Now suppose that you instead observe the value of exports for all the different products (e.g., corn, soybeans, pork, dairy products, etc.) that Colombia purchases from the US from 2000 to 2019. What type of data is this? Group of answer choices Panel Random sample Time-series Cross-sectional
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- You are given the following dataset from a sample of the population. Superhero Number of Villains Captured Number of Losses Average Hours of Sleep Spiderham 30 9 12 Alligator Loki 50 1 23 Iron Man 20 8 6 Phil Coulson 18 2 8 Yelena Belova 25 5 2 Round all answers to 2 decimal places. IE: 3.567 = 3.57 A. What kind of dataset is this? Answer either Panel, Time series, or cross sectional B. Calculate the average number of villains captured in this sample. C. Calculate the variance of the number of villains captured in this sample. D. Calculate the correlation between the number of villains captured and average hours of sleep . If answer is negative, include negative sign. E. Calculate the sample covariance between the average number of losses and average hours of sleep . If answer is negative, include negative sign.U.S. Population The U.S. population can be modeledby the function y = 165.6x1.345, where y is in thousands and x is the number of years after 1800.a. What was the population in 1960, according tothis model?b. Is the graph of this function concave up or concave down? What does this mean?c. Use numerical or graphical methods to findwhen the model estimates the population to be93,330,000.Zeitler’s Department Stores sells its products online and through traditional brick-andmortarstores. The following parallel coordinates plot displays data from a sample of20 customers who purchased clothing from Zeitler’s either online or in-store. The datainclude variables for the customer’s age, annual income, and the distance from the customer’shome to the nearest Zeitler’s store. According to the parallel coordinates plot, howare online customers differentiated from in-store customers?
- We have a random sample of workers from a large firm. In 2017, the firm ran a training program. Some workers did the training program, others did not. The firm now wants to assess the effect of the training on earnings. We use the following model to estimate the effect of a training program on annual earnings in 2018: ln(earn2018)=β0+β1train+β2ln(earn2016)+β3educ+β4exper+u where earn2018 = individual total annual earnings in 2018 in dollars train = a dummy variable that takes the value 1 if the individual worker did the training in 2017 and 0 otherwise earn2016 = individual total annual earnings in 2016 in dollars educ = the individual's years of education exper = the individual's years of experience We find: ln(earn2018)^= 11.672 + 0.041train + 0.821ln(earn2016) + 0.037educ + 0.012exper (5.864) (0.019) (0.258) (0.013) (0.007) n=1278, R2= 0.048 Which of the following is the correct interpretation of the…We have a random sample of workers from a large firm. In 2017, the firm ran a training program. Some workers did the training program, others did not. The firm now wants to assess the effect of the training on earnings. We use the following model to estimate the effect of a training program on annual earnings in 2018: ln(earn2018)=β0+β1train+β2ln(earn2016)+β3educ+β4exper+u where earn2018 = individual total annual earnings in 2018 in dollars train = a dummy variable that takes the value 1 if the individual worker did the training in 2017 and 0 otherwise earn2016 = individual total annual earnings in 2016 in dollars educ = the individual's years of education exper = the individual's years of experience Now, I want to test whether the effect of an additional year of education increases earnings by twice as much as an additional year of experience. My null hypothesis is H0:β3=2β4. To get the standard error I need to conduct this hypothesis test, I rearrange or re-parameterise…if we got the the following regression result Yi= -2.5-10 (Xi) when the number of observation =100. then as Xi increase, Yi will and when Xi increases by 10, Yi will........ by.........*
- You have been presented with the following data and asked to fit statistical demand functions:PERIOD QUANTITY PRICE INCOME ADVErtising1 120 8 10 3 2 165 4 22 7 3 120 7 20 5 4 165 3 20 8 5 180 4 30 8 6 90 10 19 6 7 150 4 18 10.2 8 190 1.6 25 9.3 9 160 5 30 8 10 200 2 35 9.5Linear Relationshipa. Use any multiple regression packages (eg. SPSS package) to estimate a linear relationship between the dependent variable and the independent variables. b. Is the estimated…This is how I interpret the question units of slope: distance/timeDifferentiate and explain the following terms: i. Time Series Data and Cross Sectional Data
- A large policy debate revolves about the causes and consequences of child labour. Extensive child labour at early ages is often considered as harmful and there is substantial theoretical and empirical interest in determining the causes for child labour. Therefore, we would like to analyze the relationship between wealth (e.g. the number of cattle or other assets) and child labour, using household data collected in several villages in rural Africa. Suppose we have data on Y and X iv where Y, is the number of hours worked by child i in village v and X, is family wealth. a) Explain how this is related to the concept of Panel Data. b) Explain how you could estimate the relationship between wealth and child labour using pooled OLS, Random Effect (RE), Fixed Effect (FE) estimators. Write down the estimation equations and the conditions needed to guarantee consistency. Compare the FE and the OLS estimators: Is either of these two estimators based on weaker assumptions than the other? c)…U.S. Population The projected population of theUnited States for selected years from 2000 to 2060 isshown in the table below, with the population givenin millions.a. According to this table, what should the U.S. population have been in 2010?b. Create a new table with x representing the numberof years after 2000 and y representing the numberof millions.c. Use a graphing utility to graph the data from thenew table as a scatter plot.3Q2Q3: Both questions correlate with each other. Use the following to complete the graph.