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 control for important omitted variable? Determine whether the regression will likely over or underestimate the effect of police on the crime rate?

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 control for important omitted variable? Determine whether the regression will likely over or underestimate the effect of police on the crime rate?

Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018

18th Edition

ISBN:9780079039897

Author:Carter

Publisher:Carter

Chapter4: Equations Of Linear Functions

Section: Chapter Questions

Problem 4SGR

See similar textbooks

Similar questions

If the regression line is not a "good fit" what would be better to use to approximate a response variable?
when a regression is used as a method of predicting dependent variables from one or more independent variables. How are the independent variables different from each other yet related to the dependent variable?
In general, what are some problems with using regression to measure causal effects?
Do you think the regression in Table 5 suffers from omitted variable bias? If yes,which additional variables would you include to control for omitted variable bias?
A study is conducted to determine if there is a relationship between the two variables, blood haemoglobin (Hb) levels and packed cell volumes (PCV) in the female population. A simple linear regression analysis was performed using SPSS. Based on the SPSS output of the ANOVA table, which of the following statements is the CORRECT interpretation? 1. The regression model statistically significantly predicts the blood haemoglobin level. 2. About 39.98 % of variance in Hb is explained by PCV. 3. The regression model does not fit the data. 4. There is significant contribution of Hb towards PCV.
Would someone familiar with SPSS be able to help me complete the table and the questions? (a) Explain which of the variables have statistically significant effects at the α = 0.05 level. (b) Are the conclusions different to the results obtained by univariate regression? Explain why and which approach is likely to be preferable?
When should a regression model not be used to make a prediction?
Explain why the coefficient for "wagegov" changed (compared to the previous regression) when the two controls were added.
La Quinta Inns has a competitive edge over its rivals because it: uses regression analysis to determine which variables most influence profitability. has better television advertisements. picks larger locations than its rivals. builds only along interstate highways. consistently receives four-star ratings for its inns.
Identify a characteristic of the data that is ignored by the regression line.
A business statistics professor would like to develop a regression model to predict the final exam scores for students based on their current GPAs, the number of hours they studied for the exam, the number of times they were absent during the semester, and their genders. The data for these variables are given in the accompanying table at the bottom of this page. a) Using Excel, construct a regression model using all of the independent variables. Create the dummy variable Gen, which equals 1 for a male and 0 for a female student ( this assignment is arbitrary) complete the regression equation for the model below, where y= Score, x1= GPA, x2= Hours, x3= Absenses, and x4= Gen. y= (__) + (__)x1 + (__)x2 + (__)x3 + (__)x4 b) Test the significance of the overall regression model using a= 0.10. c) interpret the meaning of the regression coefficient for the dummy variable. d) using the p-values, identify which independent variables are significant with a= 0.10. e) construct a regression…
A researcher was investigating variables that might be associated with the academic performance of high school students. The data included the average Math SAS score of all high school seniors in the city that took the exam (labeled as the variable SAT-M), the average number of dollars per pupil spent on education by the city (labeled as the variable $Per Pupil), and the percentage of high school seniors in the city that took the exam (labeled as the variable %Taking). The researcher ran the following multiple linear regression model as SAT-M=Beta0 + Beta1($Per Pupil) + Beta2(%Taking). This model was fit to the data using the method of least-squares, results shown inside of table within photo. If we want to test using ANOVA F-test with hypotheses Ho: Beta1=Beta2=0 versus H1: at least one of the Beta is not 0, what would the value of our F-statistic mean?