For the data given below from a simple linear regression model, the computed value of the sum square error (SSE) is: Y 1 3 8 11 12 10 12 Y02468을
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- Compute the sum-of-squares error (SSE) by hand for the given set of data and linear model. (6, 6), (7, 7), (9, 10); y = x − 1Consider the following data: Fit a line, y = x1 + x2t, to the data using the least squares approach.The following information relates to BCD Co. Month Usage Cost Jan. 600 P750 Feb. 650 775 Mar. 420 550 Apr. 500 650 May 450 570Using the least squares regression, what is the fixed cost element (to the nearest whole peso)?
- In forestry, the diameter of a tree at breast height is used to model the height of the tree. Silviculturists working in British Columbia’s boreal forest conducted a series of spacing trials to predict the heights of several species of trees. The data are the breast height diameters (in centimeters) and heights (in meters) for a sample of 18 white spruce trees. B1 B2 18.9 20.0 15.5 16.8 19.4 20.2 20.0 20.0 29.8 20.2 19.8 18.0 20.3 17.8 20.0 19.2 22.0 22.3 16.6 18.8 15.5 16.9 13.7 16.3 27.5 21.4 20.3 19.2 22.9 19.8 14.1 18.5 10.1 12.1 5.8 8.0 B1: Breast Height Diameter of White spruce (cm) B2: Height (m) a) Plot the relationship using scatter diagram between the breast height diameters and the trees’ height. Are the breast height diameters and the trees’ height linearly related? What can you infer about the relationship between the two variables? Is a linear model appropriate? b) Compare the scatter plot in (a) with the correlation coefficient…An econometrician suspects that the residuals of her model might be autocorrelated. Explain the steps involved in testing this theory using the Durbin–Watson (DW) testThe following partial JMP regression output for the Fresh detergent data relates to predicting demand for future sales periods in which the price difference will be .10. SE Fit = .165360573, s = .628152. Predicted Demand Lower 95% MeanDemand Upper 95% MeanDemand 31 8.181072245 7.842346262 8.519798229 StdErr IndivDemand Lower 95% IndivDemand Upper 95% MeanDemand 0.649552965 6.850522511 9.511621980 Click here for the Excel Data File (a) Report a point estimate of and a 95 percent confidence interval for the mean demand for Fresh in all sales periods when the price difference is .10. (Round your CI answers to 3 decimal places and other answer to 4 decimal places.) (b) Report a point prediction of and a 95 percent prediction interval for the actual demand for Fresh in an individual sales period when the price difference is .10. (Round your PI answers to 3 decimal places and other answer to 4 decimal places.) (c) StdErr Indiv Demand on…
- The following sample contains the scores of 6 students selected at random in Mathematics and English. Use the scores in English as the dependent variable Y. Mathematics score (X) 70 92 80 74 65 83 English score (Y) 74 84 63 87 78 90 ∑x=464, ∑y=476,∑x^2=36354,∑y^2=38254, ∑xy=36926. Estimate the regression parameters and also write the prediction equation.Calculate the R2of the following multivariate sample regression functions and interpret theanswers.3.1 Investment-hat = β1-hat + β2-hat*Interest rate + β3-hat*Exchange rateESS = 900RSS = 1003.2 Investment-hat = β1 + β2-hat*Interest rate + β3-hat*number of 311 studentsESS = 400RSS = 6003.3 Salary-hat = β1 + β2-hat*Frequency of blinking eyes + β3-hat*Colour of hairRSS = 950TSS = 1000If the standard error of the estimate for a regression model fitted to a large number of paired observations is 1.75, approximately 95% of the residuals would lie within ______. −3.50 and +3.50 −1.75 and +1.75 −0.95 and +0.95 −0.68 and +0.68 −0.97 and +0.97
- The following is a partial computer output of a multiple regression analysis of a data set containing 20 sets of observations on the dependent variableThe regression equation isSALEPRIC = 1470 + 0.814 LANDVAL + 0.820 IMPROVAL + 13.5 AREA Predictor Coef SE Coef T P Constant 1470 5746 0.26 0.801 LANDVAL 0.8145 0.5122 1.59 0.131 IMPROVAL 0.8204 0.2112 3.88 0.0001 AREA 13.529 6.586 2.05 0.057 S = 79190.48 R-Sq = 89.7% R-Sq(adj) = 87.8% Analysis of Variance Source DF SS MS Regression 3 8779676741 2926558914 Residual Error 16 1003491259 62718204 Total 19 9783168000 For the problem above, we want to carry out the significance test about the coefficient of LANDVAL, what is the t-value for this test, and is it significant? 46.66, significant 2.05, significant 1.59, not significant 0.26, not significantThe marketing manager of a supermarket chain would like to determine the effect of shelf spaceon the sales of pet food. A random sample of 10 stores was selected, and the results are presentedbelow. Store shelf space in cm weekly sales in thousand pesos 1 45 18 2 45 21 3 75 15 4 80 18 5 95 23 6 100 26 7 135 22 8 140 27 9 185 25 10 190 28 d. Using the estimated simple linear regression equation Y=15.6414+0.0611X, estimate the weekly sales when theshelf space is 230cm? 250cm? e. Compute the coefficient of determination and interpret its value.Compute the least-squares regression line for predicting y from x given the following summary statistics. Round the slope and y -intercept to at least four decimal places. =x8.2 =sx3 =y1350 =sy13,000 =r0.40
