The table shows a country's average fuel consumptionfor "light duty" vehicles (passenger cars and small trucks) forseveral years. Let x be the number of years since 1985, sothat x = 5 stands for 1990 and so forth.Year Fuel Economy (mpg)199016.9199517.7200018.3(a) Find the linear regression model for these data. Whatdoes the slope in the regression model represent?200518.9(b) Use the linear regression model to predict the country'saverage fuel economy for light duty vehicles in the year 2035. 201019.6(a) Find the linear regression model for these data. Choose the correct answer below.(Use integers or decimals for any numbers in the equation. Round to the nearest hundredth as needed.)

The given table, Year Fuel Economy(mpg) 1990 1995 2000 2005 2010 16.9 17.7 18.3 18.9 19.6…

Answered: The table shows a country's average…

Advanced Engineering Mathematics

10th Edition

ISBN:9780470458365

Author:Erwin Kreyszig

Publisher:Erwin Kreyszig

Chapter2: Second-order Linear Odes

Section: Chapter Questions

Problem 1RQ

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A statistical program is recommended. The owner of Showtime Movie Theaters, Inc., would like to predict weekly gross revenue as a function of advertising expenditures. Historical data for a sample of eight weeks follow. Weekly Television Gross Newspaper Advertising Advertising ($1,000s) ($1,000s) Revenue ($1,000s) 96 5.0 1.5 90 2.0 2.0 95 4.0 1.5 92 2.5 2.5 95 3.0 3.3 94 3.5 2.3 94 2.5 4.2 94 3.0 2.5 1 (a) Develop an estimated regression equation with the amount of television advertising as the independent variable. (Round your numerical values to two decimal places. Let x₁ represent the amount of television advertising in $1,000s and y represent the weekly gross revenue in $1,000s.) y = 88.64 + 1.60x1 X (b) Develop an estimated regression equation with both television advertising and newspaper advertising as the independent variables. (Round your numerical values to two decimal places. Let x₁ represent the amount of television advertising in $1,000s, x₂ represent the amount of…
Demand for Smartphones The following table shows worldwide sales of a type of phone and their average selling prices in 2012, 2013, and 2017. Year 2012 2013 2017 Selling Price p ($100) 4 Sales q (billions) 0.5 3 1 2 2 Find the regression line (round coefficients to one decimal place). q(p) = 1.0166 X Use the regression line to estimate the demand (in millions of units sold) when the selling price was $320. 1016.6 million
A grocery store manager did a study to look at the relationship between the amount of time (in minutes) customers spend in the store and the amount of money (in dollars) they spend. The results of the survey are shown below. Time 10 8 6 13 19 18 27 10 7 Money 58 25 41 51 84 81 87 45 15 1. The equation of the linear regression line is: ˆyy^ = ______ +_____ xx (Please show your answers to two decimal places)
Q1: [Regression and Correlation] The index of biotic integrity (IBI) is a measure of water quality in streams. As a data analyst you must monitor, track, and predict changes in water quality. You want to create a simple linear regression model that will allow you to predict changes in IBI in forested area. The following table conveys sample data from a coastal forest region and gives the data for IBI and forested area in square kilometers. Let forest area be the predictor variable (x) and IBI be the response variable (y). IBI X 47 72 21 72234 19 58 49 Forest Area Y 38 59 27 24 63 49 45 Required a. Q2: [Naïve Bayes] a. Write the pseudo-code of the following Naïve Bayes algorithm b. Consider the given dataset that classifies animals into two distinct classes. The classes are labeled as 'cat' and 'dog'. Use Naive Bayes classifier to figure out the class (cat/dog) of an instance if it has the following values of attributed Plot Scatter graph b. Determine the regression equation c. Compute…
Fit a linear regression model using the empirical probit as the dependent variable and the log dose level as the independent model – that is, set-up your probit model. What would be the visual Linear regression Models of c and d.
The percentage of adults who smoke in America is given below. Year America 1970 37.4 mon n 1980 33.2 1990 25.3 2000 23.1 2010 19.4 a) Write the linear regression model for A(x), representing the percentage of adult smokers in America x years since 1970. [Use your calculator OR Desmos to find the equation, Round coefficients to the nearest 100"] b) What is the slope of A(x)? Explain its meaning. c) Solve A(x) = 15. Show your work. Explain the meaning of your answer. d) Suppose in 2005, there were approximately 200 million adults in America. Use your model to estimate how many were smokers. Show how you get your answer.
A study investigated how the content of vitamin A in carrots is affected by the time being cooked. In this example: X represents the amount of time, in minutes, that the carrot slices were cooked Y represents the content of vitamin A (in milligrams) in the carrot slices The least-squares regression equation for this relationship is: Y = 23.4 – 0.55X What is the slope of the regression line? Provide a numeric value as shown in the equation.
The following table gives the amount spent on cellular service. Date Cellular service revenue(in billions) 2011 1.01 2012 1.05 2013 1.09 2014 1.11 Plot the data points. (Let tbe the number of years since 2011 and C the amount of cellular service revenue, in billions of dollars.) CORRECT (b) Find the equation of the regression line. (Let t be the number of years since 2011 and C the amount of cellular service revenue, in billions of dollars. Round the regression line parameters to three decimal places.) C(t) = C(t) = 0.034t+1.014 CORRECT Add its graph to the plotted data. CORRECT (c) In 2015, $1.14 trillion was spent on cellular service. If you had been a financial strategist in 2014 with only the data in the table above available, what would have been your prediction for the amount spent on cellular service in 2015? (Round your answer to two decimal places.) billion dollars CORRECT…
Based on data, check the linear regression for multicollinearity, and identify the best regression. Based on the final regression, check for autocorrelation of residuals, and make conclusion.
The following table shows the approximate amount of trash produced in an industrialized country from 1980 to 2000. Let x represent the year after 1980 (1980 is year 0) and y represent the amount of trash (millions of tons). Year Million Tons 1980 150 1990 202 2000 220 (a) Draw the graph for this data. (b) Find the equation of the regression line for the data. (Round your answers to one decimal place.)y = x + (c) Use the equation to predict the amount of trash y that will be produced in 2010 and 2015. (Round your answers to one decimal place.) 2010: _______million tons 2015: _______ million tons

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