one way anova
CE2.2a Use the numerical parameters in Table 2.4 for this and all ensu- ing CE2 assignments (see Figure 1.15). Simulate and plot the open-loop state variable responses for three cases (for this problem use the state-space realizations of CE1.2b); assume zero initial state for all cases [except Case i(b) below]: i. Single-input, single-output: input f () and output O(f). (a) unit impulse input f () and zero initial state. (b) zero input f () and an initial condition of e (0) = 0.1 rad (zero initial conditions on all other state variables). ii. Single-input, multiple-output: impulse input f() and two outputs w(t) and e(t). iii. Multiple-input, multiple-output: two unit step inputs f() and r() and two outputs w() and e(1). Simulate long enough to demonstrate the steady-state behavior. What are the system eigenvalues? Based on these eigenvalues and the physical system, explain the system responses. TABLE 2.4 Numerical Parameters for CE2 System Parameter Value Units Name kg cart mass - kg pendulum mass L 0.75 m pendulum length 9.81 m/s2 gravitational accelerationQuestion 11 (1 point) The RAND Health Insurance Experiment and the Oregon Medicaid Experiment are examples of discontinuity analyses non-random experiments O qualitative studies randomized experimentsWhile cause and effect can be concluded from randomized experiments, that is generally not the case with observational. studies. The reasons why researchers simply don't conduct all studies as randomized experiments rather than observational studies are: Q a. Observational studies are in general easier to conduct. @b. Randomized experiments are often not feasible because of ethical and/or logistical considerations, @c. The difference between randomized experiments and observational studies is superficial. Od. Both (a) and (b) above Oe. All of (a). (b). and (c) above.5. The residuals from the wage-productivity regression were regressed on lagged residuals going back six periods (i.e. AR (6)) thereby yielding the following results: Dependent Variable: RESI Method: Least Squares Sample (adjusted) : 1965-1998 Included Observations: 34 after adjusting endpoints Variable Coefficient Std. Error t-Statistic Prob. 5. 590462 1.963603 2.847043 0. 0085 -0. 066605 0. 023469 -2. 838058 0. 0087 RESI (-1) 0. 814971 0. 216231 3.768978 0. 0009 RES1 (-2) -0. 268651 0. 273887 -0.980882 0.3357 RES1 (-3) -0. 106017 0. 272780 -0. 388652 0.7007 RES1 (-4) 0. 305630 0. 273258 1. 118467 0.2736 RES1 ( -5) -0. 064375 0. 280577 -0. 229438 0. 8203 RES1 (-6) 0. 216156 0. 222160 0.972976 0.3395 R? = 0. 8920 Durbin-Watson d stat 1. 7589 R = 0. 8629 a. What can you say about the nature of autocorrelation in the above regression result? b. If you think that an AR (1) mechanism characterizes autocorrelation, would you use the first difference transformation to get rid of autocorrelation? Justify your answer.One-way ANOVA and two-way ANOVA are different because The single factor in one-way ANOVA has only one level Each factor in the two-way ANOVA has exactly two levels The one-way ANOVA concerns a single factor with multiple levels and the two ANOVA concerns two factors with also multiple levels The one-way ANOVA concerns a single factor with a single level and the two ANOVA concerns two factors with a single level each
