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Basic statistical concepts
Basic statistical concepts

... Investigation of a single population (one group): • H0 : The mean is equal to a specific number (e.g. mean FEV for boys is µ1 = 1.5) HA : The mean is not equal to a specific number. Comparison of two populations (two groups): • H0 : The means are equal (i.e. µ1 − µ2 = 0) HA : The means are not equal ...
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... (a) What proportion of burritos weigh at least 20 ounces? We have µ = 22.1, σ = 1.3, x = 20. Since the distribution is normal, we can use the Z score (Z = (20 − 22.1)/1.3 = −1.62) to find the probability from the normal probability table (0.0526). We want 1 minus this: 0.9474. (b) What is the chance ...
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Determine which of the four levels of measurement
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... Find the indicated probability. Round to three decimal places. 49) A machine has 11 identical components which function independently. The probability that a component will fail is 0.2. The machine will stop working if more than three components fail. Find the probability that the machine will be wo ...
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... 13) Consider a sample of body temperatures. Assume that the sample is a simple random sample of 100 people with a mean body temp of 98.2 degrees F and standard deviation .62. Find a 95% confidence interval (by hand, check with calculator). (.081, .118); margin of error is ~.0185. 14) There are appro ...
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... • Bound on error (aka Margin of error) - For a given confidence level (1-a), we can be this confident that the difference between the sample estimate and the population parameter is less than za/2 standard errors in absolute value • Researchers choose sample sizes such that the bound on error is sma ...
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ACTIVITY SET 1 Jan - Penn State Department of Statistics

... g. The U.S. Government reported that 23% of US adults age 18-24 smoked cigarettes. Based on your confidence interval do believe that this percentage is reasonable, too high, or too low for Penn State students and explain why. Since our interval does not contain 0.23 (i.e. 23%) and is less than this ...
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Misuse of statistics

Statistics are supposed to make something easier to understand but when used in a misleading fashion can trick the casual observer into believing something other than what the data shows. That is, a misuse of statistics occurs when a statistical argument asserts a falsehood. In some cases, the misuse may be accidental. In others, it is purposeful and for the gain of the perpetrator. When the statistical reason involved is false or misapplied, this constitutes a statistical fallacy.The false statistics trap can be quite damaging to the quest for knowledge. For example, in medical science, correcting a falsehood may take decades and cost lives.Misuses can be easy to fall into. Professional scientists, even mathematicians and professional statisticians, can be fooled by even some simple methods, even if they are careful to check everything. Scientists have been known to fool themselves with statistics due to lack of knowledge of probability theory and lack of standardization of their tests.
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