L1_DataMeasurement(1)

# L1_DataMeasurement(1) - Created by Dr Friedman Updated by...

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Created by: Dr. Friedman Updated by: Dr. Fluture L1_DataMeasurement p. 1 Topic One: Introduction to Statistics Key ter m s u s ed in t h e field of s tatis t ics P o pulation: The entire category under consideration. If your company manufactures one million laptops, they might take a sample of, say 500, of them to test quality. The population, N = 1,000,000 and the sample n= 500. S a mple : is a portion of the population. A good sample is representative of the population. We will learn about probability samples and how they provide assurance that a sample is indeed representative. The sample size is shown as lower case n. P a ramet e r : A characteristic of a population. The population mean, µ and the population standard deviation, σ, are two examples of population parameters. If you want to determine the population parameters, you have to take a census of the entire population. Taking a census is very costly. The population size is usually indicated by a capital N. St a tisti c : A statistic is a measure that is derived from the sample data. For example, the sample mean, X ¯ , and the sample standard deviation, s, are statistics. They are used to estimate the population parameters. St a tistical Inference : The process of using sample statistics to estimate population parameters is known as statistical inference. For instance, using X ¯ (based on a sample of, say, n=1000) to draw conclusions about µ (population of, say, 300 million). Note that pollsters do not call every adult who can vote for president. This would be very expensive. What pollsters do is call a representative sample of about 2,000 people and use the sample statistics to estimate who is going to win the election. Another example: Nielsen television ratings The Nielsen ratings are based on a sample, not the population. The sample consists of about 5,000 TV households Population of more than 100,000,000 TV households For example, if a show has a 10.0 rating, this means that 10% of the entire sample were watching that show. Another example: market share of a product Sample of supermarkets throughout the US to determine what percentage of people who buy a type of product (e.g., detergent) buy a specific brand (e.g., Tide). The sample measurement is used to estimate the population parameter. Both of these examples are of statistics that are used to make inferences about the population.

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De s criptive St a tistic s : Are the statistics that summarize a sample of numerical data in terms of measures such as the mean and standard deviation. Descriptive statistics, as opposed to inferential statistics, are not concerned with the theory and methodology for drawing inferences from the sample to the entire population. All that we care about are the summary measurements such as the average (mean). Thus, a teacher who gives a class, of say, 35 students, an exam is interested in the descriptive statistics. What was the class average, the median grade, the standard deviation, etc? The teacher is not interested in making any inferences.
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• Spring '15
• Statistics, researcher, Simple random sample, representative

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