Introduction to Sampling Distribution

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Questions and Answers

What is the term used for the methods used to make decisions or draw conclusions about a population?

  • Sample Mean
  • Statistical Inference (correct)
  • Parameter Estimation
  • Hypothesis Testing

Which of the following is considered a random variable before the data is collected?

  • Sample Mean
  • Sample Variance
  • Population Parameter
  • Observations (correct)

What is the probability distribution of a statistic called?

  • Parameter Estimation
  • Sampling Distribution (correct)
  • Hypothesis Testing
  • Sample Mean

What is used to obtain a point estimate of a population parameter?

<p>Sample Mean (A)</p>
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What method utilizes the information contained in a sample from the population in drawing conclusions?

<p>Statistical Inference (D)</p>
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Which term refers to the process of making decisions or drawing conclusions about a population using sample data?

<p>Statistical Inference (B)</p>
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What is the estimate for the mean of a single population?

<p>(x̄) the sample mean (B)</p>
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What is the estimate for the difference in means of two populations?

<p>(x̅_1 - x̅_2) the difference between the sample means of two independent random samples (D)</p>
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What is meant by the sampling distribution of the mean?

<p>The probability distribution of (X̅) (D)</p>
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When will the normal approximation be satisfactory for the sampling distribution?

<p>When n ≥ 30 (A)</p>
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What does the central limit theorem state?

<p>The sampling distribution of the sample mean will still be approximately normal with mean µ and variance σ^2⁄n, if the sample size n is large (B)</p>
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What is the standard deviation of the sampling distribution of X̅, if σ = 10 and n = 25?

<p>2.5 (B)</p>
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What does Z=(X̅-μ)/(σ⁄√n) represent?

<p>Standardization formula for X̅ (B)</p>
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What does (s²) represent?

<p>Central Limit Theorem expression (A)</p>
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How does Linear functions of independent normally distributed random variables behave?

<p>Have a normal distribution with mean and variance (D)</p>
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