2. The bias and variability of a point estimatorTwo sample statistics, T1 and T2, are used to estimate the population parameter 0. The statistics T1 and T2 have normal sampling distributions,which are shown on the following graph:Sampling Distribution 1Sampling Distribution 2The sampling distribution of T1 is labeled Sampling Distribution 1, and the sampling distribution of T2 is labeled Sampling Distribution 2. The dottedvertical line indicates the true value of the parameter 0. Use the information provided by the graph to answer the following questions.The statistic T1 isestimator of 0. The statistic T2 isestimator of 0. Which of the following best describes the variability of Tị and T2 ?T1 has a lower variability compared with T2 .Ti has a higher variability compared with T2 ·Tị has the same variability as T2 .Which of the following statements is true?T2 is relatively more efficient than T1 when estimating 0.T1 is relatively more efficient than T2 when estimating 0.You cannot compare the relative efficiency of T1 and T2 when estimating 0.

T1 has a lower variability compared with T2 as we can see that the graph of T2 is more flattened out…

Answered: Which of the following best describes…

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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You decide to add a second independent variable to your regression. Male is a dummy variable that equals 1 if the individual is male and 0 otherwise. Your regression results are now: wage = 3.071 + 0.289 × Years of Schooling + 1.28 × Male (0.143) (0.0168) (0.624) Interpret the coefficients from this regression. Make sure to clearly indicate what is changing and what is constant in each interpretation. Determine whether each coefficient is statistically significant at each of the conventional significance levels. The R2 for this regression is 0.316. Interpret the meaning of this value.
A regression was run to determine if there is a relationship between hours of study per week (xx) and the final exam scores (yy).The results of the regression were: y=ax+b a=5.614 b=32.79 r2=0.808201 r=0.899 Use this to predict the final exam score of a student who studies 1 hours per week, and please round your answer to a whole number.

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