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)…

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)…

Four pairs of data yield r= 0.942 and regression equation y=3x.Also, y= 12.75. What is the best predicted value of y for x= 2.9?

four pairs of data yield r = 0.942 and the regression equation  y-cap = 3x. also, y-bar= 12.75. what is the best predicted value of y for x = 4.6?

An investigator wants to estimate the impact of smoking on the incidence of prostate cancer. The incidence of prostate cancer by the age of 70 is about 1 in 6 (17%) P2=0.17. U.S. study reporters that heavy smokers had twice the risk of developing prostate cancer as compared to nonsmokers (34%) P1=0.34. A repeat study in England, cohort enrolls men at age 50 and followed them for 30 years. Problem: Generste a 95% confidence level, assume a 20% attrition, and a margin of error of no more than 0.05 (5%) E=0.05

The data below is for a hypothetical study investigating the Systolic Blood Pressure (SBP) of construction workers. The table shows respective measurements of 20 workers along with hours of work per day and the area of the city the work took place. [You can use Excel-Data Analysis – Regression or http://vassarstats.net/multU.html] SBP Hrs/day Area 109.6 7 east 107.4 8 east 140.3 9 east 146.5 12 east 98.2 6 east 137.8 9 east 124.1 10 east 113.2 8 east 127.8 9 east 125.3 8 east 108.5 6 west 181.3 13 west 137.4 10 west 146.2 10 west 142.4 9 west 123.7 8 west 129.6 8 west 143.6 9 west 160.7 11 west 148.3 9 west   a) What is the regression equation?   b) Interpret the meaning of the slopes in this problem.   d)  At the 0.05 level of significance, determine whether each independent variable makes a contribution to the…

In general, what are some problems with using regression to measure causal effects?

To predict the market share of one of their products, a manufacturer of consumer electronics products hired a market research company to conduct a study that relates market share in a particular geographic region (in %) to the average annual household income and the number of retail outlets per 100,000 residents. The results of a multiple regression model to predict market share from Household income (in thousands of dollars) and number of outlets per 100,000 residents are given below.   a) How much of the variation in market share can this model predict? Is this statistically significant? b) Someone claims that each additional outlet will increase the market share by more than 1.5%, regardless of what the household income is. Perform the appropriate hypothesis test to check this claim with a 5% significance level. c) What increase in market share would your model predict for every two additional retail outlets in a region where the annual household income is $75,000? Find a 95%…

Define the concept of Assessing Studies Based on Multiple Regression in brief?