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.
1b
Determine which of the five types of regression is best for the given data in that exercise (the formula or expression for the best fit is not needed). 8. Sound Intensity The table lists intensities of sounds as multiples of a basic reference sound. A scale similar to the decibel scale is used to measure the sound intensity. Sound Intensity 316 500 750 2000 5000 Scale Value 25.0 27.0 28.75 33.0 37.0
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.
urgent in 2 hour otherwise dislike
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.
The linear regression model of two numerical variables X and Y is given by Y; = -5X; + 22 where 2 < X, < 56. %3D The slope -5 represents Select one: O a. the predicted increase of the value of Y, if X increases one unit O b. the predicted decrease of the value of X, if Y increases one unit O c. the predicted average value of Y when Xis equal to zero O d. the predicted average value of X when Y is equal to zero O e. the predicted decrease of the value of Y, if X increases one unit O f. the predicted increase of the value of X, if Y increases one unit
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

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