The following results were obtained when each of a series of standard silver solutions wasanalyzed by flame atomic spectrometry.0 51520253010Concentration, ng ml10.003 0.127 0.251 0.390 0.498 0.625 0.763Absorbancee. Standard deviation of the regression linef. Standard deviation and confidence limits of slope and interceptg. Xo- and Sko- values for a sample giving an absorbance of 0.456h. Limit of detection

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MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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Following is a portion of the regression output for an application relating maintenance expense (dollars per month) to usage (hours per week) for a particular brand of computer terminal. Excel File: data14-41.xlsx Yes 5.29 ANOVA ▾to usage. Regression Residual Total Intercept Usage df 1 8 9 S.S 1575.76 349.14 1924.90 Coefficients Standard Error 0.9361 0.149 6.1092 0.8951 MS t Stat F If your answer is zero, enter "0". a. Write the estimated regression equation (to 4 decimals). ŷ = 6.1092 +0.8951 € b. Use a t test to determine whether monthly maintenance expense is related to usage at the .05 level of significance (to 2 decimals, if necessary). Use Table 1 of Appendix B. t= p-value = 0.01 Reject the null hypothesis Monthly maintenance expense is related c. Did the estimated regression equation provide a good fit? P-value Significance F
You obtained the following raw data when setting up a Biuret standard curve: BSA (mg/ml) Absorbancy 540nm 0 0.158 1 0.210 2 0.260 3 0.305 4 0.360 5 0.410 6 0.455 7 0.510 8 0.530 9 0.550 10 0.554 What would the quality of the line-fit (R2 value) be if you do not exclude experimental outliers? (Give you answer to 4 decimal places)
The price X (dollars per pound) and consumption y (in pounds per capita) of beef were samples for 10 randomly selected years. The following data should be used to answer the question that follows. n = 10 Ex = 36.19 Ex2 = 134.17 2.9 < x s 6.2 Ey = 774.7 Iy² = 60739.23 Exy = 2832.21 Before the slope of the line of best fit can be estimated, it is necessary to calculate SS Enter the value for SSx accurate to the nearest tenth.
Consider 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. Explain clearly what this slope says about the change in the mean of y for a change in x.
The table below is the output of the regression analysis of the 63 total payment for all the claims in thousands of Swedish Kronor. Where is the X = number of claims and Y = total payment for all the claims in thousands of Polokwane Kronor for geographical zones in Polokwane: Regression analysis Multiple R 0.912878 R Square Adjusted R Square 0.830615 Standard Error 35.94092 Observations 63 ANOVA df MS F P-value Regression 1 394021.5 394021.5 305.0293 2.04664E-25 Residual 61 78796.74 1291.75 Total 62 472818.3 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 19.99449 6.367757 0.002605 7.261369448 32.72760207 X 3.413824 0.195466 J 2.05E-25 N The values N and O of 95% confidence interval will be: OA. [3.023, 7.261] O B. [3.011, 3.805] O C. [3.805, 32.728] O D. 13.023, 3.805]
The following output comes from regression using the actual HW9 scores and the Final test scores from intro stats one semester. Sample size: 50R (correlation coefficient) = 0.3658R-sq = 0.1338Estimate of error standard deviation: 10.36256Parameter estimates: Parameter Estimate Std. Err. DF T-Stat P-Value Intercept 43.558118 11.1716 48 3.899 0.0003 Slope 0.360048 0.132225 48 2.723 0.009 Assume no assumptions are violated. Form a 87% Confidence interval for how much your final is supposed to increase for each problem done on homework 9.Use 5 decimal places
The following regression model was fitted to sample data with 12 observations: = 30 +4.50x. What is the residual for an observation (x = 2, y = 40)? 0.50 -1 1 -0.50
Listed below are systolic blood pressure measurements (in mm Hg) obtained from the same woman. Find the regression equation, letting the right arm blood pressure be the predictor (x) variable. Find the best predicted systolic blood pressure in the left arm given that the systolic blood pressure in the right arm is 90 mm Hg. Use a significance level of 0.05. Right Arm 101 100 92 75 75 O Left Arm 174 167 181 149 147 Click the icon to view the critical values of the Pearson correlation coefficient r The regression equation is y = + x. (Round to one decimal place as needed.) Given that the systolic blood pressure in the right arm is 90 mm Hg, the best predicted systolic blood pressure in the left arm is mm Hg. (Round to one decimal place as needed.)
Do the following plots show 1. Constant variability 2. Nearly normal residuals 3. Independent observations for SLR (conditions for linear regression)
An engineer measures the peak current (in microamps) when a solution containing an amount of nickel (in parts per 10°) is added to a buffer. The experiment was repeated for eleven different values of nickel solutions. A scatterplot showing the data is given below: Peak Current in Eleven Buffers with Added Nickel Solutio 60 120 140 100 Amount of nickel (pp million) Suppose a regression line was added to the plot above. If an additional measurement had been taken with the nickel of 62 parts per 10 and a peak current of 0.38 microamps, adding this observation would: O A. increase the intercept, decrease the slope. OB. increase the intercept, increase the slope. C. decrease the intercept, increase the slope. OD. decrease the intercept, decrease the slope. E. not affect the regression line.
Consider a certain data set on shoulder girth and height of a group of individuals. The mean shoulder girth is 107.20 cm with a standard deviation of 10.37 cm. The mean height is 171.14 cm with a standard deviation of 9.41 cm. The correlation between height and shoulder girth is 0.67. Calculate R2 of the regression line for predicting height from shoulder girth, and interpret it in the context of the application.
Consider the following information about variable X: Mean = 4, standard deviation = 2. Consider the following information about variable Y: Mean = 5, standard deviation = 3. If there is a correlation of r = 0.52 between variable X and variable Y, what is the intercept for a simple linear regression equation predicting Y on the basis of X? Please provide your answer as a raw score (not a z score) with a minimum of two decimal places.

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