=tion 3 of 15 > The National Institute of Standards and Technology (NIST) supplies "standard materials" whose physical properties are supposed to be known. For example, you can buy from NIST a copper sample whose melting point is certified to be 1084.80 °C. Of course, no measurement is exactly correct. NIST knows the variability of its measurements very well, so it is quite realistic to assume that the population of all measurements of the same sample has the Normal distribution with mean u equal to the true melting point and standard deviation o= 0.25 °C. Six measurements on the same copper sample, which is supposed to have melting point 1084.80 °C, are provided. Copper Melting Point (C) 1084.55 1084.89 1085.02 1084.79 1084.691084.86 To access the data, click the link for your preferred software format. CSV Excel (xls) Excel (xlsx) JMP Mac-Text Minitab14-18 Minitab18+ PC-Text R SPSS TI CrunchIt! Side-by-Side Macmillan Learning SOLVE: Calculate a 90% confidence interval. Use software or statistical tables for your calculations. Give your answers to four decimal places. (If you are using CrunchIt, change the default precision for confidence interval and mean to "8". This will give answers to four decimal places.) lower bound: Attempt 5 upper bound: 'C °C
=tion 3 of 15 > The National Institute of Standards and Technology (NIST) supplies "standard materials" whose physical properties are supposed to be known. For example, you can buy from NIST a copper sample whose melting point is certified to be 1084.80 °C. Of course, no measurement is exactly correct. NIST knows the variability of its measurements very well, so it is quite realistic to assume that the population of all measurements of the same sample has the Normal distribution with mean u equal to the true melting point and standard deviation o= 0.25 °C. Six measurements on the same copper sample, which is supposed to have melting point 1084.80 °C, are provided. Copper Melting Point (C) 1084.55 1084.89 1085.02 1084.79 1084.691084.86 To access the data, click the link for your preferred software format. CSV Excel (xls) Excel (xlsx) JMP Mac-Text Minitab14-18 Minitab18+ PC-Text R SPSS TI CrunchIt! Side-by-Side Macmillan Learning SOLVE: Calculate a 90% confidence interval. Use software or statistical tables for your calculations. Give your answers to four decimal places. (If you are using CrunchIt, change the default precision for confidence interval and mean to "8". This will give answers to four decimal places.) lower bound: Attempt 5 upper bound: 'C °C
MATLAB: An Introduction with Applications
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ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
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Transcribed Image Text:**Understanding the Copper Melting Point and Confidence Intervals**
The National Institute of Standards and Technology (NIST) provides "standard materials" with known physical properties. For instance, NIST offers a copper sample certified to have a melting point of 1084.80 °C. It's important to recognize that no measurement is exact. NIST is aware of the variability in its measurements, making it reasonable to assume that all measurements from the same sample follow a Normal distribution, with a mean (μ) equal to the true melting point and a standard deviation (σ) of 0.25 °C.
In this context, six measurements of the same copper sample, presumed to have a melting point of 1084.80 °C, are listed as follows:
**Copper Melting Point (°C):**
1084.55, 1084.89, 1085.02, 1084.79, 1084.69, 1084.86
To analyze this data:
1. You can access the dataset in various formats by clicking the relevant link:
- CSV
- Excel (xls)
- Excel (xlsx)
- JMP
- Mac-Text
- Minitab14-18
- Minitab18+
- PC-Text
- R
- SPSS
- TI
- CrunchIt!
**Task:**
Calculate a 90% confidence interval for the melting point. Use statistical software or tables, and provide your results to four decimal places. If using CrunchIt!, adjust the settings for confidence interval and mean both to "8" for precision.
Enter your results:
- **Lower bound:** _____ °C
- **Upper bound:** _____ °C
This exercise helps understand the variability in measurements and how confidence intervals provide a range where the true value likely lies.
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