Discuss the advantages of using data collected from a designed experiment in performing a regression analysis.

A researcher plans to study the causal effect of police on crime using data from a random sample of U.K. counties. He plans to regress the county’s crime rate on the (per capita) size of the county’s police force. Explain why this regression is likely to suffer from omitted variable bias. Which variable would you add to the regression to controlfor important omitted variable? Determine whether the regression will likely over or underestimate the effect of police on the crime rate?

What are the different ways to reduce the error in least square regression?

A rural state wants to encourage high school graduates to continue their education and attend college.  The state collected information on a random sample of high school seniors from across the state 7 years ago and is now observing how many years of education they completed.  They believe students decide to achieve more education when they are more capable, have easier access to college education, and the opportunity cost of attending are lower.  To explore the factors that affect the years of education completed they have used multiple regression to estimate the years of completed education as a function of: Unemployment rate - the unemployment rate in the county (3.9 – 16.8) County Hr Wage - average starting hourly manufacturing wage in the county Test - student score on college admission test (0 to 100 scale) Dist to college - Distance to near college (measured in 100’s of miles) Tuition - Tuition charged at nearest state university (measured in $1000s)…

Explain the Different threats to the Internal Validity of a Multiple Regression Study?

How could we provide evidence for the positive association between two variables through linear regression model and hypothesis testing? What are the steps?

You are analyzing a dataset containing 379 datapoints, and want to use 13 predictor variables to create a multiple variable linear regression model of the data. You conduct an ANOVA analysis, and yield a R² of 38%. Using this information, what would be the F statistic of your analysis?

Explain the advantages of all-subsets regression over stepwise regression.

Provide an example of a regression that arguably would have a high value of R² but would produce biased and inconsistent estimators of a causal effect. Explain why the R² is likely to be high. Explain why the OLS estimators would be biased and inconsistent.

When you are deciding which variables to include as predictors in a multiple regression equation, what are some conditions that you must consider first?