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The Normal Distribution PSYC 6130, PROF. J. ELDER 2 is the mean is the standard deviation The height of a normal density curve at any point x is given.

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Presentation on theme: "The Normal Distribution PSYC 6130, PROF. J. ELDER 2 is the mean is the standard deviation The height of a normal density curve at any point x is given."— Presentation transcript:

1

2 The Normal Distribution

3 PSYC 6130, PROF. J. ELDER 2 is the mean is the standard deviation The height of a normal density curve at any point x is given by Normal Distribution

4 PSYC 6130, PROF. J. ELDER 3 Normal Distribution A Normal Distribution has a symmetric, unimodal and bell- shaped density curve. The mean and standard deviation completely specify the curve. The mean, median, and mode are the same.

5 PSYC 6130, PROF. J. ELDER 4 Probabilities and the Normal Distribution Shaded area = 0.683Shaded area = 0.954Shaded area = and       99.7% chance of falling between and           

6 PSYC 6130, PROF. J. ELDER 5 Z-Scores The z-score is a normalized representation of a random variable with zero mean and unit variance. z-scores are dimensionless. The z-score tells you how many standard deviations a score lies from the mean.

7 PSYC 6130, PROF. J. ELDER 6 The Standard Normal Table: (Appendix z) A table of areas (probabilities) under the standard normal density curve. The table entry for each value z is the area under the curve between the mean and z.

8 PSYC 6130, PROF. J. ELDER 7

9 8 Estimating percentiles using z-scores From a frequency table we can directly compute percentiles and percentile ranks. If we model the data as normal, we can also calculate percentiles and percentile ranks using z-scores.

10 PSYC 6130, PROF. J. ELDER 9 Z-Scores

11 PSYC 6130, PROF. J. ELDER 10 Z-Scores

12 PSYC 6130, PROF. J. ELDER 11 Z-Scores

13 PSYC 6130, PROF. J. ELDER 12 Z-Scores

14 PSYC 6130, PROF. J. ELDER 13 Z-Scores

15 PSYC 6130, PROF. J. ELDER 14 Sampling distribution of the mean

16 PSYC 6130, PROF. J. ELDER 15 Sampling Distribution of the Mean

17 PSYC 6130, PROF. J. ELDER 16 Properties of the Sampling Distribution of the Mean

18 PSYC 6130, PROF. J. ELDER 17 Example Chest measurements of 5738 Scottish soldiers by Belgian scholar Lambert Quetelet ( ) –First application of the Normal distribution to human data

19 PSYC 6130, PROF. J. ELDER 18 The sample mean has a sampling distribution Sampling batches of Scottish soldiers and taking chest measurements. Pop mean = 39.8 in, Pop sd = 2.05 in

20 PSYC 6130, PROF. J. ELDER samples of size 24

21 PSYC 6130, PROF. J. ELDER 20 Histograms from 100,000 samples

22 PSYC 6130, PROF. J. ELDER 21 Example: Height of US Males, Sampling Distribution of the Mean Height of US Males (in) Probability p n=1 n=9 n=100

23 PSYC 6130, PROF. J. ELDER 22 The Central Limit Theorem

24 PSYC 6130, PROF. J. ELDER 23 Example: a uniform distribution n=1 n=2 n=10 n=100

25 PSYC 6130, PROF. J. ELDER 24 Example: a chi-squared distribution n=1 n=2 n=10 n=100

26 PSYC 6130, PROF. J. ELDER 25 Z-Scores for Groups

27 PSYC 6130, PROF. J. ELDER 26 Example: IQ Tests

28 PSYC 6130, PROF. J. ELDER 27 Underlying Assumptions Population is normally distributed Random sampling –Every sample of size n has the same probability of being selected. All individuals have the same probability of being selected. Selection of each individual is independent of the selection of all other individuals. Technically, sampling should be with replacement, but in Psychology, sampling is normally without replacement.

29 End of Lecture 3 Sept 24, 2008


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