### Predicting Porosity and Calculating Residuals#### (c) Predict Porosity When Unit Weight is 135- **Predicted Value**: \(\hat{y} = -3.401\) ✔ **Explanation**: When attempting to predict porosity for a unit weight of 135, it's important to note that this value is outside the scope of available data, leading to a prediction of a negative porosity value, which logically cannot occur. **Rationale**: Predicting beyond the data range (135 in this case) can lead to results that do not make sense in real-world applications.#### (d) Calculate Residuals1. **Residual for the First Observation** - **Calculated Residual**: \(-0.50\) ✖2. **Residual for the Second Observation** - **Calculated Value**: [Blank]**Note**: Residuals should be computed by finding the difference between the observed and predicted values, rounded to two decimal places.#### (e) Calculate a Point Estimate of \(\sigma\)- **Calculated Value**: [Blank]**Instructions**: Provide your answer rounded to two decimal places. The calculation of \(\sigma\) involves statistical estimation methods based on residual calculations.This guide highlights the challenges of extrapolation in statistical predictions and emphasizes accuracy in calculation methods, particularly with residuals and standard deviations. **No-Fines Concrete and Its Properties**No-fines concrete, composed of uniformly graded coarse aggregate and cement-water paste, is particularly effective in areas prone to excessive rainfall due to its excellent drainage properties. An article titled "Pavement Thickness Design for No-Fines Concrete Parking Lots" discusses the advantages and applications of this type of concrete.A least squares analysis was conducted to understand the relationship between porosity (y, in percentage) and unit weight (x, in pounds per cubic foot, pcf) in concrete specimens. Below is the representative data collected from the study:- **Figure 1: Data Table** - **First Dataset:** - x (Unit Weight in pcf): 99.2, 101.0, 102.6, 102.9, 105.5, 107.1, 108.9, 110.7 - y (Porosity in %): 28.9, 27.7, 26.8, 25.3, 22.9, 21.4, 21.0, 19.5 - **Second Dataset:** - x (Unit Weight in pcf): 112.0, 112.2, 113.5, 113.7, 115.3, 115.3, 120.1 - y (Porosity in %): 17.2, 18.8, 15.8, 16.9, 12.9, 13.7, 10.6The data reveals a negative correlation between unit weight and porosity, implying that as the unit weight increases, the porosity tends to decrease. This relationship is crucial for understanding and optimizing the performance of no-fines concrete in construction applications.

Given: x y 99.2 28.9 101 27.7 102.6 26.8 102.9 25.3 105.5 22.9 107.1 21.4 108.9…

Answered: (c) Predict porosity when unit weight…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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As a researcher, you are interested if there is a significant difference in immunizations in those in low socioeconomic status and those in high socioeconomic status. To evaluate this you use the data below to calculate a Chi-Square (X2). Socioeconomic Status Socioeconomic Status Total High Low Immunized 1000 500 1500 Not Immunized 250 750 1000 Total 1250 1250 2500
A regression was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y).The results of the regression were :ŷ=a+bx a=20.874 b=-0.638 r2=0.894916 r=-0.946 Use this to predict the number of situps a person who watches 8 hours of TV can do (to one decimal place) _________
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A regression was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y).The results of the regression were: y=a+bx b=-0.997 a=20.107 r2=0.950625 r=-0.975 Use this to predict the number of situps a person who watches 0.5 hours of TV can do.Round to one decimal place.
Emissions of sulfur dioxide by industry set off chemical changes in the atmosphere that result in "acid rain". The acidity of liquids is measured by pH on a scale of 00 to 14.14. Distilled water has pH 7.0,7.0, and lower pH values indicate acidity. Normal rain is somewhat acidic, so acid rain is sometimes defined as rainfall with a pH below 5.0.5.0. The pH of rain at one location varies among rainy days according to a Normal distribution with mean 5.435.43 and standard deviation 0.54.deviation 0.54. What proportion of rainy days have rainfall with pH below 5.0?5.0?
Using your dataset, run a regression of Y=GPA and X=# Friends.(do not need your actual data, just the regression results)a) State what this regression is attempting to analyze. “By running this regression, we areattempting to show.....”b) Write out the regression equation and describe what it shows (if Friends increase by 1, then. . . ).c) Find your hypothesized GPA when the # friends equals 17.d) Is the slope of # of Friends significantly different from zero?Include Ho, Ha, decision rule, t statistic from table, tc, decision, and conclusion.e) Is the r-squared of # of Friends significantly different from zero?Include Ho, Ha, decision rule, F statistic from table, Fc, decision, and conclusion.
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Discuss the two types of variation=systematic variation and unsystematic variation. try to interpret an obtained t value (example t=3.15). lastly indicate the terms of what these two variations measure.
You estimated a regression with the following output. Source | SS df MS Number of obs = 423 -------------+---------------------------------- F(1, 421) = 267.80 Model | 8758968.84 1 8758968.84 Prob > F = 0.0000 Residual | 13769523.8 421 32706.7074 R-squared = 0.3888 -------------+---------------------------------- Adj R-squared = 0.3873 Total | 22528492.7 422 53385.0537 Root MSE = 180.85 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 6.150402 .3758334 16.36 0.000 5.411658 6.889145 _cons | -8.022201 24.02003 -0.33 0.739 -55.23632 39.19192…
Please answer both subparts. I will really upvote. Thanks
The slope, b represents * O predicted value of Y when X = 0. O the estimated average change in Y per unit change in X. O variation around the line of regression. O the predicted value of Y.
A regression analysis was performed to determine if there is a relationship between hours of TV watched per day (x) and number of sit ups a person can do (y ). The results of the regression were: y=ax+b a=-1.33 b=25.138 r2=0.712336 r=-0.844 Use this to predict the number of sit ups a person who watches 10 hours of TV can do, and please round your answer to a whole number.

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