![6. Given the following data: 1,0.5, 1 .25,0.8, 0.4-0.3,-09-1 ,-1 .5,-0.9, 0.65 -0 (a) Estimate the mean, variance and the first two autocorrelation values for this data. (b) Determine the first and second order LMMSE predictors for this data. (First order: 치n] = a1x[n-l] and second order predictor: 치지 = bixin-11+ba(n-2) (e) Determine the minimum mean square error for these](http://img.homeworklib.com/questions/bdc6d670-7042-11ea-9414-51a2e13290dd.png?x-oss-process=image/resize,w_560)
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6. Given the following data: 1,0.5, 1 .25,0.8, 0.4-0.3,-09-1 ,-1 .5,-0.9, 0.65...
Question 2. Consider the following data: data 1 2.9 3.7 1.0 2.4 5.1 0.3 0.4 data...
Question 2. Consider the following data: data 1 2.9 3.7 1.0 2.4 5.1 0.3 0.4 data 2 9.0 1.8 4.5 4.8 7.9 9.9 6.8 2.2 3.5 (a). Neither of the two datasets are bell-shaped. Assume the two datasets are random, indepen dent samples. Which test should we use to compare the two datasets? Why? (b). Use the test from the previous part to determine if the first distribution is shifted to the left of the second distribution.
Given the following data, use exponential smoothing with = 0.3 and α =.5 to develop...
Given the following data, use exponential smoothing with = 0.3 and α =.5 to develop a demand forecasts for period 7. Assume that the forecast for week 1= 19. Use the Mean Absolute Percent Error to determine which forecasts are more accurate. Period 1 2 3 4 5 6 Demand 17 19 15 19 13 18
Consider the following data table: 0 2i = 0.2 0.4 f(xi) = 2 2.018 2.104 2.306...
Consider the following data table: 0 2i = 0.2 0.4 f(xi) = 2 2.018 2.104 2.306 0.6 0.2 and 23=0.4 is The linear Lagrange interpolator L1,1 (2) used to linearly interpolate between data points 12 (Chop after 2 decimal places) None of the above. -2.50x+0.20 -5.00x+2.00 -5.00x+2.00 5.00x-1.00 Consider the following data table: 2 Ti = 0 0.2 0.4 0.6 f(x) = 2.018 2.104 2.306 0.2 and 23 = 0.4, the value obtained at 2=0.3 is Using Lagrange linear interpolation...
PART I. Multiple Choice. Cirele the letter to the correct answer on the front page 1. Below is a ...
PART I. Multiple Choice. Cirele the letter to the correct answer on the front page 1. Below is a list of assumptions necessary for the regression analysis to be valid. With each assumption is a proposed procedure (on the right) for checking the validity of the assumption. Select the assumption validity which is the correct match. a. Normal errors b. Constant error variance C. Plot of residuals versus x Plot of residuals versus x Histogram of residuals Look for outliers...
PART I. Multiple Choice. Cirele the letter to the correct answer on the front page 1. Below is a ...
PART I. Multiple Choice. Cirele the letter to the correct answer on the front page 1. Below is a list of assumptions necessary for the regression analysis to be valid. With each assumption is a proposed procedure (on the right) for checking the validity of the assumption. Select the assumption validity which is the correct match. a. Normal errors b. Constant error variance C. Plot of residuals versus x Plot of residuals versus x Histogram of residuals Look for outliers...
Please answer asap, thanks! We collect the following data to study the operation of a plant for the oxidation of ammonia to nitric acid. In the regression models, x is air flow, x2 is cooling w...
Please answer asap, thanks!
We collect the following data to study the operation of a plant for the oxidation of ammonia to nitric acid. In the regression models, x is air flow, x2 is cooling water inlet temperature and y is stack loss. The standard errors of predicted are derived from the second model. Note that 1 57.765 and Σ(x1,-%)2-871.06. StdErr Pred y 27 0.9980645052 2 0.3599917876 23 0.4203293994 24 0.5286438199 4 0.5286438199 23 0.5199911743 8 0.5148291582 8 0.5148291582 7...
thanks for the help Each of the following two data sets represents a straight line. 1....
thanks for the help
Each of the following two data sets represents a straight line. 1. Draw a graph (plot the data points and draw the best-fit-straight-line) for each data set (first line vs. second line for each data set). 2. Find the slope of the line-of-best-fit in if plot is linear. 3. Show the calculation of the slope of graph for the corresponding case in the space below 4. Determine the equations of the curve of best fit for...
1. Researchers at National Institutes of Standards and Technology (NIST) collected pipeline data on ultrasonic measurements...
1. Researchers at National Institutes of Standards and Technology (NIST) collected pipeline data on ultrasonic measurements of the depth of defects in the Alaska pipeline in the field. The depth of the defects were then remeasured in the lab- oratory. These measurements were performed in six different batches. It turns out that this batch effect is not significant and so can be ignored in the analysis that follows. The laboratory measurements are more accurate than the in-field measurements, but more...
#5 & 6 Questions 3-6: lowing data were obtained for the reaction: 2NO g) + Odg)→...
#5 & 6
Questions 3-6: lowing data were obtained for the reaction: 2NO g) + Odg)→ 2NO2(g) Concentrations are "mol/L" and rates are in "(mol/L)Ys" INOlo 1.0 x 10-3 1.0 × 10-3 2.0 × 10-3 02]o 1.0 × 10-3 2.0 x 103 1.0 x 10-3 Initial Rate 2.0 × 10-5 4.0 × 10-5 8.0 x 10- 3. Consider the differential rate law, r kNoTO.], how do we determine the reaction o m? A. By educated guess B. By using the...
