Price of eggs and milk: The following table presents the average price in dollars for a dozen eggs and a gallon of milk for each month from January through October 2004. Use a TI-84 calculator to answer the following. Dozen Eggs Gallon of Milk 1.00 2.82 1.05 2.78 1.00 2.78 0.98 2.77 0.99 2.75 1.05 2.72 1.06 2.73 1.04 2.73 1.08 2.72 1.18 2.68

What does it mean for a regression line to be the "best-fit" line.

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.

Compute the least-squares regression line for predicting the price of milk from the price of eggs. Round the slope and -intercept to at least four decimal places.

The table below shows the amounts of crude oil (in thousands of barrels per day) produced by a country and the amounts of crude oil (in thousands of barrels per day) mported by a country, for the last seven years. Construct and interpret a 95% prediction interval for the amount of crude oil imported by the this country when the amount of crude oil produced by the country is 5,508 thousand barrels per day. The equation of the regression line is y = - 1.120x + 15,839.271. Oil produced, x 5,684 5,654 5,452 5,157 5,061 5,030 5,826 9,680 10,041 10,154 10,121 10,060 Oil imported, y 9,304 9,105 Construct and interpret a 95% prediction interval for the amount of crude oil imported when the amount of crude oil produced by the country is 5,508 thousand barrels per day. Select the correct choice below and fill in the answer boxes to complete your choice. (Round to the nearest cent as needed.) and O A. We can be 95% confident that when the amount of oil produced is 5,508 thousand barrels, the…

The scatter plot below shows the average cost of a designer jacket in a sample of years between 2000 and 2015. The least squares regression line modeling this data is given by yˆ=−4815+3.765x.     A scatterplot has a horizontal axis labeled Year from 2005 to 2015 in increments of 5 and a vertical axis labeled Price ($) from 2660 to 2780 in increments of 20. The following points are plotted: (2003, 2736); (2004, 2715); (2007, 2675); (2009, 2719); (2013, 270). All coordinates are approximate.    Interpret the slope of the least squares regression line. Select the correct answer below:   1.The average cost of a designer jacket decreased by $3.765 each year between 2000 and 2015.   2.The average cost of a designer jacket increased by $3.765 each year between 2000 and 2015.   3.The average cost of a designer jacket decreased by $4815 each year between 2000 and 2015.   4. The average cost of a designer jacket increased by $4815 each year between 2000 and…

Use the least squares regression line of this data set to predict a value. Meteorologists in a seaside town wanted to understand how their annual rainfall is affected by the temperature of coastal waters. For the past few years, they monitored the average temperature of coastal waters (in Celsius), x, as well as the annual rainfall (in millimetres), y. Rainfall statistics • The mean of the x-values is 11.503. • The mean of the y-values is 366.637. • The sample standard deviation of the x-values is 4.900. • The sample standard deviation of the y-values is 44.387. • The correlation coefficient of the data set is 0.896. The least squares regression line of this data set is: y = 8.116x + 273.273 How much rainfall does this line predict in a year if the average temperature of coastal waters is 15 degrees Celsius? Round your answer to the nearest integer. millimetres

A least squares regression line was calculated to relate the length​ (cm) of newborn boys to their weight in kg. The line is weight=−5.82+0.1601 length. A newborn was 48 cm long and weighed 3 kg. According to the regression​ model, what was his​ residual? What does that say about​ him?

The table below shows the amounts of crude oil (in thousands of barrels per day) produced by a country and the amounts of crude oil (in thousands of barrels per day) imported by a country, for the last 7 years. Construct and interpret a 98% prediction interval for the amount of oil imported by the country and the amount of oil produced by the country is 5,483 thousand barrels per day. The equation of the regression line is y = -1.194x + 16,234.906

What are the assumptions of multiple linear regressions only?