If possible, construct an example linear recursive model that approaches a constant final value (steady state) that is not 0, +infinity or –infinity. If it is not possible, make the example model nonlinear with the same steady state restrictions.
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If possible, construct an example linear recursive model that approaches a constant final value (steady state)...
Computer output for fitting a simple linear model is given below. State the value of the sample slope for this model and give the null and alternative hypotheses for testing if the slope in the population is different from zero. Identify the p-value and use it (and a 5% significance level) to make a clear conclusion about the effectiveness of the model. The regression equation is Y=82.0-0.0116X. Predictor Coef SECoef T P. Constant 81.98 11.76 6.97 0.000 X -0.01161...
Computer output for fitting a simple linear model is given below. State the value of the sample slope for the given model. In testing if the slope in the population is different from zero, identify the p-value and use it (and a 5% significance level) to make a clear conclusion about the effectiveness of the model. Coefficients: Estimate Std.Error t value Pr(>|t|) (Intercept) 821.91 88.38 9.30 0.000 A -3.804 1.247 -3.05 0.006 Sample slope: Enter your answer; sample slope p-value:...
What is the time constant? What is the expected steady state value? 3(dω/dt)+2ω=0 ω(0)=4
Computer output for fitting a simple linear model is given below. State the value of the sample slope for this model and give the null and alternative hypotheses for testing if the slope in the population is different from zero. Identify the p-value and use it (and a 5% significance level) to make a clear conclusion about the effectiveness of the model.
Q.3(a) Transfer function model of a plant is, G(s) The controller is Ge(s)-K, where K is a constant. Find the value of K such that steady-state error for unit ramp input is 0.1. Find the gain margin and the phase mar gin (6 marks) (b) What are the effects on gain margin, phase margin and steady-state error, if the gain K is increased? (3 marks (c) Can the closed loop be unstable if the controller of Q.3(a) is implemented digi...
Consider the aircraft model shown in Figure 1. We will assume that the aircraft is in steady-cruise at constant altitude and velocity; thus, the thrust, drag, weight and lift forces balance each other in the x- and y directions. We will also assume that a change in pitch angle will not change the speed of the aircraft under any circumstance (unrealistic but simplifies the problem a bit). Under these assumptions the longitudinal equations of motion for the aircraft can be...
ld ts biovs Part II: Analysis of recursive algorithms is somewhat different from that of non-recursive algorithms. We are very much interested in how many times the method gets called. The text refers to this as the number of activations. In inefficient algorithms, the number of calls to a method grows rapidly, in fact much worse than algorithms such as bubble sort. Consider the following: public static void foo ( int n ) { if n <=1 ow ura wor...
Table 1: How to interpret logged models, table adapted from Bailey's textbook model equation Log-linear In Y; = Bo + BiX; + ei Linear-log Y; = Bo + B, In Xi + ei interpretation A one-unit increase in X is associated with a B1 percent change in Y (on a 0-1 scale). A one percent increase in X is associated with a B1/100 change in Y. A one-percent increase in X is associated with a B1 percent change in Y...
Question 2: Hypothesis testing (30 pts) Consider the following simple linear regression model with E[G-0 and var(G)-σ2. The output of linear where €1, €2, . . . ,en regression from R takes the form are i.i.d. errors Cal1: lm(formula y ~ x + 1) Residuals: Min 1Q Median 3Q Max 2.0606-0.3287-0.1148 0.5902 1.2809 Coefficients: Estimate Std. Error t value Prlt (Intercept) 0.507932 0.340896 1.49 0.147 0.049656 0.003455 14.37 1.89e-14 Signif. codes: 0.0010.010.05 .'0.1''1 Residual standard error: 0.7911 on 28 degrees...
Part A - SIR model for the spread of disease Overview. This part of the assignment uses a mix of theory and data to estimate the contact number c=b/k of an epidemic and hence to estimate the infection-spreading parameter b. The point is that once you know the value of b for a certain disease and population, you can use it in your model the next time there is an cpidemic, thus cnabling you to make predictions about the demand...