Cadmium. The data below are influent and effluent cadmium at a wastewater treatment plant.Use graphical and smoothing methods to interpret the data. Time runs from left to right.Inf. Cd (µg/L) 2.5 2.3 2.5 2.8 2.8Eff. Cd (µg/L) 0.8 1.00.0 1.0 1.0Inf. Cd (µg/L) 2.0 2.0 2.0 2.5 4.5Eff. Cd (pg/L) 0.30.50.30.32.5 2.0 1.80.0 1.30.32.0 10.01.3 1.5 8.81.82.5 3.00.0 0.5 0.02.50.09.0 10.012.58.58.08.8 0.8 10.5 6.8 7.8

Given Information: Consider the given dataset: Influent effluent 2.5 0.8 2.3 1 2.5 0 2.8…

Answered: Cadmium. The data below are influent…

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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Formaldehyde is an organic compound commonly used in furniture paint. A paint company created a model to simulate the formaldehyde concentration in 50 houses as follows. |Coefficients Standard Error tStat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 0.362 4.076 0.000 0.184 0.541 0.184 0.541 X Variable 1 0.297 1.900 0.063 -0.017 0.612 -0.017 0.612 a) Complete the ANOVA table and find the R². ANOVA df SS MS F Regression Residual 0.091 Total b) Find the standard error for slope and intercept. c) Is the model significant at 1% significance? d) Is the intercept significant at 5% significance?
At the right is the computer output for a regression analysis involving starting salary (in $1,000) and college GPA. Variable Coef s.e. Constant -0.73391 5.744 -0.128 0.9015 GPA 11.8204 1.848 6.4 0.0002 S3.772 R-sq 83.6% R-sq(adj) 81.6% Source SS What is the equation for the least squares regression line? df MS Regression 581.798 581.798 40.9 Residual 8. 113.802 14.2252 (A) GPA - -0.734 + 11.82(Salary) (D) Salary 5.744 + 1.848(GPA) %3! %3D (B) Salary -0.734 + 11.82(GPA) (E) Salary 1.848 + 11.82(GPA) (C) GPA - 0.9015 + 0.0002(Salary)
I need to Comment on these residual plots. Select all that apply.A. The x 1 plot displays patterned residuals.B. The x 3 plot displays patterned residuals.C. All of the plots display random residuals.D. The x 2 plot displays patterned residuals.
Residual Length Length, y Length, y 1.84 Time, x 1. The following data represent the time between eruptions and the length of eruption for 8 randomly selected geyser eruptions. The coefficient of determination is 95.6%. Provide an interpretation of this value. Click here to view a scatter plot of the data. Click here to view a residual plot of the data. Time, x 12.14 11.74 1.73 11.56 1.68 12.25 1.86 12.01 1.84 11.61 1.72 12.12 1.88 11.67 1.71 2 11.28 1.62 The coefficient of determination is found to be 95.6%. Provide an interpretation of this value. % of the variation in (1) The least squares regression line explains (Type an integer or a decimal. Do not round.) 1: Scatter plot of eruption data. Time between Eruptions vs. Length of Eruption F00 F06 1.80- 1.70- 1.50- 11 11.8 11.4 12.2 12.6 Time 2: Residual plot of eruption data. Residuals vs. Time 0.10- 0.08- 0.02- 0.00- -0.02구 -0.04- -0.06- -0.08- -0.10+ 11 11.4 11.8 12.2 12.6 Time (1) O length of eruption. O time between eruptions.
determine the equation of the logarithmic regression function for the data. x, 1,2,3,4,5,6 y, 0.0, 3.2,4.5,5.0,5.4,5.6
Functions Supplement [Type here] Section 3.4 For each of the following data sets: plot the data, determine the regression equation, and add the graph of the regression to the graph. 1. Y3Db00kete0elo 2 3 4 Y 2.3 2 1.8 1.4 1.3 b. Pat the dato elong with the regresion e X. X 1.3 2.5 3.3 4.2 5.1 Y 2.6 2.6 2 1.8 1.5 X 5.2 Y -3.1 -4.8 -5.3 8.9 12.6 16.3 20 -7.1 -7.9 4. The table below shows the number C of Crayloa colors available t years after 1900. t, yr since 1900 3 49 58 72 90 98 103 C, number of colors 8 48 64 72 80 120 120 a. Find the equation of the regression line for C as a function of t. 5. How many Crayola colors does the regression line indicate for 1993? Round to the nearest whole number. 2. 3.
You estimated a regression with the following output. Source | SS df MS Number of obs = 383 -------------+---------------------------------- F(1, 381) = 1616.74 Model | 20894411.7 1 20894411.7 Prob > F = 0.0000 Residual | 4923967.53 381 12923.7993 R-squared = 0.8093 -------------+---------------------------------- Adj R-squared = 0.8088 Total | 25818379.2 382 67587.3802 Root MSE = 113.68 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 10.5851 .2632541 40.21 0.000 10.06749 11.10272 _cons | 71.97038 19.03739 3.78 0.000 34.53887 109.4019…
tobs= -2.5 T Reject HO terit=-2.064 Show Transcribed Text μ=475 OM=14 t0 o The effect size can be given as Cohen's d= -0.5 terit=2.064 X Reject HO
6.6 You estimated a regression with the following output. Source | SS df MS Number of obs = 209 -------------+---------------------------------- F(1, 207) = 92470.91 Model | 622579639 1 622579639 Prob > F = 0.0000 Residual | 1393670.56 207 6732.70804 R-squared = 0.9978 -------------+---------------------------------- Adj R-squared = 0.9978 Total | 623973310 208 2999871.68 Root MSE = 82.053 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 34.2537 .1126432 304.09 0.000 34.03163 34.47578 _cons | 8.806569 10.31802 0.85 0.394 -11.5353 29.14844…
Q9 Please answer fill-ins.
3.3 You estimated a regression with the following output. Source | SS df MS Number of obs = 115 -------------+---------------------------------- F(1, 113) = 5454.39 Model | 186947380 1 186947380 Prob > F = 0.0000 Residual | 3873036.62 113 34274.6603 R-squared = 0.9797 -------------+---------------------------------- Adj R-squared = 0.9795 Total | 190820417 114 1673863.3 Root MSE = 185.13 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 28.58986 .3871141 73.85 0.000 27.82292 29.3568 _cons | 10.54686 26.92706 0.39 0.696 -42.80051 63.89423…
Match the description with its symbol. Question content area bottom Part 1 y m x,y b yi yi Drag each symbol given above to its corresponding description below. 7. The y-value of a data point corresponding to xi 8. The y-value for a point on the regression line corresponding to xi 9. Slope 10. y-intercept 11. The mean of the y-values 12. The point a regression line always passes through

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