Lecture 14 - 1 2 3. The t-distribution. (1). Standard...

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Lecture 14 Material Covered in This Lecture: 1 Chapter 6, Section 6.2: Tests of Significance. 2 Chapter 6, Section 6.3: Use and Abuse of Tests. 3 Chapter 7, Section 7.1: Inference for the mean of a population. 1 1. Two-sided significance tests and confidence interval. Example(example 6.17) Talk about Example 6.16 1 2 3 4 5 6 Example(Example 6.19):
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7 8 9 10 11 Example(exercise 6.45): 12 2. When conducting a test, we must pay attention to the following issues. (1).Choosing a level of significance. There is no sharp border between "significant" and "not significant," only increasingly strong evidence as the P-value decreases. (2). Statistical inference is not valid for all sets of data. (3).Beware of searching for significance.
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Unformatted text preview: 1 2 3. The t-distribution. (1). Standard error: When the standard deviation of a statistic is estimated from the data, the result is called the standard error of the statistic. Suppose a population X has mean and standard deviation . It is well known that the sample mean, as a random variable, has mean and standard deviation n . When is unknown, we estimate it with the sample standard (2). The t-distribution Suppose that an SRS of size n is drawn from an N(, ). We know n x -~ N(0,1) Notation t ~ t n-1 , or t ~ t(n-1). Practice finding t* values from the table. Homework:6.44, 6.47, 6.54, 6.61....
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This note was uploaded on 07/25/2008 for the course STT 421 taught by Professor Nane during the Summer '08 term at Michigan State University.

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Lecture 14 - 1 2 3. The t-distribution. (1). Standard...

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