Concept explainers
An investigator has reported the data tabulated below. It is known that such data can be modeled by the following equation
where a and b are parameters. Use a transformation to linearize this equation and then employ linear regression to determine a and b. Based on your analysis predict y at
x | 1 | 2 | 3 | 4 | 5 |
y | 0.5 | 2 | 2.9 | 3.5 | 4 |
Want to see the full answer?
Check out a sample textbook solutionChapter 17 Solutions
Numerical Methods for Engineers
Additional Math Textbook Solutions
Fundamentals of Differential Equations (9th Edition)
Basic Technical Mathematics
Advanced Engineering Mathematics
Algebra: Structure And Method, Book 1
- Find the equation of the regression line for the following data set. x 1 2 3 y 0 3 4arrow_forwardbThe average rate of change of the linear function f(x)=3x+5 between any two points is ________.arrow_forwardThe table below shows the number of state-registered automatic weapons and the murder rate for several Northwestern states. x 11.9 8.4 6.6 3.8 2.6 2.3 2.2 0.9 y 14.2 11.1 9.6 7 6.2 6.1 5.8 5 x = thousands of automatic weaponsy = murders per 100,000 residentsThis data can be modeled by the equation y=0.85x+4.03. Use this equation to answer the following; Special Note: I suggest you verify this equation by performing linear regression on your calculator.A) How many murders per 100,000 residents can be expected in a state with 7.7 thousand automatic weapons?arrow_forward
- create a line in DESMOS with the linear regression equation: y1 - mx1 + b 2) create a second line with quadratic regression: y1 - ax1^2 + bx + c After looking at the regression in DESMOS, is the data LINEAR or QUADRATIC?arrow_forwardBased on the data shown below, calculate the regression line.Regression Equation: Enter the equation in slope-intercept form (y=mx+b)(y=mx+b) with parameters accurate to three decimal places. x y 2 10.3 3 11.41 4 9.92 5 12.93 6 12.34 7 9.85 8 12.36 9 11.87 10 11.68 11 12.49 12 11.4arrow_forwardTo fit a simple linear regression model to the data and to provide its equation (d = a*t + b), along with R2 Day Date Weekday Daily Demand Weekend 1 4/25/2016 Mon 297 0 2 4/26/2016 Tue 293 0 3 4/27/2016 Wed 327 0 4 4/28/2016 Thu 315 0 5 4/29/2016 Fri 348 0 6 4/30/2016 Sat 447 1 7 5/1/2016 Sun 431 1 8 5/2/2016 Mon 283 0 9 5/3/2016 Tue 326 0 10 5/4/2016 Wed 317 0 11 5/5/2016 Thu 345 0 12 5/6/2016 Fri 355 0 13 5/7/2016 Sat 428 1 14 5/8/2016 Sun 454 1 15 5/9/2016 Mon 305 0 16 5/10/2016 Tue 310 0 17 5/11/2016 Wed 350 0 18 5/12/2016 Thu 308 0 19 5/13/2016 Fri 366 0 20 5/14/2016 Sat 460 1 21 5/15/2016 Sun 427 1 22 5/16/2016 Mon 291 0 23 5/17/2016 Tue 325 0 24 5/18/2016 Wed 354 0 25 5/19/2016 Thu 322 0 26 5/20/2016 Fri 405 0 27 5/21/2016 Sat 442 1 28 5/22/2016 Sun 454 1 29 5/23/2016 Mon 318 0 30 5/24/2016 Tue 298 0 31 5/25/2016 Wed 355 0 32 5/26/2016 Thu 355 0 33 5/27/2016 Fri 374 0 34 5/28/2016 Sat 447 1 35 5/29/2016…arrow_forward
- The table below shows the number of state-registered automatic weapons and the murder rate for several Northwestern states. x 11.8 8.2 6.9 3.9 2.3 2.4 2.6 0.6 y 14.2 11.2 10.2 7.5 5.9 6.4 6 4.3 x = thousands of automatic weaponsy = murders per 100,000 residents This data can be modeled by the equation y=0.88x+3.95. Use this equation to answer the following; Special Note: I suggest you verify this equation by performing linear regression on your calculator. A) How many murders per 100,000 residents can be expected in a state with 7.2 thousand automatic weapons? Answer = Round to 3 decimal places. B) How many murders per 100,000 residents can be expected in a state with 1.6 thousand automatic weapons? Answer = Round to 3 decimal places.arrow_forwardConsider the following data for two variables, x and y.x 2 3 4 5 7 7 7 8 9y 4 5 4 6 4 6 9 5 11a. Does there appear to be a linear relationship between x and y? Explain.b. Develop the estimated regression equation relating x and y.c. Plot the standardized residuals versus yˆ for the estimated regression equation developed in part (b). Do the model assumptions appear to be