c) Let Ô₁ = X₁+2X₂+2X3-X4 one of the two estimators is more efficient? and Ô₂ = (X₁ + X₂ + X3 + X4) be two unbiased estimators of 8. Which
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- the comic book store owner is curious in how well radio and newspaper advertisements work. A sample of 22 counties with almost equal populations was gathered during the month. For each of the counties, the owner has set aside a particular sum for radio and newspaper advertising. Additionally, the owner was able to access comic book sales information for the stores he controlled in each county and classified it as Low or High. COUNTY (X1) Comic book Sales (X2) (0 = Low, 1 = High) Radio Ads (X3) (in thousands) Newspaper Ads (X4) (in thousands) 1 1 0 40 2 0 0 40 3 0 25 25 4 0 25 25 5 0 30 30 6 0 30 30 7 0 35 35 8 0 35 35 9 0 40 25 10 0 40 25 11 1 45 45 12 1 45 45 13 1 50 0 14 1 50 0 15 1 55 25 16 1 55 25 17 1 60 30 18 1 60 30 19 1 65 35 20 1 65 35 21 1 70 40 22 1…The following table was generated from the sample data of 1010 college students regarding the number of parking tickets the student receives in a semester, the student's age, and the student's GPA. The dependent variable is the student's GPA, the first independent variable (x1�1) is the number of parking tickets, and the second independent variable (x2�2) is the student's age. Coefficients Standard Error t-Stat p-value Intercept −18.593656−18.593656 3.6727623.672762 −5.062581−5.062581 0.0038910.003891 Number of Parking Tickets 0.3232630.323263 0.0657790.065779 4.9144144.914414 0.0044190.004419 Student's Age 1.0181271.018127 0.1791910.179191 5.6817895.681789 0.0023530.002353 Copy Data Step 1 of 2 : Write the multiple regression equation for the computer output given. Round your answers to three decimal placesAssume that W (t) is a standard Brownian motion and that S(t) is a random variable defined as S(t) = 8 · 2W(t). Determine
- if you score on your next statics says is converted to a z-score, which of these z-scores would you prefer?F1.6 Question 18 please write on paperThe subsets of {1,2} are Φ, {1}, {2} and {1,2}. So there are four possible potential models when deciding on a regression with a choice of inputs from a dataset containing two potential input variables. Your friend tells you this is nonsense and there are three possible models because the empty set Φ is just an imaginary concept concocted by some mathematician. What should you say to your friend? You should explain that the empty set corresponds to the model y = β + ε, where β is some constant and ε is a residual term. In this model, the output predictions are always equal to the output's sample average. You should explain that the empty set corresponds to the model y = ε, where ε is a residual term. In this model, the output predictions are totally random. You should indeed agree. Your friend is spot on and one cannot have a regression with no input variables. You should partially agree. Your friend is spot on that there are three models and not…
- I got the right calculations for p(x=0) and p(x=1) but I can’t seem to get 2,3, and 4 even though I’m using the same steps. Can you please show me where I’m making an error. Thank you!Q10 needed to be solved correctly in 30 minutes and get the thumbs up please show neat and clean workGiven the data points (1, 13), (4, 235), (8, 2974), and (10, 6875.5), find the value of f(x) if x = 3.21 using quadratic interpolation. Round off your final answer to five decimal places but do not round off in preliminary calculations. Answer: 24.21023
- In a small area of a construction site there are seven construction workers. There is a 0.25 probability that each construction worker requires extra assistance from another construction worker from elsewhere. After these extra workers have moved into the small area, what is the variance of the number of construction workers in the small area. (You may assume that the probability of assistance required for each worker is independant of all the others) options:A researcher designed a study to test whether caffeine use enhanced performance time to run a 100 meter sprint. The participants (n=30) ran a 100 meter sprint before the consumption of caffeine on day 1 and then they ran another 100 meter sprint on the following day 2 after ingesting caffeine. The average time taken to run the 100 meter sprint on day 1 was (M1) and the average to run on day 2 after caffeine was (M2). The variance for this study was calculated to be (S2). Does caffeine use improve the running time in the 100 meter sprint (e.g., lower time)? Use all of the data provided to you below and test at an alpha of 0.05. M1 = 26.53M2 = 14.69S2 = 207.75n=30 a) state null and alternate hypothesis b) what is the critical value c) what is the obtained value d) conclusionDistinguished carefully between the members of each of the following pairs of properties:a. Unbiasedness and Consistencyb. Efficient estimator and Fully Efficient estimatorc. Asymptotic efficiency and Blue
