The Normal distribution is explained and it is noted that there exists an infinite number of combinations for the pair of parameter values for the mean, μ, and the variance, σ2. Since we are often interested in finding probabilities associated with Normal distributions, the reason behind the production of statistical tables for the Standard Normal distribution is discussed. Consequently the important technique of standardisation is discussed to convert a non-standard Normal variable into a standard Normal one, hence allowing consultation of the statistical tables. Duration: 14 minutes 4 seconds.
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Video tutorial 5.2 Calculation of Normal probabilities
This recording applies the standardisation technique outlined in Recording 5.1 to two simple probability questions. Since a lower-tail probability of a negative z value is required in the first case, recall the use of Φ (–k) = 1 - Φ(k) for some constant k. In the second part an upper-tail probability is obtained by noting that Pr(Z > z) = 1 - Pr(Z ≤ z) = 1 - Φ(z). Duration: 14 minutes 56 seconds.
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Video tutorial 5.3 Sampling distribution of 
The standardisation technique is now applied to computing probabilities associated with the sample mean random variable, . It should be emphasised that during the standardisation we now divide by the standard error, σ/√n, and not the standard deviation, σ, as in tutorials 5.1 and 5.2. Duration: 13 minutes 36 seconds.
. It should be emphasised that during the standardisation we now divide by the standard error, σ/√n, and not the standard deviation, σ, as in tutorials 5.1 and 5.2. Duration: 13 minutes 36 seconds.Click HERE
Video tutorial 5.4 Central Limit Theorem review
Following your background reading, the Central Limit Theorem (CLT) may still seem a little confusing. This recording aims to consolidate your previous reading to clarify the importance of the CLT. Duration: 6 minutes 48 seconds.
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