For the regression equation, Ŷ = –2X + 6, what can be determined about the correlation between X and Y?

A company has a set of data with employee age (X) and the corresponding number of annual on-the-job-accidents (Y).  Analysis on the set finds that the regression equation is Y=60-0.5*X.  What can be said of the correspondence (relation) between age and accidents? Are younger workers safer or more prone to accident? What is the likely number of accidents for someone aged 25?

The researchers decide to use simple linear regression to obtain a regression equation that would allow them to predict how many meals would be eaten according to a preference for exotic foods. The following regression equation is obtained: Y = 2.185 + 0.523X How many meals would someone be predicted to eat if they obtain a score of 3.8 on food preference ?

The​ least-squares regression equation is y=620.6x+16,624 where y is the median income and x is the percentage of 25 years and older with at least a​ bachelor's degree in the region. The scatter diagram indicates a linear relation between the two variables with a correlation coefficient of 0.7004. Interpret the slope.

A set of X and Y scores has SSx=10,Ssy=20,and SP =8,what is the b in the regression equation?

The police chief believes that maintenance costs on high-mileage police vehicles are much higher than those costs for low-mileage vehicles.  If high-mileage vehicles are costing too much, it may be more economical to purchase more vehicles.  An analyst in the department regresses yearly maintenance costs (Y) for a sample of 200 police vehicles on each vehicle’s total mileage for the year (X). The regression equation finds: Y = $50 + .030X with a r2 of .90 What is the IV? What is the DV? If the mileage increases by one mile, what is the predicted increase in maintenance costs?  If a vehicle’s mileage for the year is 50,000, what is its predicted maintenance costs? What does an r2 of .90 tell us? Is this a strong or weak correlation? How can you tell?

A regression analysis was performed to determine if there is a relationship between hours of TV watched per day (xx) and number of sit ups a person can do (yy). The results of the regression were: y=ax+b a=-1.223 b=22.907 r2=0.600625 r=-0.775 Use this to predict the number of sit ups a person who watches 5 hours of TV can do, and please round your answer to a whole number.

From a regression equation r2= 0.39 and the slope = -2.8 What is the linear correlation coefficient r?

Draw the regression line for the following equation: Y = -3 + (1) X Show your calculation for a low and high value on the predictor value.

1. Develop a simple linear regression equation for starting salaries using an independent variable that has the closest relationship with the salaries. Explain how you chose this variable.