Model SummaryAdjusted RSquareStd. Error ofModelRR Squarethe Estimate1.772.596.516215.1509a. Predictors: (Constant), PorosityANOVASum ofModelSquaresdfMean SquareSig.Regression341943.282.042341943.2827.387Residual231449.57546289.915Total573392.857a. Dependent Variable: Runoffb. Predictors: (Constant), PorosityCoefficientsStandardizedUnstandardized CoefficientsCoefficientsВSig.ModelStd. ErrorBetat1(Constant)1863.761237.5467.846.001Porosity-80.72929.703-.772-2.718.042a. Dependent Variable: RunoffA geomorphologist is building a model to understand variation in the amount ofrunoff observed in various streams within a large drainage basin. Linear regression isused to determine whether runoff is inversely related to the porosity of the soil. Thevariables here are thus runoff and porosity. The SPSS output from the analysis isshown above. Use the output to identify what percentage in of the variation inrunoff can be explained by variation in porosity?

Regression is a statistical process used in making predictions to estimate the association between…

Answered: A geomorphologist is building a model…

Algebra & Trigonometry with Analytic Geometry

13th Edition

ISBN:9781133382119

Author:Swokowski

Publisher:Swokowski

Chapter3: Functions And Graphs

Section3.3: Lines

Problem 76E

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In a study of housing demand, the county assessor is interested in developing a regression model to estimate the market value (i.e., selling price) of residential property within his jurisdiction. The assessor feels that the most important variable affecting selling price (measured in thousands of dollars) is the size of house (measured in hundreds of square feet). He randomly selected 15 houses and measured both the selling price and size, as shown in the following table. OBSERVATIONi SELLING PRICE (× $1,000)Y SIZE (× 100 ft2 )X 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 265.2 279.6 311.2 328.0 352.0 281.2 288.4 292.8 356.0 263.2 272.4 291.2 299.6 307.6 320.4 12.0 20.2 27.0 30.0 30.0 21.4 21.6 25.2 37.2 14.4 15.0 22.4 23.9 26.6 30.7 a. Plot the data.b. Determine the estimated regression line. Give an economic interpretation of the estimated slope (b) coefficient.c. Determine if size is a statistically significant variable in estimating selling price.d. Calculate the coefficient…
Cost accountants often estimate overhead based on the level of production. At the XYZ Company, they have collected information on overhead expenses and units produced at different plants and want to estimate a regression equation to predict future overhead. The data is available as given in the table below. Develop the regression equation for the cost accountants using Least Square Method. Predict overhead when 50 units are produced.
=Make the prediction equation and interpret the slope within the context of the problem =State the hypotheses for the hypothesis test for regression. =make ANOVA table
Draw a scatter diagram for the above data Obtain the regression equation by using least square method Predict the price of a house with a size of 28 square feet
Interpret the slope of the least square regression line in content
1. Use regression analysis to estimate the line of best fit -use the manual method with the formulas shown below.
In a study of housing demand, the county assessor develops the following regression model to estimate the market value (i.e., selling price) of residential property within his jurisdiction. The assessor suspects that important variables affecting selling price (YY, measured in thousands of dollars) are the size of a house (X1X1, measured in hundreds of square feet), the total number of rooms (X2X2), age (X3X3), and whether or not the house has an attached garage (X4X4, No=0, Yes=1No=0, Yes=1). Y=α+β1X1+β2X2+β3X3+β4X4+εY=α+β1X1+β2X2+β3X3+β4X4+ε Now suppose that the estimate of the model produces following results: a=166.048a=166.048, b1=3.459b1=3.459, b2=8.015b2=8.015, b3=−0.319b3=−0.319, b4=1.186b4=1.186, sb1=1.079sb1=1.079, sb2=5.288sb2=5.288, sb3=0.789sb3=0.789, sb4=12.252sb4=12.252, R2=0.838R2=0.838, F-statistic=12.919F-statistic=12.919, and se=13.702se=13.702. Note that the sample consists of 15 randomly selected observations. According to the estimated model, holding all…
Acrylamide is a chemical that is sometimes found in cooked starchy foods and which is thought to increase the risk of certain kinds of cancer. The paper "A Statistical Regression Model for the Estimation of Acrylamide Concentrations in French Fries for Excess Lifetime Cancer Risk Assessment"† describes a study to investigate the effect of x = frying time (in seconds) and y = acrylamide concentration (in micrograms per kg) in french fries. The data in the accompanying table are approximate values read from a graph that appeared in the paper. FryingTime AcrylamideConcentration 150 155 240 120 240 195 270 185 300 145 300 270 (a) Construct a scatterplot of these data. A scatterplot has 6 points. The horizontal axis is labeled "x" and ranges from 100 to 350. The vertical axis is labeled "y" and ranges from 50 to 350. The points are plotted from left to right in a downward, diagonal direction starting from the upper left of the diagram. Along the horizontal axis,…
Acrylamide is a chemical that is sometimes found in cooked starchy foods and which is thought to increase the risk of certain kinds of cancer. The paper "A Statistical Regression Model for the Estimation of Acrylamide Concentrations in French Fries for Excess Lifetime Cancer Risk Assessment"† describes a study to investigate the effect of x = frying time (in seconds) and y = acrylamide concentration (in micrograms per kg) in French fries. The data in the accompanying table are approximate values read from a graph that appeared in the paper. FryingTime AcrylamideConcentration 150 155 240 115 240 190 270 180 300 140 300 265 (a) Construct a scatterplot of these data. A scatterplot has 6 points.The horizontal axis is labeled "x" and ranges from 100 to 350.The vertical axis is labeled "y" and ranges from 50 to 350.The points are plotted from left to right in a horizontal direction starting from the lower left side of the diagram.Along the horizontal axis, there is 1 point at 150, 2 points…
Acrylamide is a chemical that is sometimes found in cooked starchy foods and which is thought to increase the risk of certain kinds of cancer. The paper "A Statistical Regression Model for the Estimation of Acrylamide Concentrations in French Fries for Excess Lifetime Cancer Risk Assessment"† describes a study to investigate the effect of x = frying time (in seconds) and y = acrylamide concentration (in micrograms per kg) in french fries. The data in the accompanying table are approximate values read from a graph that appeared in the paper. Frying Time Acrylamide Concentration 150 155 240 120 240 195 270 185 300 145 300 270 (a) Construct a scatterplot of these data. A scatterplot has 6 points. The horizontal axis is labeled "x" and ranges from 100 to 350. The vertical axis is labeled "y" and ranges from 50 to 350. The points are plotted from left to right in a downward, diagonal direction starting from the upper left of the diagram. Along the horizontal axis, there are 2 points at…
Disk drives last time Here is a scatterplot of the residu-als from the regression of the hard drive prices on their sizes from Exercise 18.a) Are any assumptions or conditions violated? If so,which ones?b) What would you recommend about this regression?
Issue of multicollinearity impacted the ‘validity and trustworthiness’ of a regression model. Demonstrate how this issue can be a problem by using appropriate hypothetical example.(INCLUDE TABLE AND FIGURES)

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