Find the least squares estimate of the Intercept = Find the least squares estimate of the slope =
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Find the least squares estimate of the Intercept =
Find the least squares estimate of the slope =
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- Use the table of values you made in part 4 of the example to find the limiting value of the average rate of change in velocity.The following fictitious table shows kryptonite price, in dollar per gram, t years after 2006. t= Years since 2006 0 1 2 3 4 5 6 7 8 9 10 K= Price 56 51 50 55 58 52 45 43 44 48 51 Make a quartic model of these data. Round the regression parameters to two decimal places.An article in the Journal of Applied Polymer Science (Vol. 56, pp. 471–476, 1995) studied the effect of the mole ratio of sebacic acid on the intrinsic viscosity of copolyesters.- The data follows: Viscosity 0.45 0.2 0.34 0.58 0.7 0.57 0.55 0.44 Mole ratio 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 (a) Construct a scatter diagram of the data.
- A number of studies have shown lichens (certain plants composed of an alga and a fungus) to be excellent bioindicators of air pollution. The article “The Epiphytic Lichen Hypogymnia physodes as a Biomonitor of Atmospheric Nitrogen and Sulphur Deposition in Norway” (Environ. Monitoring Assessment, 1993: 27–47) gives the following data (read from a graph) on x ¼ NO3 wet deposition (g N/m2 ) and y ¼ lichen N (% dry weight): (refer to chart) The author used simple linear regression to analyze the data. Use the accompanying MINITAB output to answer the following questions: a. What are the least squares estimates of b0 and b1? b. Predict lichen N for an NO3 deposition value of .5. c. What is the estimate of s? d. What is the value of total variation, and how much of it can be explained by the model relationship?The 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…Snowpacks contain a wide spectrum of pollutants thatmay represent environmental hazards. The article“Atmospheric PAH Deposition: Deposition Velocitiesand Washout Ratios” (J. of EnvironmentalEngineering, 2002: 186–195) focused on the depositionof polyaromatic hydrocarbons. The authors proposeda multiple regression model for relating depositionover a specified time period (y, in mg/m2) to tworather complicated predictors x1 (mg-sec/m3) and x2 (mg/m2), defined in terms of PAH air concentrations forvarious species, total time, and total amount of precipitation.Here is data on the species fluoranthene andcorresponding Minitab output:obs x1 x2 flth1 92017 .0026900 278.782 51830 .0030000 124.533 17236 .0000196 22.654 15776 .0000360 28.685 33462 .0004960 32.666 243500 .0038900 604.707 67793 .0011200 27.698 23471 .0006400 14.189 13948 .0004850 20.6410 8824 .0003660 20.6011 7699 .0002290 16.6112 15791 .0014100 15.0813 10239 .0004100 18.0514 43835 .0000960 99.7115 49793 .0000896 58.9716 40656…
- A researcher hypothesizes that in a certain country the net annual growth of private sector purchases of government bonds, B, is positively related to the nominal rate of interest on the bonds, NI, and negatively related to the rate of inflation Π: Bt = a0 + a1NIt + a2Π t + ut Note that it may be hypothesized that B depends on the real rate of interest on bonds, R, where R = NI – Π. Using a sample of 56 annual observations, s/he estimates the following equations: (1) Bt = 0.43 + 0.90NIt - 0.97Πt R21 = 0.962, SSR1 = 2.20, QRESET(F1,52) = 16.6 (3.58) (8.80) (-1.05) (2) Bt = 0.44 + 0.94Rt R22 = 0.960, SSR2 = 2.22, QRESET(F1,53) = 0.9 (9.70) (16.7) (3) Bt = 0.44 + 1.14NIt SSR3 = 9.20, QRESET(F1,53) = 59.9 (8.84) (36.1) (4) NIt = 0.08 + 0.94Πt R24 = 0.997, SSR4 = 0.18, QRESET(F1,53) = 1.4…The table below shows (lifetime) peptic ulcer rates (per 100 population), UU, for various family incomes, xx, as reported by the 1989 National Health Interview Survey. Income 4000 6000 8000 12000 16000 20000 30000 45000 60000 Ulcer rate 14.1 13.1 13.8 12.6 11.8 11.7 11.5 9.3 7.6 (a) Find the equation of the regression line. Ulcer rate, U(x)= . (b) Estimate the peptic ulcer rate for an income level of x0= 25000 according to the linear model in part (a). Ulcer rate, U(x0)= .The accompanying dataset provides data on the monthly usage of natural gas (in millions of cubic feet) for a certain region over two years. Implement the Holt-Winters multiplicative seasonality model with no trend to find the forecast for periods 13-26, where alphaαequals=0.50.5 and gammaγequals=0.80.8. Then find the MAD for periods 13-24. Month Period Gas Usage Jan 1 242 Feb 2 227 Mar 3 153 Apr 4 144 May 5 55 Jun 6 34 Jul 7 29 Aug 8 27 Sep 9 28 Oct 10 40 Nov 11 88 Dec 12 203 Jan 13 231 Feb 14 248 Mar 15 251 Apr 16 139 May 17 35 Jun 18 32 Jul 19 28 Aug 20 26 Sep 21 27 Oct 22 38 Nov 23 86 Dec 24 182 Use the Holt-Winters multiplicative seasonality model with no trend to find the forecast for periods 13-18, periods 19-24, and then for periods 25 and 26. (Type integers or decimals rounded to two decimal places as needed.) Period Forecast 13 14 15 16 17 18
- The Lorenz curves for the income distribution in the United States for all races for 2015 and for 1980 are given below. 2015: y = x2.661 1980: y = x2.241 Find the Gini coefficient of income for both years. (Round your answers to three decimal places.) 2015: 1980:Below you are given a partial Excel output based on a sample of 16 observations. ANOVA df SS MS F Regression 4,853 2,426.5 Residual 485.3 Coefficients Standard Error Intercept 12.924 4.425 x1 -3.682 2.630 x2 45.216 12.560 The interpretation of the coefficient of x1 is that _____. a. a one-unit increase in x1 will lead to a 3.682-unit decrease in y when all other variables are held constant b. a on-unit increase in x1 will lead to a 3.682-unit decrease in x2 when all other variables are held constant c. It is impossible to interpret the coefficient. d. a one-unit change in x1 will lead to a 3.682-unit decrease in yConsider a linear regression model for the decrease in blood pressure (mmHg) over a four-week period with muy=2.8+0.8x and standard deviation chi=3.2. The explanatory variable x is the number of servings fruits and vegetables in a calorie-controlled diet. Using the 68-95-99.7 rule, between what two values would approximately 95% of the observed responses, y, fall when x = 7?
