Suppose you estimate the equationYit = a +b Xit + eit -Which one of the following statements is true?Within group and logit estimates of b will be identicalOLS and random effect estimates of b will be identicalLSDV and random effect estimates of b will be identicalOLS and within group estimates of b will be the identicalNone of the above

Answered: Image /qna-images/answer/9187a568-4367-4fe4-8389-8c8c12814bcb.jpg

Answered: Suppose you estimate the equation Yit =…

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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A survey was conducted and asked: "Do you consider your health to be poor?" This was coded as 1 for "yes" and 0 for "no." The effect of age (in years) and sex (1 male, 0 = female) on the outcome was examined using logistic regression. The fitted logistic model is: = logit(ô) = −0.544 + ß * sex + 0.0331 * age (a) What are the odds of responding "yes" for a female subject who is 60 years old? (b) What is the estimated odds ratio corresponding to the age variable? Interpret this estimate. (c) Suppose that, based on the data from the survey, the odds of responding "yes" for males are 3.79% greater than the odds for females, adjusting for age. What is the value of Â? (d) If we only wanted to assess the association between survey response (yes/no) and sex, what other method(s) (that we have learned in this class) could we have used (name at least one)? In your own words, explain why we cannot use the same method(s) if we want to consider the effects of both sex and age on survey response.
A recent study showed that the hours a person exercised in a week affected the individual'sresting heart rate. It was computed that r = -.68 and the least squares regression line was?̂ = 83-1.4x, where x is the hours exercised and y is the resting heart rate. d. What percentage of variability in resting heart rate can be explained by variability inhours exercised?
Q2. 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?
Please solve it perfectly
ruan Use wage1.txt data set for this question. Consider the following regression: logtwage)-Bo-Beduc-3.exper+u. Suppose we want to test whether the return of an additional year in school will pay more than spending an additional year in the labor market Which of the following is the correct set of hypotheses for this test? Ho: 1-2 HA: 1-20 31 Ho: 1-2 HA: 1-8 270 Ho: 1-220HA: 1-³ 20 Ho: 1-2 HA: 1-820 0.91
11. In this same article on sleep duration and start time, researchers also considered whether school start time was related to obtaining an adequate amount of sleep. An adequate amountbof sleep was considered at least 8.5 hours of sleep, as recommended by the National Sleep Foundation. The authors used logistic regression models to associate the probability of adequate sleep to school start time. Here are some adapted logistic regression results from this study: In(odds of adequate sleep) = 6, + B,, where x1 = school start time, measured as the number of minutes after 7 AM that the school starts. For this model, B,-0.014, SE(B,)-0.005. - What Is the estimated odds ratlo of adequate sleep, and 95% CI, for students who start at 8:30 AM compared to those who start at 7:30 AM? a. 1.01 (1.00, 1.02) b. 1.52 (1.13, 2.05) C. 2.32 (1.27, 4.22) d. 4.05 (1.49, 11.02)
SHOW STEP BY STEP. pls
2. I surveyed 150 adults in the U.S. and asked them how many hours of TV the watched on average per week. I then ran a regression of # of hours of TV on whether or not they were a college graduate (=1 if yes, =0 if no), their age in years, the number of children in their household, and whether or not they live a cold climate (=1 if latitude is greater than 41.2, =0 otherwise). The results fro the regression are shown in the table below. Estimate p-value College Graduate -0.8 0.003 Age 0.1 0.150 Number of Children 0.10 0.521 Cold Climate 1.8 0.047 Intercept -1.23 0.041 (a) What is the dependent variable in this regression? (b) What are the independent variable(s) in this regression? (c) What is the unit of analysis? (d) What is the sample size? (e) What is one binary variable used in this analysis? (f) What is one ratio variable used in this analysis? (g) What is the predicted number of TV hours watched by a 50 year old, colleg graduate, with no children at home who lives in Arizona…
True, or false? One reason it is usually wise to treat an ordinal variable with methods that use the ordering is that in tests about effects, chi-squared statistics have smaller df values and tend to be more powerful. The cumulative logit model assumes that Y is ordinal; it should not be used with nominal Y. The baseline-category logit model treats Y as nominal; it can be used with ordinal Y, but it then ignores the ordering.
Please look at the following regression table. The year is 2010. The data originates from the Penn World Table 9.0, from Harris et.al. 2014 from the World Bank and from the International Energy Agency. The variables are defined as follows: Lntran_pc = log of transportation energy consumption per capita (ktoe) Lnypcpenn =log of GDP per capita (USD) Ln_gasprice = log of pump price for gasoline (USD/liter) Ln_temperature = log of the average annual temperature (in C) Ln_annualprecip= log of annual precipitation (mm) Ln_land = log of the land area of a country OECD = a dummy (indicator) that takes on the value of 1 if the country is OECD member, zero otherwise. Please define (Gauss Markov) MLR 4, state if it is likely to hold or not in this case.
Interpret the coefficient estimate on Married in column 2, commenting on both magnitude and statistical significance
Suppose Wesley is a marine biologist who is interested in the relationship between the age and the size of male Dungeness crabs. Wesley collects data on 1,000 crabs and uses the data to develop the following least-squares regression line where ?X is the age of the crab in months and ?ˆY^ is the predicted value of ?Y, the size of the male crab in cm. ?ˆ=9.5603+0.3976?Y^=9.5603+0.3976X What is the value of ?ˆY^ when a male crab is 24.9118 months old? Provide your answer with precision to two decimal places. Y=

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