mean, median and mode carry the equal value. / same numerical value mean, median and mode  Therefore they fall at the same point on the curve
  5. 5.  Since the mean , median and mode lye at one point of the curve it is unimodal in nature.
  6. 6.  It means the curve inclines towards both sides equally from the centre of the curve.  Thus we get equal halves on both sides from the central point.  The curve is not skewed. Therefore the values of the measure of skewness is zero
  7. 7. A normal curve is symmetric about the mean. Each of the two shaded areas is .5 or 50% .5.5 μ x
  8. 8.  The curve does not touch the base or OX axis on both sides.  Thus it extends from negative infinity to positive infinitive.
  9. 9.  For practical purpose the base line of the curve is divided into six sigma distance from . Most of the cases I.e.99.73% are covered within such distance
  10. 10.  Maximum ordinate of the curve occurs at the mean. I.e. where Z1=0 and the value of the highest ordinate is 0.3989.  The height of the ordinate at 1sigma is 0.2420  2 sigma = 0.0540  3 sigma = 0.0044
  11. 11. The Theoretical Normal Curve (from http://www.music.miami.edu/research/statistics/normalcurve/images/normalCurve1.gif
  12. 12. 68-95-99.7 Rule • For any normal curve with mean mu and standard deviation sigma: • 68 percent of the observations fall within one standard deviation sigma of the mean. • 95 percent of observation fall within 2 standard deviations. • 99.7 percent of observations fall within 3 standard deviations of the mean.
  13. 13. 68-95-99.7 Rule 997. 2 1 95. 2 1 68. 2 1 3 3 )( 2 1 2 2 )( 2 1 )( 2 1 2 2 2 =• =• =• ∫ ∫ ∫ + − − − + − − − + − − − σµ σµ σ µ σµ σµ σ µ σµ σµ σ µ πσ πσ πσ dxe dxe dxe x x x
  14. 14. Properties (cont.) • Has a mean = 0 and standard deviation = 1. • General relationships: ±1 s = about 68.26% ±2 s = about 95.44% ±3 s = about 99.72% -5 -4 -3 -2 -1 0 1 2 3 4 5 68.26% 95.44% 99.72%
  15. 15.  The scale values are analysed as Z1=(X-M)/sigma Where z1 = 0 and range of mean plus or minus Z is equal to mean plus or minus sigma
  16. 16.  The points of infection are each plus or minus one sigma from above and below the mean.  The curve changes from convex to concave at these points with the baseline.
  17. 17. Various measures In the normal curve  Quartile deviation Q = Proper error = 0.6745a  Mean deviation, AD = 0.7979a  Skewness = 0  Kurtosis = 0.263
  18. 18. Properties of the Normal Curve • Bell-shaped • Unimodal – mean = median = mode • Symmetrical • Tails are asymptotic • 68,95 & 99.7 Rule • Maximum ordinate of the curve: • Points of infection
  19. 19. APPLICATIONS OF THE NORMAL PROBABILITY CURVE To normalize a frequency distribution. It is an important step in standardizing a psychological test or inventory. To test the significance of observations in experiments, findings them relationships with the chance fluctuations or errors that are result of sampling procedures. To generalize about population from which the samples are drawn by calculating the standard error of mean and other statistics. To compare two distributions. The NPC is used to compare two distributions. To determine the difficulty values. The Z scores are used to determine the difficulty values of test items. To classify the groups. The Normal Probability Curve (NPC) is used for classifying the groups and assigning grades to individuals. To determine the level of significance. The levels of significance of statistics results are determined in terms of NPC limits. To scale responses to opinionnaires, judgement, ratings or rankings by transforming them numerical values.
  20. 20.  Skewness means asymmetrial nature.  When the curve on clones towards right or left we cannot take it as a normal curve but as a skewed curve.  The degree of departure from symmetry is called symmetry.
  21. 21. Skewness  Positive Skewness: Mean ≥ Median  Negative Skewness: Median ≥ Mean  Pearson’s Coefficient of Skewness3 : = 3 (Mean –Median) Standard deviation
  22. 22.  When the curve inclines more to the left skewness becomes negative m ean m edian m ode Negative or Left Skew Distribution
  23. 23.  When the curve inclines more towards right m ean m edian m ode Positive or Right Skew Distribution
  24. 24. C.I. NORMAL CURVE POSITIVELY SKEWED NEGATIVELY SKEWED f f f 91 - 100 2 2 20 81 – 90 3 2 10 71 - 80 10 3 10 61 – 70 20 3 3 51 – 60 10 10 3 41 – 50 3 10 2 31 - 40 2 20 2
  25. 25. Reason
  26. 26.  Literally kurtosis means tendency of ‘flatness’ or ‘peakedness’.  The normal curve is moderately peaked.
  27. 27. kurtosis: the proportion of a curve located in the center, shoulders and tails How fat or thin the tails are leptokurtic no shoulders platykurtic wide shoulders
  28. 28.  When the curve is more flattened the distribution will be called as platy kurtoc
  29. 29.  When the curve is more peaked than normal one it is called as lepto kurtoc curve
  30. 30. 1090 PP Q Ku − = Where Q = quartile deviation, P90 = 90th percentile P10 = 10th percentile
  31. 31.  Biased selection of sample  Scoring errors  Improper construction of a test  Improper administration of a test  Abnormality in the traits of the items.

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