5.1-5.5 (3).pptx
TRANSCRIPT
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MFGE 341Quality Science Statistics
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What do we remember?
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Normal Distributions
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So What is a Normal Distribution
• A continuous probability distribution for a random variable ‘x’– Its’ graph is called the normal curve
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Normal Curves
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Continuous Probability Distributions
• Because a normal distribution is continuous, we cannot use a histogram to graph the data.
• We can use a different type of graph called a probability density function (or pdf for short)– The total area under a pdf curve is equal to 1– The pdf curve can never be negative
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The Normal Curve
• The normal curve is a specific pdf curve with the equation
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Properties of a Normal Distribution
• The mean, median, and mode are all equal• The normal curve is ‘bell-shaped’ and symmetric about
the mean• The total area under the normal curve equals 1• The normal curve approaches but never reaches the x-
axis• Within 1 standard deviation of the mean, the normal
curve curves downward, outside of 1 standard deviation it curves upward– This means there is an inflection point at 1 standard deviation
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Let’s Standardize
• If we standardize our data so the we have a mean of zero and a standard deviation of 1, we get the graph of a standard normal distribution.– We did this before using the z-score
z
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Properties of Standard Normal Distributions
• The cumulative area is close to 0 for z-scores close to z=-3.49
• The cumulative area is 0.5 for z=0• The cumulative area is close to 1 for z-scores close to z-3.49
– As the z-scores increase, the cumulative area increases
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What if we want to find the area somewhere in-between?
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Guidelines
• Draw the picture• Plot the z-score that you are interested in• Shade the area– If shaded left, use the value in the table– If shaded right, use 1-the value in the table– If shaded between two values, subtract the left
value from the right value
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What about Probability?
• The probability of a normally distributed event occurring is equal to the appropriate area under the normal curve.
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Let’s do the 2-step
• First convert the upper and/or lower bounds to z-scores
• Second, find the area under the normal distribution utilizing those z-scores
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Let’s do the 2-step
• You can also go through the process backwards.– If you are given an area or probability, you can find
the corresponding z-score from the table.– Once you have the z-score, you can determine the
desired value from the mean and standard deviation.
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Central Limit Theorem
• The central limit theorem describes the relationship between the sampling distribution of sample means and the population mean
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Sampling Distribution
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Central Limit Theorem
• If the population is normally distributed than the sampling distribution of sample means is normally distributed
• If we do not know how the population is distributed, but our sample size is at least 30, then the sampling distribution of sample means is approximately normal
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Central Limit Theorem
• The mean of the sample means is equal to the population mean
• The standard deviation of the sample means is equal to the standard deviation of the population divided by the square root of the sample size
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Mean Probability
• To find the probability that a sample mean will lie in a given interval of the sample distribution, convert it to the z-score
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Approximating a Binomial Distribution
• What happens when we need to calculate a binomial distribution on a large number of events?– Too much work to use the binomial distribution
formulas– Try to approximate the binomial distribution as a
normal distribution
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Can we do that?
• If and , and ‘x’ is a random variable, than we can do a normal approximation– Our mean would be – Our standard deviation would be
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Maybe not
• Because you are using a continuous distribution to approximate a discrete distribution, there is some error.
• We need a continuity correction factor– Move .5 units to the left and right• Which direction depends on the case
• Now we can find the probability
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5 Easy Steps
• After we recognize that we have a binomial distribution, to find the probability, we:– Determine if we can use a normal approximation– Find the mean and standard deviation– Apply the appropriate continuity correction– Translate to z-scores– Look up the probability in the table
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