The following table shows the hot dogs bought from a street vendor over the course of eight days ("Demand"). Also shown is the temperature for each day in degrees Celsius. Complete parts a and b below.Temperature (°C) 20Demand4911 2332 3619405171021164420 D34a. Calculate the slope and y-intercept for the linear regression equation for these data.ŷ=+*(Round to two decimal places as needed.)

Answered: Image /qna-images/answer/dbcfb070-4ee2-4fbf-bd9e-aed88402a133.jpg

The following table shows the hot dogs bought from a street vendor over the course of eight days ("Demand"). Also shown is the temperature for each Temperature (°C) 20 11 23 19 Demand 49 32 36 40 5 17 10 21 16 44 20 34 a. Calculate the slope and y-intercept for the linear regression equation for these data. ŷ=+x (Round to two decimal places as needed.)

The following table shows the hot dogs bought from a street vendor over the course of eight days ("Demand"). Also shown is the temperature for each Temperature (°C) 20 11 23 19 Demand 49 32 36 40 5 17 10 21 16 44 20 34 a. Calculate the slope and y-intercept for the linear regression equation for these data. ŷ=+x (Round to two decimal places as needed.)

Functions and Change: A Modeling Approach to College Algebra (MindTap Course List)

6th Edition

ISBN:9781337111348

Author:Bruce Crauder, Benny Evans, Alan Noell

Publisher:Bruce Crauder, Benny Evans, Alan Noell

Chapter5: A Survey Of Other Common Functions

Section5.6: Higher-degree Polynomials And Rational Functions

Problem 1TU: The following fictitious table shows kryptonite price, in dollar per gram, t years after 2006. t=...

See similar textbooks

Similar questions

The following fictitious table shows kryptonite price, in dollar per gram, t years after 2006. t= Years since 2006 0 1 2 3 4 5 6 7 8 9 10 K= Price 56 51 50 55 58 52 45 43 44 48 51 Make a quartic model of these data. Round the regression parameters to two decimal places.
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?
For the following exercises, consider this scenario: The profit of a company decreased steadily overa ten-year spam.The following ordered pairs shows dollars and the number of units sold in hundreds and the profit in thousands ofover the ten-year span, (number of units sold, profit) for specific recorded years: (46,600),(48,550),(50,505),(52,540),(54,495). Use linear regression to determine a function Pwhere the profit in thousands of dollars depends onthe number of units sold in hundreds.
The table below contains the geographic latitudes, x, and average January temperatures, y, of 20 cities. Use Excel to find the best fit linear regression equation. Round the slope and intercept to two decimal places. x y46 2332 6039 4033 5938 5740 3342 3330 6434 5641 3936 4939 5447 2026 7645 2531 6239 4243 3137 5541 31 Answer: y=___x+___
The following table shows the annual number of PhD graduates in a country in various fields. NaturalSciences Engineering SocialSciences Education 1990 70 10 60 30 1995 130 40 100 50 2000 330 130 280 120 2005 490 370 460 210 2010 590 550 830 520 2012 690 590 1,000 900 (a) With x = the number of social science doctorates and y = the number of education doctorates, use technology to obtain the regression equation. (Round coefficients to three significant digits.) y(x) = What does the slope tell you about the relationship between the number of social science doctorates and the number of education doctorates? The slope tells us the increase in the number of education doctorates for each additional social science doctorate. The slope tells us the decrease in the number of social science doctorates for each additional education doctorate. The slope tells us the increase in the number of social science doctorates for each additional education doctorate.…
The following table gives the data for the grades on the midterm exam and the grades on the final exam. Determine the equation of the regression line, yˆ=b0+b1x�^=�0+�1�. Round the slope and y-intercept to the nearest thousandth. Grades on Midterm and Final Exams Grades on Midterm 7171 6262 7878 9494 8383 8181 8080 9494 8585 6262 Grades on Final 8888 7979 8888 9191 8080 7070 7171 9393 6565 7777
The following table gives the data for the average temperature and the snow accumulation in several small towns for a single month. Determine the equation of the regression line, yˆ=b0+b1xy^=b0+b1x. Round the slope and y-intercept to the nearest thousandth. Then determine if the regression equation is appropriate for making predictions at the 0.01 level of significance. Average Temperatures and Snow Accumulations Average Temperature (℉℉) 45 34 24 45 39 20 31 19 35 44 Snow Accumulation (in.in.) 9 16 24 9 15 28 25 18 16 5 1. Regression equation: y=__________ 2. Is the equation appropriate? yes or no
The following table represents the survey result from the seven online stores. Use linear regression to find the linear equation that is best fit for the data, width x equal to the online advertising. Round your coefficient to three decimal places. Online Store Online Advertising (in thousand dollars) Monthly e-commerce Sales (in thousand dollars) 1 1.5 368 2 1.7 340 3 2.8 665 4 5 954 5 1.3 331 6 2.2 556 7 1.3 376
The following table gives the data for the average temperature and the snow accumulation in several small towns for a single month. Determine the equation of the regression line, yˆ=b0+b1xy^=b0+b1x. Round the slope and y-intercept to the nearest thousandth. Then determine if the regression equation is appropriate for making predictions at the 0.010.01 level of significance. Average Temperatures and Snow Accumulations Average Temperature (℉℉) 38 30 17 39 45 22 34 24 29 38 Snow Accumulation (in.in.) 6 19 27 5 13 26 26 14 13 5 Copy Data
The managing director of a company wants to find whether there is a relationship between units of a product produced and the profits in a period of 12 months. Month Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec Units produced (X)‘000 2 3 5 8 11 10 12 15 17 18 20 24 Profits (Y) ‘000 3 5 8 11 13 14 16 20 22 24 26 30 i. Calculate the regression equation of the form Y = a + bX and hence estimate the value of the profits when 25,000 units are produced. ii. Calculate the regression equation of the form X = a + bY and hence estimate the value of the units produced when 27,000 in profits is made. iii. Calculate and both the Pearson correlation coefficient and coefficient of determination, interpret both and justify in this case why it would be prudent to rely on the coefficient of determination rather than Pearson in decision…
The following data set shows the entrance exam score (Verbal GMAT) for each of eight MBA students along with his or her grade point average (GPA) upon graduation. Calculate the slope and y-intercept for the linear regression equation for these data. GMAT GPA 310 3.7 290 2.9 270 3.1 280 3.2 340 3.8 270 2.9 290 3.7 300 3.1 Calculate the slope and y-intercept for the linear regression equation for these data. y= _____ + _______x
The following table gives the percentages p of adults living in households with only wireless telephone services, where t is the number of years after the year 2000. Find the equation of the regression line, and then estimate the percentage of adults living in households with only wireless telephone services in 2019. Number of years after 2000, t 6 8 10 12 14 Percentage with only wireless phones, p (%) 12 17 24 35 43 A. The equation of the regression line is p= ___t+ ____ . (Type integers or decimals rounded to two decimal places as needed.) B. The estimate of the percentage of adults living in households with only wireless telephone services in 2019 is ____ %. (Type an integer or decimal rounded to two decimal places as needed.)