satisfied? Explain.d. Perform a logarithmic transformation on the dependent variable y. Develop an estimated regression equation using the transformed dependent variable. Do the modelassumptions appear to be satisfied by using the transformed dependent variable?Does a reciprocal transformation work better in this case? Explainarrow_forwardA box office analyst seeks to predict opening weekend box office gross for movies. Toward this goal, the analyst plans to use online trailer views as a predictor. For each of the 66 movies, the number of online trailer views from the release of the trailer through the Saturday before a movie opens and the opening weekend box office gross (in millions of dollars) are collected and stored in the accompanying table. A linear regression was performed on these data, and the result is the linear regression equation Yi=−0.840+1.4108Xi. Determine the coefficient of determination,r2,and interpret its meaning. Determine the standard error of the estimate. How useful do you think this regression model is for predicting opening weekend box office gross? Can you think of other variables that might explain the variation in opening weekend box office gross?arrow_forward
- A box office analyst seeks to predict opening weekend box office gross for movies. Toward this goal, the analyst plans to use online trailer views as a predictor. For each of the 66movies, the number of online trailer views from the release of the trailer through the Saturday before a movie opens and the opening weekend box office gross (in millions of dollars) are collected and stored in the accompanying table. A linear regression was performed on these data, and the result is the linear regression equation Yi=−0.372+1.3802Xi. Complete parts (a) through (d). a. Determine the coefficient of determination, r2, and interpret its meaning.b. Determine the standard error of the estimate.c. How useful do you think this regression model is for predicting opening weekend box office gross?d. Can you think of other variables that might explain the variation in opening weekend box office gross?arrow_forward(a)The following table shows the amount of melted plastic (in grams) at various temperatures of the plastic (in Celsius). Temperature (Celcius) 1.0 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2.0 Melted Plastic (in grams) 8.1 7.8 8.5 9.8 9.5 8.9 8.6 10.2 9.3 9.2 10.5 (I)Find the linear regression equation of the amount of melted plastic temperature. (ii)Estimate the amount of melted plastic at temperature 1.75 Celcius. (iii)Calculate the coefficient of correlation and comment on the relationship between temperature and amount of melted plastic. (iv)Calculate the Spearman’s rank correlation coefficient for the above data. (b)A random sample of twelve students were chosen, and their midterm test score ( y), assignment score (x1), and missed classes (x2) were…arrow_forwardThe table below shows the number of state-registered automatic weapons and the murder rate for several Northwestern states. x y 11.8 14.2 8.4 11.37.1 9.83.9 72.6 5.92.5 6.12.1 5.70.5 4.2 x= thousands of automatic weaponsy = murders per 100,000 residents Determine the regression equation in y = ax + b form and write it below. (Round to 2 decimal places) A) How many murders per 100,000 residents can be expected in a state with 2.2 thousand automatic weapons?Answer = . Round to 3 decimal places.B) How many murders per 100,000 residents can be expected in a state with 1.9 thousand automatic weapons?Answer = .Round to 3 decimal places.arrow_forward
- Functions and Change: A Modeling Approach to Coll...AlgebraISBN:9781337111348Author:Bruce Crauder, Benny Evans, Alan NoellPublisher:Cengage LearningAlgebra & Trigonometry with Analytic GeometryAlgebraISBN:9781133382119Author:SwokowskiPublisher:CengageTrigonometry (MindTap Course List)TrigonometryISBN:9781337278461Author:Ron LarsonPublisher:Cengage Learning
- Algebra and Trigonometry (MindTap Course List)AlgebraISBN:9781305071742Author:James Stewart, Lothar Redlin, Saleem WatsonPublisher:Cengage Learning