Analytical Chem istry - DePauw University

206 Analytical Chemistry 2.0The standard deviation in C A is1.997×10s CA=29.571 1 ( 0. 114 −0. 1183)+ += 4.778× 10−23 6 ( 29. 57) × ( 4.408×10 -5 )−3 2and the 95% confidence interval isµ CC tsA A C A−3 −5= ± = 380 . × 10 ± { 2. 78× ( 4. 778×10 )}−= 3. 80× 10 M± 013 . × 10Click here to return to the chapter.Practice Exercise 5.53 −3To create a residual plot, we need to calculate the residual error for eachstandard. The following table contains the relevant information.M5residual error0.0100.000-0.0100.000 0.002 0.004 0.006 0.008C AFigure 5.27 Plot of the residual errors forthe data in Practice Exercise 5.5.x i y iŷ iy − yˆ0.000 0.00 0.0015 -0.00151.55×10 –3 0.050 0.0473 0.00273.16×10 –3 0.093 0.0949 -0.00194.74×10 –3 0.143 0.1417 0.00136.34×10 –3 0.188 0.1890 -0.00107.92×10 –3 0.236 0.2357 0.0003Figure 5.27 shows a plot of the resulting residual errors is shown here. Theresidual errors appear random and do not show any significant dependenceon the analyte’s concentration. Taken together, these observationssuggest that our regression model is appropriate.iiClick here to return to the chapterPractice Exercise 5.6Begin by entering the data into an Excel spreadsheet, following the formatshown in Figure 5.15. Because Excel’s Data Analysis tools provide most ofthe information we need, we will use it here. The resulting output, whichis shown in Figure 5.28, contains the slope and the y-intercept, alongwith their respective 95% confidence intervals. Excel does not provide afunction for calculating the uncertainty in the analyte’s concentration, C A ,given the signal for a sample, S samp . You must complete these calculationsby hand. With an S samp . of 0.114, C ACAS=samp− b00. 114−0.0014= = 380 . × 10-1b 29.59 M1−3 M