Bachelor’s Degree The president of Brown University wants to estimate the mean time (years) it takes students to earn a bachelor’s degree. How many students must be surveyed in order to be 95% confident that the estimate is within 0.2 year of the true population mean? Assume that the population standard deviation is sigma=1.3 years
Table of contents
- 1. Intro to Stats and Collecting Data1h 14m
- 2. Describing Data with Tables and Graphs1h 56m
- 3. Describing Data Numerically2h 5m
- 4. Probability2h 16m
- 5. Binomial Distribution & Discrete Random Variables3h 6m
- 6. Normal Distribution and Continuous Random Variables2h 11m
- 7. Sampling Distributions & Confidence Intervals: Mean3h 23m
- Sampling Distribution of the Sample Mean and Central Limit Theorem19m
- Distribution of Sample Mean - Excel23m
- Introduction to Confidence Intervals15m
- Confidence Intervals for Population Mean1h 18m
- Determining the Minimum Sample Size Required12m
- Finding Probabilities and T Critical Values - Excel28m
- Confidence Intervals for Population Means - Excel25m
- 8. Sampling Distributions & Confidence Intervals: Proportion2h 10m
- 9. Hypothesis Testing for One Sample5h 8m
- Steps in Hypothesis Testing1h 6m
- Performing Hypothesis Tests: Means1h 4m
- Hypothesis Testing: Means - Excel42m
- Performing Hypothesis Tests: Proportions37m
- Hypothesis Testing: Proportions - Excel27m
- Performing Hypothesis Tests: Variance12m
- Critical Values and Rejection Regions28m
- Link Between Confidence Intervals and Hypothesis Testing12m
- Type I & Type II Errors16m
- 10. Hypothesis Testing for Two Samples5h 37m
- Two Proportions1h 13m
- Two Proportions Hypothesis Test - Excel28m
- Two Means - Unknown, Unequal Variance1h 3m
- Two Means - Unknown Variances Hypothesis Test - Excel12m
- Two Means - Unknown, Equal Variance15m
- Two Means - Unknown, Equal Variances Hypothesis Test - Excel9m
- Two Means - Known Variance12m
- Two Means - Sigma Known Hypothesis Test - Excel21m
- Two Means - Matched Pairs (Dependent Samples)42m
- Matched Pairs Hypothesis Test - Excel12m
- Two Variances and F Distribution29m
- Two Variances - Graphing Calculator16m
- 11. Correlation1h 24m
- 12. Regression3h 33m
- Linear Regression & Least Squares Method26m
- Residuals12m
- Coefficient of Determination12m
- Regression Line Equation and Coefficient of Determination - Excel8m
- Finding Residuals and Creating Residual Plots - Excel11m
- Inferences for Slope31m
- Enabling Data Analysis Toolpak1m
- Regression Readout of the Data Analysis Toolpak - Excel21m
- Prediction Intervals13m
- Prediction Intervals - Excel19m
- Multiple Regression - Excel29m
- Quadratic Regression15m
- Quadratic Regression - Excel10m
- 13. Chi-Square Tests & Goodness of Fit2h 21m
- 14. ANOVA2h 28m
7. Sampling Distributions & Confidence Intervals: Mean
Introduction to Confidence Intervals
Problem 5.4.8
Textbook Question
True or False? In Exercises 5–8, determine whether the statement is true or false. If it is false, rewrite it as a true statement.
If the sample size is at least 30, then you can use z-scores to determine the probability that a sample mean falls in a given interval of the sampling distribution.
Verified step by step guidance1
Step 1: Understand the context of the problem. The statement is about using z-scores to determine probabilities for a sample mean when the sample size is at least 30. This relates to the Central Limit Theorem (CLT), which states that the sampling distribution of the sample mean approaches a normal distribution as the sample size increases, regardless of the population's original distribution.
Step 2: Recall the conditions for using z-scores. Z-scores are used when the population standard deviation (σ) is known, and the sampling distribution of the sample mean is approximately normal. For a sample size of at least 30, the CLT ensures that the sampling distribution is approximately normal, even if the population distribution is not normal.
Step 3: Evaluate the statement. The statement is true if the population standard deviation (σ) is known. If σ is unknown, you would typically use a t-distribution instead of z-scores, especially for smaller sample sizes. However, for large sample sizes (n ≥ 30), the t-distribution and z-distribution become very similar.
Step 4: If the statement is false, rewrite it as a true statement. A true version of the statement would be: 'If the sample size is at least 30 and the population standard deviation is known, then you can use z-scores to determine the probability that a sample mean falls in a given interval of the sampling distribution.'
Step 5: Conclude by emphasizing the importance of verifying whether the population standard deviation is known before deciding to use z-scores. If it is unknown, consider using the t-distribution instead.
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Key Concepts
Here are the essential concepts you must grasp in order to answer the question correctly.
Central Limit Theorem
The Central Limit Theorem states that, regardless of the population distribution, the sampling distribution of the sample mean will approach a normal distribution as the sample size increases, typically becoming approximately normal when the sample size is 30 or more. This theorem is fundamental in statistics as it allows for the use of normal probability methods for inference about population means.
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Calculating the Mean
Z-scores
A z-score is a statistical measurement that describes a value's relationship to the mean of a group of values. It is calculated by subtracting the mean from the value and then dividing by the standard deviation. Z-scores are used in hypothesis testing and confidence intervals to determine how many standard deviations an element is from the mean, which is particularly useful when the sample size is large.
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Z-Scores From Given Probability - TI-84 (CE) Calculator
Sampling Distribution
A sampling distribution is the probability distribution of a statistic (like the sample mean) obtained from a large number of samples drawn from a specific population. It provides a framework for understanding how sample statistics vary and is crucial for making inferences about the population based on sample data. The shape of the sampling distribution is influenced by the sample size and the population distribution.
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