Let p P(Y = 1 |X). Consider the following generalized linear model for binary data: log(-log(p)) = 1-X What is the probability that Y=1 among individuals with X-0? Leave your answer in decimal form rounded to three decimals (e.g. if you find a probability of 0.12345, leave this as 0.123, not 12.3%)
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- Q.4. with A-50, a=10. What is the probability of the precipitation to remain in the range of 45 and Annual depth of precipitation X (cm) is assumed to be lognormally distributed 62 cm? StrQ2. You collected 500 weeks of data (2500 days total). Based on that you find Tuesday's mean return is 12 bps. Mean return of all days is 2 bps. Stdev across all days is 100 bps. There is no noticeable difference b/w Tuesday stdev vs other weekdays' stdev. Based on q1c find D Q1c. what is the mean log return and stdev of log return over one year period and four year period (assuming 252 trading days per year)? Q1d. based on Q1c what is the probably of losing money (negative log return) or doubling your money (total log return = ln(2)) over 1 year and 4 year period?Determine whether the statements are true or false. A. In logistic regression, the model is fitted by using The Least Square Regression Estimation method. B. Odds ratio is defined as the ratio of two log-odds. C. In logistic regression, as Bo + Bx gets huge, p approaches 1.
- Consider an economy where each worker is employed in either agriculture or services. Assume that log earnings of a worker are wo = 4o + €o if she is employed in agriculture and wi = µ1+€1 if she is employed in services, where µo and H1 are constants, ɛo ~ N(0, o3), and e1 - N(0, o?). Assume also that the correlation between earnings in the two sectors is p = g01 where oo1 = Cov(Eo, E1). Workers can be employed in agriculture at no cost, but they incur a cost C = T exp(wo) if they choose to work in services. Finally, assume that workers maximize their net earnings. Each worker knows all the parameters relevant to her decision. (a) Derive a linear approximation of the equation that determines whether an individual chooses services or agriculture. Assume that C is small compared to exp(wo), so you can ignore the error involved in this approximation. (b) Define v = €1 chosen worker is employed in services as a function of z, where z = (Ho – H1 +7)/0,. (c) How does this probability change…Part D. Data from the Statistical Abstract of the United States provides a panel data collected at the state level in 1987 and 1990. These data are used to estimate MODEL 1: The variables used in the analysis are: infmort is number of deaths within the year per 1,000 live births Ipcinc is natural log of per capita income Ipopul is natural log of the population (the population is in thousands) Iphysic is natural log of physicians per 100,000 inhabitants d90 is year dummy for 1990. For questions 1 to 4 you can assume that MLR 1-4 are satisfied. A 1. Use the Stata output below to interpret ß3. Test at a 5% significance level whether the number of physicians per capita has any effect on infant mortality rate. reg infmort 1pcinc 1popul 1physic d90 Source Model Residual Total infmort infmort = Po + B₁lpcinc + B₂lpopul + ß3lphysic + 8₁ d90 + u SS 78.0499129 350.452136 428.502049 df . 19.5124782 4 97 3.61290862 Coef. Std. Err. MS 101 4.24259454 1pcinc -4.693354 1.638132 1popul - .0551426…A quality control team studies the relationshipbetween years of experience (x) for individualdesign employees and ability to complete acomplex project within a certain time frame (y),where Y = 1 if the project is successfullycompleted, and Y = 0 if not. We know: b0 = −1.68and b1 = 0.12.a) Write the estimated logit function.b) Estimate the odds that a design employeewith 10 years of experience completes thecomplex project.c) Find and interpret the estimated odds ratiofor this model.
- Consider the plot of the total number E of Ebola cases in West Africa reported to the Centers for Disease Control and Prevention from April 1, 2014, through December 1, 2015. D +|+|||||||||||||||| ► Ebola Cases in West Africa E = A logistic fit for these data is given by 27,841.42 1 + 134.65-0.646 where t is the time in months since April 1, 2014. Below, we have added the graph of this model. +-+|||||||||||||||||| ► Logistic Graph Added (a) Does the first plot, "Ebola Cases in West Africa," show a continuing epidemic or a health crisis that is under control by December 2015? Because the data points appear to be [leveling off ✔✔✔, they show a health crisis that is under control (b) According to the model, what was the total number of cases when the disease was spreading at the fastest rate? Round your answer to the nearest whole number. 27021.61 x cases (c) Use the crossing-graphs method to determine when the disease was growing at the fastest rate. Round your answer, in months after…Comment on the statistical significance of the estimated logit model.Would you include ros in a final model explaining CEO compensation in terms of firm performance?
- Aleem tossed a coin 50 times and recorded the results in a frequency distribution table. Outcome Frequency Heads 22 Tails 28 What is the experimental probability of tossing a head?You are an econometrician working in the Ministry of Finance in Trinidad and Tobago and using a database consisting of 108 monthly observations on automobile accidents for Trinidad and Tobago between January 2011 and December 2019, you estimate the following model: log( totacc,) = B, + Bit + B2feb, + ßzmar.. + B12dec, + µe where totacc is the total number of accidents, t is time (measured in months), and feb,, mar,, dec, are dummy variables indicating whether time period t corresponds to the appropriate month. You obtain the following OLS results: Number of obs F( 12, Prob > F df 108 31.06 0.0000 0.7969 Source I MS 95) Model | 1.00244071 Residual | .255496765 12 .083536726 95 .00268944 R-squared Adj R-squared = 0.7712 Root MSE Total | 1.25793748 107 .011756425 - .05186 ltotacc | Coef. Std. Err. t P>It| [958 Conf. Interval] .0024274 -.0912208 .031287 .0027471 .0001611 .0244475 .0244491 17.06 0.000 .0030669 .0058479 .1283621 feb | -.0426865 mar | apr | may | jun | jul | aug | sep I oct I…
