A regression analysis predicts the undrained shear strength (kPa) of a sandy soil based on depth (m) and watercontent (%). Assess the current model and propose improvements if necessary. (need to show TWO assessments:one for the coefficients and another for the goodness-of-fit) Use alpha = 5%ShearWaterStrength(Kra)14.70Content(%)31.50Depth(m)8.90SUMMARY OUTPUT48.0025.6027.0036.60Regression StatisticsMultiple RR SquareStandard Error25.9036.8010.0039.106.100.670.4516.0016.8039.2038.306.906.909.0214.0020.7033.907.30Observations38.8033.808.4016.9027.906.50StandardCoefficientst Stat0.621027.0033.10ErrorP-value8.004.500.54730.032714.2366InterceptDepth (m)Water Content (%)22.92520.272416.0026.300.6652-0.010224.9037.809.902.44150.6321-0.01610.98757.3012.8034.602.9036.402.00EditFormat Table

Variable: Depth Null Hypothesis: H0: The variable depth is not significant. Alternative Hypothesis:…

Answered: A regression analysis predicts the…

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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(2 Find the regression equation, letting the diameter be the predictor (x) variable. Find the best predicted circumference of a marble with a diameter of 1.2 cm. How does the result compare to the actual circumference of 3.8 cm? Use a significance level of 0.05. Baseball Basketball Golf Soccer Tennis Ping-Pong Volleyball Diameter 7.5 24.2 4.4 21.7 6.9 4.0 21.4 Circumference 23.6 76.0 13.8 68.2 21.7 12.6 67.2 Click the icon to view the critical values of the Pearson correlation coefficient r. The regression equation is y=+x. (Round to five decimal places as needed.) The best predicted circumference for a diameter of 1.2 cm is cm. (Round to one decimal place as needed.) How does the result compare to the actual circumference of 3.8 cm? O A. Since 1.2 cm is beyond the scope of the sample diameters, the predicted value yields a very different circumference. O B. Even though 1.2 cm is within the scope of the sample diameters, the predicted value yields a very different circumference. O C.…
Please do the first three parts
We give JMP output of regression analysis. Above output we give the regression model and the number of observations, n, used to perform the regression analysis under consideration. Using the model, sample size n, and output: Model: y = 6e + 61x1 + 62x2 + 63x3 + & Sample size: n = 30 %3D Summary of Fit RSquare RSquare Adj Root Mean Square Error Mean of Response Observations (or Sum Wgts) ped 0.981083 0.978900 0.240671 8.382667 nt 30 Analysis of Variance Sum of Мean Source df Squares Square F Ratio Model 3. 78.104400 26.03480 449.4761 Error 26 1.505986 0.05790 Prob > F C. Total 29 79.610387 <.0001*
Can brand, battery life, and camera resolution affect a smartphone's price? Use α = .05 to perform a regression analysis for the Smartphones01BC dataset and answer the following questions. Solve this problem using MegaStat, and write your complete answers in the Response Box so I know that you can interpret the output. Attach your MegaStat file so I can see that you used the correct analysis method to solve this problem. Download: Smartphones01BC a. Perform the regression and write the complete regression equation. b. Based on your regression equation, which brand increases the price of a smartphone the most? Use the statistics from your output to explain your answer. c. Which predictors are significant, and which are not? Use the statistics from your output to explain your answer. d. Use your regression equation to predict the price of an LG smartphone with a 40-hour battery and 12-megapixel camera.
I am doing linear modeling and I don't quite understand
Give an interpretation of the term “R-sq” and comment on its value.
894c3d916%#10001 MAT 213 OL1 Elementary Statistics Shayjunna Jones &| 0| 02/03/21 4:56 PM Homework: Homework 3 Save Score: 0 of 3 pts 17 of 20 (3 complete) ▼ HW Score: 10%, 6 of 60 pts 4.2.50 Question Help v Use the given data to do the following. a. Find the regression equation for the data points. b. Graph the regression equation and the data points. 4 3 y 4 -1 9 3 a. Find the regression equation for the data points. y = (Round to two decimal places as needed.) Enter your answer in the answer box and then click Check Answer. Check Answer 1 part remaining Clear Al 4:56 PM 2/3/2021 Cip
Measurements of the electrical resistivity of tungsten at specified temperatures are presented in the table below. The error in the determination of the temperature was negligible. The temperatures are stated as absolute temperatures in °K. The units of resistivity are u Ncm . Carry out a regression analysis for resistivity as a function of temperature. Temperature Resistivity 300 5.54 Include a scatterplot showing the residuals vs temperature. 400 7.95 Do the regression assumptions appear to be valid? 500 11.00 600 12.28 Include a scatterplot showing the raw data points as black circles as well as a continuous blue line representing the regression model. 700 15.95 800 18.46 Find the lower and upper limits for a 99% confidence interval for the slope of the regression model. Add a pair of red lines to the scatterplot: both passing through the centroid of the data, one having the slope corresponding to the lower bound of the confidence interval, and the other having the slope…
Q) Plot the relationship between total operating costs and machine hours. Draw the regression line.
A researcher's results are shown below using Femlab(labor force participation rate among females) to try to predict Cancer (death rate per 100,000 population due to cancer) in the 50 US states. What is the R2 for this regression? Source of variation df ss ms f Regression 1 5377.836 5377.836 5.228879 residual 48 49367.389 1028.487 total 49 54745.225
From the results shown above, write the regression equation.
We give JMP output of regression analysis. Above output we give the regression model and the number of observations, n, used to perform the regression analysis under consideration. Using the model, sample size n, and output: Model: y = 6e + 61x1 + 62x2 + 63x3 + & Sample size: n = 30 %3D Summary of Fit RSquare RSquare Adj Root Mean Square Error Mean of Response Observations (or Sum Wgts) ped 0.981083 0.978900 0.240671 8.382667 nt 30 Analysis of Variance Sum of Мean Source df Squares Square F Ratio Model 3. 78.104400 26.03480 449.4761 Error 26 1.505986 0.05790 Prob > F C. Total 29 79.610387 <.0001*

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