Ultra high-performance concrete (UHPC) is a relatively new construction material that i characterized by strong adhesive properties with other materials. The following data are the adhesion measurements (in mJ/m²) for UHPC specimens adhered to steel: 107.1, 109.5 107.4, 106.8, 108.1. Assume that these sample observations were selected from a norma distribution. a) Calculate a two-sided 95% confidence interval for the true average work of adhesion for UHPC adhered to steel using R. b) Predict the resulting work of adhesion value resulting from a single future replication o the experiment by calculating a 95% prediction interval and compare the width of thi interval to the width of the CI from Part a) using R.

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1 Ultra high-performance concrete (UHPC) is a relatively new construction material that is
characterized by strong adhesive properties with other materials. The following data are the
adhesion measurements (in mJ/m²) for UHPC specimens adhered to steel: 107.1, 109.5,
107.4, 106.8, 108.1. Assume that these sample observations were selected from a normal
distribution.
a) Calculate a two-sided 95% confidence interval for the true average work of adhesion for
UHPC adhered to steel using R.
b) Predict the resulting work of adhesion value resulting from a single future replication of
the experiment by calculating a 95% prediction interval and compare the width of this
interval to the width of the CI from Part a) using R.
Transcribed Image Text:1 Ultra high-performance concrete (UHPC) is a relatively new construction material that is characterized by strong adhesive properties with other materials. The following data are the adhesion measurements (in mJ/m²) for UHPC specimens adhered to steel: 107.1, 109.5, 107.4, 106.8, 108.1. Assume that these sample observations were selected from a normal distribution. a) Calculate a two-sided 95% confidence interval for the true average work of adhesion for UHPC adhered to steel using R. b) Predict the resulting work of adhesion value resulting from a single future replication of the experiment by calculating a 95% prediction interval and compare the width of this interval to the width of the CI from Part a) using R.
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