The U.S. national debt (in billions of dollars) for selected years from 1980 is shown in the following table. Year 1980 1985 1990 1995 2000 2004 Debt 930.2 1945.9 3233.3 4974.0 5674.2 7379.1 (a) Find the least squares regression linear function giving national debt as a function of years since 1980 (x 0 for 1980). (Round all numerical values to two decimal places.) f(x) = (b) Use the regression line to estimate the national debt in 2010. In 2014. (Round your answers to one decimal place.) 2010 24 billion 2014 24 billion (c) Assume 290 million (0.29 billion) people in the United States in 2014, What is the estimated per capita debt? (Round your answer to the nearest thousand.)

The U.S. national debt (in billions of dollars) for selected years from 1980 is shown in the following table. Year 1980 1985 1990 1995 2000 2004 Debt 930.2 1945.9 3233.3 4974.0 5674.2 7379.1 (a) Find the least squares regression linear function giving national debt as a function of years since 1980 (x 0 for 1980). (Round all numerical values to two decimal places.) f(x) = (b) Use the regression line to estimate the national debt in 2010. In 2014. (Round your answers to one decimal place.) 2010 24 billion 2014 24 billion (c) Assume 290 million (0.29 billion) people in the United States in 2014, What is the estimated per capita debt? (Round your answer to the nearest thousand.)

Name: Finite Mathematics The U.S. national debt (in billions of dollars) for
Uploaded: 2024-03-20T06:38:44.000Z
Channel: Bartleby
Description: Finite Mathematics The U.S. national debt (in billions of dollars) for selected years from 1980 is shown in the following table.Year 198019851990199520002004Debt 930.2 1945.9 3233.3 4974.0 5674.2 7379.1(a) Find the least squares regression linear fun

Elementary Linear Algebra (MindTap Course List)

8th Edition

ISBN:9781305658004

Author:Ron Larson

Publisher:Ron Larson

Chapter2: Matrices

Section2.CR: Review Exercises

Problem 90CR

See similar textbooks

Similar questions

Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s pole vault height has fallen below the value predicted by the regression line in Example 2. This might have occurred because when the pole vault was a new event there was much room for improvement in vaulters’ performances, whereas now even the best training can produce only incremental advances. Let’s see whether concentrating on more recent results gives a better predictor of future records. (a) Use the data in Table 2 (page 176) to complete the table of winning pole vault heights shown in the margin. (Note that we are using x=0 to correspond to the year 1972, where this restricted data set begins.) (b) Find the regression line for the data in part ‚(a). (c) Plot the data and the regression line on the same axes. Does the regression line seem to provide a good model for the data? (d) What does the regression line predict as the winning pole vault height for the 2012 Olympics? Compare this predicted value to the actual 2012 winning height of 5.97 m, as described on page 177. Has this new regression line provided a better prediction than the line in Example 2?
The U.S. national debt (in billions of dollars) for selected years from 1980 is shown in the following table. Year 1980 1985 1990 1995 2000 2004 Debt 930.2 1945.9 3233.3 4974.0 5674.2 7379.1 (a) Find the least squares regression linear function giving national debt as a function of years since 1980 (x = 0 for 1980). (Round all numerical values to two decimal places.) f(x) = (b) Use the regression line to estimate the national debt in 2005. In 2018. (Round your answers to one decimal place.) 2005 $ billion 2018 $ billion (c) Assume 310 million (0.31 billion) people in the United States in 2018. What is the estimated per capita debt? (Round your answer to the nearest thousand.) $
The amount (in millions of dollars) of used autos sold online in a certain country from 2011 to 2015 is given in the following table (x = 0 corresponds to 2011). Year, x 0 1 2 3 4 Sales, y 12.9 13.9 14.65 15.25 15.85 (a) Find an equation of the least-squares line for these data.y = (b) Use the result of part (a) to estimate the sales of used autos online in 2016, assuming that the predicted trend continued. (Round your answer to one decimal place.)$ million
(a) Find the equation of the least-squares line for the data. (Round all numerical values to two decimal places.) y= ?
generate a simple linear regression solution such that Price predicts Sales. What is the value of SSE (rounded to an integer)? PriceSales(1000s)2.00 4782.50 3733.00 3352.00 4732.50 3583.00 3292.00 4562.50 3603.00 3222.00 4372.50 3653.00 3422.00 8102.50 6533.00 3452.00 8322.50 6413.00 3722.00 8002.50 6203.00 3902.00 7902.50 6703.00 393
For a sample of 15 rural counties in one state, a researcher has collected information about the rate of deaths due to traffic accidents per 1,000,000 population and the percentage of the population which consists of males, age 16 to 25. For these variables, find the least squares regression line and compute r and r squared. Write a sentence or two summarizing and explaining your results. RATE OF % OF POPULATION County Traffic Deaths Young Males County Traffic Deaths Young Males A 15 4 B 16 5 C 22 10 D 23 9 E 20 8 F 25 11 G 18 2 H 15 6 I 19 12 J 10 3 K 9 2 L 18 15 M 15 9 N 24 16 O 9 3
Compute the least-squares regression equation for the given data set. Round the slope and y-intercept to at least four decimal places. 2 4 7 6 6 5 3 3 4 2