Use the age transition matrix L and age distribution vector x₁ to find the age distribution vectors x₂ and X3. 02 20 0 00 4 01 00 0 X2 11 L = 0000 - 0 0 → X₁ = 100 100 100 100
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- 9Use the age transition matrix L and age distribution vector x1 to find the age distribution vectors x2 and x3. O 2 0 1 L = 16 X1 =| 16 16 X2 =Consider a model with an interaction term between being female and being married. The dependent variable is the log of the hourly wage: log(wage) = 0.151 - 0.038 female + 0.1 married - 0.301 female* married + 0.079 educ + 0.027 exper+0.029 tenure (0.072) (0.056) (0.055) (0.007) (0.005) (0.007) n = 536, R2 = 0.461 Numbers in parantheses are standard errors of coefficients. Given the estimation result and the observation number fill in the blanks below which aim at discussing the statistical significance of variables. The test statistic of the interaction term is The critical value at 1% significance level is Then the interaction term statistically significant at 1% significance level. (Hint: to fill the blank make a choice between "is" and "is not".)
- Assume that a population of patients contains 30% of individuals who suffer from a certain fatal syndrome Z, which simultaneously makes it uncomfortable for them to take a life-prolonging drug X. Let Z = 1 and Z = 0 represent, respectively, the presence and absence of the syndrome, Y = 1 and Y = 0 represent death and survival, respectively, and X = 1 and X = 0 represent taking and not taking the drug. Assume that patients not carrying the syndrome, Z = 0, die with probability 0.5 if they take the drug and with probability 0.5 if they do not. Patients carrying the syndrome, Z = 1, on the other hand, die with probability 0.7 if they do not take the drug and with probability 0.3 if they do take the drug. Further, patients having the syndrome are more likely to avoid the drug, with probabilities p(X = 1|Z=0) = 0.9 and P(X = 1|Z = 1) = 0.6 . Based on this model, compute the joint distributions and for all values of x, y, and z. Present the following joint distributions in tables. [Hint:…If a random variable X has the moment generating function Mx (t)= 2 - ť Determine the variance of X.Suppose that we want to build a model that predicts the group membership of a hurricane, either tropical (0) or non-tropical (1) based on the latitude of formation of the hurricane. The response variable is the binary variable Type.new (type of hurricane) and the predictor variable is FirstLat (First Latitude). Using R, we build a model by applying the glm () function. For the logistic regression model, we specify family = "binomial". The data is available at https:/userpage.fu-berlin.de/soga/200/2010_data_sets hurricanes.xlsx. The R code is set up filename my.filename |z|) * (Intercept) -9.08263 *# FirstLat # --- * Signif. codes: -9.446 9.447 <2e-16 **. <2e-16 **. 0.96148 0.37283 0.03947 0 *** 0.001 ** 0.01 * 0.05 '. 0.1 '1 *# (Dispersion parameter for binomial family taken to be 1) on 336 degrees of freedom on 335 degrees of freedom Null deviance: 463.11 # Residual deviance: 232.03 # AIC: 236.03 ** Number of Fisher Scoring iterations: 6 Which of the following is the correct model? I.…
- Find the variance stabilizing transformation g(y) for a response variable y, if E(y) = µ_and_V(y) = √P.Suppose that {Yt} is (weakly) stationary with autocovariance function Yk. (a) Find an expression for yk in terms of yo = of correlation) Var(Yt). (Think about the definitionThe longitude, rt of an air plane is affected by a random component e, due to the wind effect and its speed t It = -1 + 20t-1 + 6t. The speed of the plane is affected by a constant global linear trend, ß = B₁-1 = 3, and varies randomly due to weather conditions, v₁ = V₁-1 + B₁-1+w₂. In the above equations, is assumed to be white Gaussian noise with zero mean and o = 3. Similarly w is assumed to be white Gaussian noise with zero mean and 2,=0.5. A GPS transmitter mounted on the plane sends a noisy measurement to a receiver about its position, X₁ = 0.5xt + nt, where n, is assumed to be white Gaussian noise with zero mean and o2 = 2. (a) Using the following definition for the state vector, 0t. 0₁ It + ve B₂ write the motion of the plane in state space form. Note: You need to provide the exact form of the h, G and W matrices. (b) Evaluate the initial estimate for the state vector 03-
- Suppose a logistic regression model is fitted for the probability of car ownership for residents of a certain city in Oman (Y=1 if a resident owns a car, Y=0 if a resident does not own a car). Suppose the explanatory variables used are x1=no. of years a resident spent in schooling and x2 is gender of the resident of the city (x2=1 for a male and x2=0 for a female resident) a) Interpret el and e82 b) if BO= -1.6, B1=0.4 and B2=3, estimate the probability of a resident in the city owning a car.Suppose observations yt follow a linear trend + white noise stochastic process: Yt = c+ Bt + €t Ez ~ WN(0, o²) (a) Derive it’s unconditional mean. (b) Derive it's unconditional variance function. (c) Derive it's autocorrelation function. (d) Is yt covariance stationary ? Explain.3) Fresh tomatoes were stored at 15°C. Lycopene (orange-red pigment) contents were determined for a period of 10 weeks at 15°C. The results are presented in Table 3. Lycopene degradation during storage of tomatoes is known to give the straight line in a semi-log graphic paper. Table 3- Lycopene contents in fresh tomatoes stored at 15°C Storage time (week) Lycopene content (mg/kg) 33 2 23 6 5.3 10 2.1 a) Find out the equation describing the degradation of lycopene in tomatoes at 15°C, using the "linear regression analysis. Note: Give units wherever it is applicable!!!! Σxy- (Σx Σy / n ) ΣyΣx%- Σx Σxy b = Ex² - [ (Ex)² / n) ] η (Σχ'- [ (Σx)>/ n) ] )