Reference no: EM13691736
1. Teacher Salaries and School Characteristics (Computer Exercise),
(i) Download the dataset benefits4.dta and report the sample mean, minimum and maximum values for each variable.
(ii) Estimate the equation lavgsal = β0+ β1bs + u (1)
by OLS and report the results in the usual form.
(iii) Test H0 : β1 = -1 against H1 : β1 > -1 using a 5% significance level.
(iv) Estimate the equation:
lavgsal = β0 + β1bs +β2lenroll + β3lstaff + β4poor + u
and report the results in the usual form. What happens to the coefficient on bs when the additional explanatory variables are added to the model? Is β1 statistically different from zero (against a two-sided alternative) at the 5% level of significance?
(v) What is the interpretation of the coefficient on lstaf f ? Why do you think it is negative?
(vi) From the model in (iv), obtain the predicted lavgsal when bs = 0.34, lenroll = 5.93, lstaf f = 4.32 and poor = 36. Run a regression which allows you to put a 90% confidence interval around the predicted value (this is a 'conditional' or 'within sample' prediction). Report the confidence interval.
(vii) Now consider a prediction for an individual. Construct the 90% confidence interval for the predicted lavgsal for an individual teacher when bs = 0.34, lenroll = 5.93, lstaf f = 4.32 and poor = 36 (this is an 'unconditional' prediction). Comment on the width of this confidence interval compared to that in (vi).
(viii) Add poor2 to the model in (iv). Compute the turning point in the quadratic, and show that this point lies in the observed data for poor. How many observations have poor higher than the calculated turning point?
(ix) Based on your findings from part (viii) describe how teacher salaries relate to school poverty rates. In terms of teacher salary, and holding other factors constant, is it better to teach at a school with poverty = 0, poverty = 50 or poverty = 100? Explain.
(x) Does the model in (viii) provide a good explanation of lavgsal? Does the model capture causal effects? Explain your reasoning.
Note: The benefit4.dta data set can be downloaded from the unit Blackboard website. This file has 901 observations and 5 variables. The variables correspond to:
1. lavgsal (log( average teacher salary)),
2. bs (average benefits / average salary for the school),
3. lenroll (log(total student enrollment)),
4. lstaf f (log(staff / 1000 students),
5. poor (percent of the student population from low socio-economic backgrounds).
|
Calculate the ratio of masses
: Two wires on a piano are the same length and are pulled by the same tension. wire 1 vibrates at a frequency 1.5 times the frequency of wire 2.
|
|
What is your expected starting salary
: You have chosen biology as your college major because you would like to be a medical doctor. However, you find that the probability of being accepted into medical school is about 10 percent.
|
|
Ending inventory and cost of goods sold under fifo method
: Calculate ending inventory and cost of good s sold under the weighted average method and calculate ending inventory and cost of goods sold under the FIFO method.
|
|
Define the terms pharmacokinetics and pharmacodynamics
: Explain why the mild respiratory infection would be considered a trigger for BB's asthma attack and what is the rationale for the use of inhaled corticosteroids in the long-term management of BB's chronic asthma?
|
|
What is the interpretation of the coefficient
: What is the interpretation of the coefficient on lstaf f? and does the model in (viii) provide a good explanation of lavgsal? Does the model capture causal effects? Explain your reasoning.
|
|
Write an expression using variables x and y
: Write an expression using variables x and y that evaluates to True if the dart hits (is within) the dartboard, and evaluate the expression for these dart coordinates:
|
|
Data mining functionalities
: Define each of the following data mining functionalities: characterization, discrimination, association and correlation analysis, classification, prediction and clustering. Give examples of each data mining functionality, using a real-life databas..
|
|
Computer crime events in history
: Research the Internet for two significant computer crime events in history. Gather details on the impacts of these crimes and identify the victims, identify details on how the researched crimes developed throughout time.
|
|
Program robots with varyingbehaviors
: Your task is to program robots with varyingbehaviors - The MemoryRobot remembersall positions that it has previously occupied and never goes back to a position that it knows to be adead end.
|