URGENT please show work and write clearly. Thank you. Use the data above to answer the...
URGENT please show work and write clearly. Thank you.
Use the data above to answer the questions.
The original Absorbance vs. Time graph
Absorbance vs. Time (min) 035 Ln(Absorbance) vs. Time (min) y = 1.174681-0.0733 R'9.990 Absoba Ice I absorbat) Time in Time (MI) 1/Absorbance vs. Time Y 0.62518x1.79428 R'-0.987 12Absotele ad Ln Time Absorbance 1/Absorbance Absorbance 0 0.3397 -1.07969 2.943774 1.5366 0.2806 -1.27083 3.563792 2.6937 0.255 -1.36649 3.921569 3.7872 0.2271 -1.48236 4.403347 5.0772 0.2105 -1.55827 4.750594 6.6 0.1809 -1.70981...
Question 2. Consider the following data: data 1 2.9 3.7 1.0 2.4 5.1 0.3 0.4 data 2 9.0 1.8 4.5 4.8 7.9 9.9 6.8 2.2 3.5 (a). Neither of the two datasets are bell-shaped. Assume the two datasets are random, indepen dent samples. Which test should we use to compare the two datasets? Why? (b). Use the test from the previous part to determine if the first distribution is shifted to the left of the second distribution.
Consider the following data table: 0 2i = 0.2 0.4 f(xi) = 2 2.018 2.104 2.306 0.6 0.2 and 23=0.4 is The linear Lagrange interpolator L1,1 (2) used to linearly interpolate between data points 12 (Chop after 2 decimal places) None of the above. -2.50x+0.20 -5.00x+2.00 -5.00x+2.00 5.00x-1.00 Consider the following data table: 2 Ti = 0 0.2 0.4 0.6 f(x) = 2.018 2.104 2.306 0.2 and 23 = 0.4, the value obtained at 2=0.3 is Using Lagrange linear interpolation...
PART I. Multiple Choice. Cirele the letter to the correct answer on the front page 1. Below is a list of assumptions necessary for the regression analysis to be valid. With each assumption is a proposed procedure (on the right) for checking the validity of the assumption. Select the assumption validity which is the correct match. a. Normal errors b. Constant error variance C. Plot of residuals versus x Plot of residuals versus x Histogram of residuals Look for outliers...
PART I. Multiple Choice. Cirele the letter to the correct answer on the front page 1. Below is a list of assumptions necessary for the regression analysis to be valid. With each assumption is a proposed procedure (on the right) for checking the validity of the assumption. Select the assumption validity which is the correct match. a. Normal errors b. Constant error variance C. Plot of residuals versus x Plot of residuals versus x Histogram of residuals Look for outliers...
Please answer asap, thanks!
We collect the following data to study the operation of a plant for the oxidation of ammonia to nitric acid. In the regression models, x is air flow, x2 is cooling water inlet temperature and y is stack loss. The standard errors of predicted are derived from the second model. Note that 1 57.765 and Σ(x1,-%)2-871.06. StdErr Pred y 27 0.9980645052 2 0.3599917876 23 0.4203293994 24 0.5286438199 4 0.5286438199 23 0.5199911743 8 0.5148291582 8 0.5148291582 7...
thanks for the help
Each of the following two data sets represents a straight line. 1. Draw a graph (plot the data points and draw the best-fit-straight-line) for each data set (first line vs. second line for each data set). 2. Find the slope of the line-of-best-fit in if plot is linear. 3. Show the calculation of the slope of graph for the corresponding case in the space below 4. Determine the equations of the curve of best fit for...
1. Researchers at National Institutes of Standards and Technology (NIST) collected pipeline data on ultrasonic measurements of the depth of defects in the Alaska pipeline in the field. The depth of the defects were then remeasured in the lab- oratory. These measurements were performed in six different batches. It turns out that this batch effect is not significant and so can be ignored in the analysis that follows. The laboratory measurements are more accurate than the in-field measurements, but more...
#5 & 6
Questions 3-6: lowing data were obtained for the reaction: 2NO g) + Odg)→ 2NO2(g) Concentrations are "mol/L" and rates are in "(mol/L)Ys" INOlo 1.0 x 10-3 1.0 × 10-3 2.0 × 10-3 02]o 1.0 × 10-3 2.0 x 103 1.0 x 10-3 Initial Rate 2.0 × 10-5 4.0 × 10-5 8.0 x 10- 3. Consider the differential rate law, r kNoTO.], how do we determine the reaction o m? A. By educated guess B. By using the...
URGENT please show work and write clearly. Thank you.
Use the data above to answer the questions.
The original Absorbance vs. Time graph
Absorbance vs. Time (min) 035 Ln(Absorbance) vs. Time (min) y = 1.174681-0.0733 R'9.990 Absoba Ice I absorbat) Time in Time (MI) 1/Absorbance vs. Time Y 0.62518x1.79428 R'-0.987 12Absotele ad Ln Time Absorbance 1/Absorbance Absorbance 0 0.3397 -1.07969 2.943774 1.5366 0.2806 -1.27083 3.563792 2.6937 0.255 -1.36649 3.921569 3.7872 0.2271 -1.48236 4.403347 5.0772 0.2105 -1.55827 4.750594 6.6 0.1809 -1.70981...
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