An article in Concrete Research ("Near Surface Characteristics of Concrete: Intrinsic Permeability," Vol. 41, 1989), presented data oncompressive strength x and intrinsic permeability y of various concrete mixes and cures. Summary quantities are n = 14,Σy = 572, Σ.y = 23,530, x = 43, Ex = 157.42, and xy = 1697.80. Assume that the two variables are relatedaccording to the simple linear regression model.Statistical Tables and ChartsPart 1Calculate the least squares estimate of the slope. (Round your answer to 3 decimal places.)

Answered: Image /qna-images/answer/f5c22883-20f0-4534-a84a-1fbc297b1972.jpg

Answered: "Near Surface Characteristics of…

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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Is the price elasticity of demand for cars statistically significant at 10% significant level? Explain.
An engineer at a company wants to model the relationship between the yield (y) and three variables: pressure (x1), volume (x2) and temperature (x3). Data is collected and the output of the statistical analysis is displayed table below. i. From appropriate statistical tests, determine the relationship between yield and the three variables, Use a 5% significance level where required.
A researcher records age in years (x) and systolic blood pressure (y) for volunteers. They perform a regression analysis was performed, and a portion of the computer output is as follows: ŷ = 4.5+ 14.4x Coefficients (Intercept) x Estimate 4.5 Ho: B₁ = 0 H₁: B₁ > 0 Ho: B₁ = 0 Ha: B₁ <0 14.4 Ho: B₁ = 0 Ha: B₁ #0 Std. Error Test statistic 2.9 4.7 1.55 3.06 P-value Specify the null and the alternative hypotheses that you would use in order to test whether a linear relationship exists between x and y. 0.07 0
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Canvas -→ X C0| ® E Exhibit 6 Part of an Excel regression output relating salary for programmer (dependent variable) with three independent variables: Test score, Graduate Degree, and Experience is shown below. Coeffic. Std. Err. t Stat P-value Intercept 7.9448 7.3808 1.0764 0.2977 Test Score 1.1476 0.2976 4.8561 0.0850 Grad. 0.1969 0.0899 2.1905 0.0011 Degree Experience -2.2804 1.9866 -1.0479 0.3679 Refer to Exhibit 6. Based on the Excel output, at 95% confidence (alpha = 0.05) which of the independent variables in the output contributes significantly to explain salary? O Experience Grad Degree Intercept Test score
The article "The Undrained Strength of Some Thawed Permafrost Soils"+ contained the accompanying data on the following. y = shear strength of sandy soil (kPa) x₂ = depth (m) x₂= water content (%) The predicted values and residuals were computed using the estimated regression equation ŷ-140.14 13.15x₁ + 12.22x₂ + 0.070x3 -0.227x4 + 0.413x5 where x3 = x₁²₁x4x₂², and x = x1x2. y X1 14.7 8.8 31.6 48.0 36.7 27.1 x2 25.6 36.7 25.8 10.0 6.0 39.0 16.0 6.8 39.1 16.8 7.0 38.4 20.7 7.2 33.8 38.8 8.5 33.7 16.9 6.6 27.0 7.9 33.0 7.3 27.8 16.0 4.6 26.2 24.9 10.0 37.7 2.8 34.5 12.8 2.1 36.5 Predicted y 23.92 46.76 26.79 11.42 14.23 16.77 23.03 25.48 16.21 24.09 15.00 29.13 14.88 7.79 Residual -9.22 1.24 -1.19 -1.42 1.77 0.03 -2.33 13.32 0.69 2.91 1.00 -4.23 -7.58 5.01 (a) Use the given information to calculate SSResid, SSTO, and SSRegr. (Round your answers to four decimal places.) SSTO= SSResid= SSRegr = (b) Calculate R² for this regression model. (Round your answer to three decimal places.) R² = How…
Please do not explain, just say the answer like A,B,C,D,
Develop a scatterplot and explore the correlation between customer age and net sales by each type of customer (regular/promotion). Use the horizontal axis for the customer age to graph. Find the linear regression line that models the data by each type of customer. Round the rate of changes (slopes) to two decimal places and interpret them in terms of the relation between the change in age and the change in net sales. What can you conclude? Hint: Rate of Change = Vertical Change / Horizontal Change = Change in y / Change in x
PLEASE DO THIS TYPEWRITTEN AND SKIP IT IF YOU HAVE ALREADY DONE THIS, OTHERWISE DOWNVOTE. I WILL UPVOTE IF YOU FOLLOW MY INSTRUCTIONS. THANK YOU
Let x be the size of a house (in square feet) and y be the amount of natural gas used (therms) during a specified period. Suppose that for a particular community, x and y are related according to the simple linear regression model with the following values. ? = slope of population regression line = 0.016 ? = y intercept of population regression line = −7 Question: Graph the population regression line by first finding the point on the line corresponding to x = 1,000 and then the point corresponding to x = 2,000, and drawing a line through these points.
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The ols() method in statsmodels module is used to fit a multiple regression model using “Quality” as the response variable and “Speed” and “Angle” as the predictor variables. The output is shown below. A text version is available. What is the correct regression equation based on this output? What is the coefficient of determination? Select one.

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