Researcher A is interested in the wage differential between genders in the Brisbane area.She interviewed 4,520 full time employees in the Brisbane metropolitan area to collectdata on weekly income and gender from each interviewee, along with other individualcharacteristics that may determine labour market outcomes including, but not limited to,years of schooling, marital status, and number of kids. Every individual in the dataset iseither male or female.Researcher A then tried to regress log(income) on the dummy for male and the dummyfor female and other individual characteristics including a constant. But, she wasconfused because the econometrics package refused to compute the estimate for thedummy for male. In fact, the econometrics package omits the dummy for male in order toavoid the problem known as:a.Omitted Variable Bias.b.Perfect Multicollinearity.C.Homoskedastic errors.d.Control variables.e.Existence of Outliers.

Researcher aim : Wage differential between genders in Brisbane area. Sample : ( 4520 full time…

Answered: Researcher A is interested in the wage…

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 study was undertaken to determine whether there was a significant weight (in lb) loss after one year course of therapy for diabetes, and whether the amount of weight (in lb) loss was related to initial weight. The following table gives the initial weight (x) and weight after one year of therapy (y) for 16 newly diagnosed adult diabetic patients.
Major League Baseball now records information about every pitch thrown in every game of every season. Statistician Jim Albert compiled data about every pitch thrown by 20 starting pitchers during the 2009 MLB season. The data set included the type of pitch thrown (curveball, changeup, slider, etc.) as well as the speed of the ball as it left the pitcher's hand. A histogram of speeds for all 30,740 four-seam fastballs thrown by these pitchers during the 2009 season is shown below, from which we can see that the speeds of these fastballs follow a Normal model with meanu = 92.12 mph and a standard deviation of o = 2.43 mph. 85 90 100 speed (mph) Compute the z-score of pitch with speed 95.8 mph. (Round your answer to two decimal places.) 1.5 Approximately what fraction of these four-seam fastballs would you expect to have speeds between 90.2 mph and 90.8 mph? (Express your answer as a decimal, not a percent, and round to three decimal places.) Approximately what fraction of these four-seam…
(True/False). These data show that 80% of companies worth $75K or more are tracking the online and cellphone activity of 47% percent of US adults. (True/False). These data show that in comparison to companies, the government is less likely to track online activity. (True/False). These data show that companies run by individuals ages 18 to 29 years old are the most likely to track online activity. (True/False). These data show that the government is very likely to track Black adults (60%) and Hispanic adults (56%) and less likely to track White adults (43%).
A statistics professor would like to build a model relating student scores on the first test to the scores on the second test. The test scores from a random sample of 2121 students who have previously taken the course are given in the table. Test Scores Student First Test Grade Second Test Grade 1 8989 7777 2 6060 7373 3 5252 6767 4 4444 6262 5 7474 7373 6 6969 7171 7 4343 6363 8 8080 7878 9 4747 6161 10 7676 7878 11 6565 7575 12 8989 7878 13 7878 7777 14 9696 8181 15 5252 6161 16 9393 8585 17 9090 8080 18 6767 6969 19 4343 6060 20 4848 6161 21 4646 6161 Copy Data Step 1 of 2: Using statistical software, estimate the parameters of the model Second Test Grade=β0+β1(First Test Grade)+εiSecond Test Grade=β0+β1(First Test Grade)+εi. Enter a negative estimate as a negative number in the regression model. Round your answers to 4 decimal places, if necessary.
In 2015, over 30,00030,000 Americans died from opioid overdoses, and the number of inpatient stays and emergency visits to hospitals related to opiods increased dramatically compared to previous years. The National Institutes of Health (NIH) have partnered with the Healthcare Cost and Utilization Project (HCUP) to gather data on opioid-related deaths and hospitalizations. These data include information about inpatient stays, emergency department visits, age, sex, income, and opiod-related use. For a given patient admitted to a hopsital for an opioid-related event, identify which of these events are disjoint.
The authors of a paper compared two different methods for measuring body fat percentage. One method uses ultrasound, and the other method uses X-ray technology. Body fat percentages using each of these methods for 16 athletes (a subset of the data given in a graph that appeared in the paper) are given in the accompanying table. You can assume that the 16 athletes who participated in this study are representative of the population of athletes. Athlete X-ray Ultrasound 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 5.00 8.00 9.25 12.00 17.25 29.50 5.50 6.00 8.00 13.50 9.25 11.00 12.00 14.00 17.00 18.00 4.25 4.75 9.00 11.75 17.00 27.50 6.50 6.75 8.75 14.50 9.50 12.00 12.25 15.50 18.00 18.25 Use these data to estimate the difference in mean body fat percentage measurement for the two methods. Use a confidence level of 95%. (Use μ = MX-ray-Multrasound. Round your answers to three decimal places.) × % Interpret the interval in context. O There is a 95% chance that the true mean body fat percentage…
The questions below are based on a dataset containing the characteristics of 5,407households. In particular, we will use the following variables:- size: home size, measured in square feet.- hhinc: annual household net income, measured in euros.- owner: dummy variable equal to 1 if the living space is owned, and 0 if the livingspace is rented.- hhsize1: dummy variable equal to 1 if the number of household’s members is 1 or2, and 0 otherwise.- hhsize2: dummy variable equal to 1 if the number of household’s members is 3 or4, and 0 otherwise.- hhsize3: dummy variable equal to 1 if the number of household’s members is 5 orabove 5, and 0 otherwise.- edu: education level of household’s members. We conduct a simple regression of size on hhinc, now using robust standard errors.The regression output is reported in Table 2. Why are the estimated coefficients inTable 2 equal to the estimated coefficients in Table 1? Do the conclusions on thestatistical significance at 5% of the coefficient of…

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