**Graph-based Quadratic Regression Analysis****Instructions:** Use the data shown in the graph to write a quadratic regression equation. Then predict the box office revenue in 2013. Let $ x $ represent the number of years since 2000.**Graph Description:**- **Title:** Box Office Revenue- **Y-Axis (Vertical):** "Millions of dollars" ranging from 27 to 32.- **X-Axis (Horizontal):** "Years" ranging from 2004 to 2009.- **Data Points:** - Year 2004: Box office revenue is \$28.9 million. - Year 2005: Box office revenue is \$28.7 million. - Year 2007: Box office revenue is \$29.4 million. - Year 2008: Box office revenue is \$30.8 million.The graph shows the movement in box office revenue over time, with a slight dip in 2005 followed by a gradual increase through to 2008. The trend and data from the graph need to be utilized to form a quadratic regression equation. This equation can then project future box office revenue, specifically to predict for the year 2013.**Objective:** By analyzing the trend in the graph, derive a quadratic equation that best fits the presented data points and use it to forecast the box office revenue for subsequent years.

From the provided graph, which represents the year (since 2000) on the x-axis and the revenue (in…

Answered: Use the data shown in the graph to…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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A company manufactures memory chips for microcomputers. The table below shows the price, p. per chip at which x million chips can be sold. Using linear regression on a graphing calculator, the price-demand function to model this data is given by p(x) = 84 - 4x x (millions) p ($) B0 4. 68 6. 48 a. Graph the data and the model on the same coordinate system. Choose the correct graph below. All graphs are shown on a scale of 10,25,5) by 10,100,10). where the x-axis represents the number of chips sold (in milions) and the y-axis represents the price in dollars OD. Q Next an Jy 13M 04Da 1 80 4 68 48 14 28 20 4 b. Use p(x) to estimate the price per chip for a demand of 4 million chips. The price per chip for 4 million chips will be $ (Round to nearest integer.) c. Use p(x) to estimate the demand at a price of $3. The demand at a price of $3 is million chins
The table gives the amount of money (in dollars) spent on football by a major university. Letting a represent the number of years since 2005, and letting y represent the amount of money spent on football, in thousands of dollars, use the regression capabilities of a graphing calculator to find the equation of the line of best fit. Round values off to the nearest hundredth. Then, use your equation to make the following predictions. Year 2005 2006 2007 2008 2009 2010 Dollars spent on football 165,000 196,000 210,000 226,000 242,000 279,000 The equation of the line of best fit is: Hint Predict the amount of money that will be spent on football in the year 2024. +A Predict the amount of money that will be spent on football in the year 2041.
Use the time/tip data from the table below, which includes data from New York City taxi rides. (The distances are in miles, the times are in minutes, the fares are in dollars, and the tips are in dollars.) Find the regression equation, letting time be the predictor (x) variable. Find the best predicted tip for a ride that takes 22 minutes. How does the result compare to the actual tip amount of $5.05? Distance 1.80 8.51 1.40 1.65 12.71 1.02 1.32 Time Fare Tip 0.68 8.00 6.00 25.00 31.00 18.00 11.00 27.00 8.00 16.30 31.75 12.30 9.80 36.80 7.80 7.80 6.30 1.50 2.98 2.46 1.96 0.00 2.34 0.00 1.89 The regression equation is ŷ = + (x. (Round the y-intercept to two decimal places as needed. Round the slope to four decimal places as needed.)
Define the different ways to use linear regression?
Develop a linear regression equation to predict future demand from thefollowing data:Demand 23 24 31 28 29Year 2008 2009 2010 2011 2012a. Write the regression equation.b. Predict demand for 2015.c. Name the independent variable.d. Name the dependent variable.
Part a: Make a scatter plot and determine which type of model best fits the data.Part b: Find the regression equation.Part c: Use the equation from Part b to determine y when x = 7.
Use the time/tip data from the table below, which includes data from New York City taxi rides. (The distances are in miles, the times are in minutes, the fares are in dollars, and the tips are in dollars.) Find the regression equation, letting time be the predictor (x) variable. Find the best predicted tip for a ride that takes 30 minutes. How does the result compare to the actual tip amount of $4.70? Distance 1.80 1.40 12.71 1.32 8.51 0.49 2.47 Time 25.00 18.00 27.00 31.00 2.00 8.00 36.80 7.80 31.75 4.80 Fare 16.30 12.30 Tip 1.50 2.46 0.00 0.00 1.65 18.00 11.00 14.30 9.80 2.98 0.00 4.29 1.96 The regression equation is y = + X. (Round the y-intercept to two decimal places as needed. Round the slope to four decimal places as needed.)
The table gives the amount of money (in dollars) spent on football by a major university. Letting x represent the number of years since 2006, and letting y represent the amount of money spent on football, in thousands of dollars, use the regression capabilities of a graphing calculator to find the equation of the line of best fit. Round values off to the nearest hundredth. Then, use your equation to make the following predictions. Year 2006 Dollars spent on football 173,000 The equation of the line of best fit is: Hint 2009 2010 2011 2007 2008 19 000 210,000 223,000 243,000 271,000 Predict the amount of money that will be spent on football in the year 2022. Predict the amount of money that will be spent on football in the year 2041.
Use the time/tip data from the table below, which includes data from New York City taxi rides. (The distances are in miles, the times are in minutes, the fares are in dollars, and the tips are in dollars.) Find the regression equation, letting time be the predictor (x) variable. Find the best predicted tip for a ride that takes 20 minutes. How does the result compare to the actual tip amount of $4.55? Distance 0.68 2.47 12.71 1.80 1.02 8.51 1.32 1.40 6.00 18.00 27.00 25.00 8.00 31.00 8.00 18.00 6.30 14.30 36.80 16.30 7.80 31.75 7.80 12.30 1.89 4.29 0.00 1.50 2.34 2.98 0.00 2.46 Time Fare Tip The regression equation is y=+ X (Round the y-intercept to two decimal places as needed. Round the slope to four decimal places as needed.) The best predicted tip for a ride that takes 20 minutes is $ (Round to the nearest cent as needed.) How does the result compare to the actual tip amount of $4.55? O A. The best predicted value is close to the actual tip of $4.55. O B. The best predicted value…
You estimate a regression equation that estimates individuals’ yearly income as a function of age (measured in years), years of education, and their score on a standardized intelligence exam (IQ test). The estimated coefficients are: I = 26.3 + 4.21*Age + 7.21*Education + .23 * IQ Where I is income measured in thousands of dollars. Based on the regression results, how much more should an individual earn after attending graduate school for two years? Explain your reasoning.
Estimate the price per sleeping bag when the weekly demand is 187. Round to the nearest cent. $ ____per sleeping bag
Briefly explain how we can investigate whether the regression model faces the problem of multicollinearity

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Use the data shown in the graph to write a quadratic regression equation. Then predict the box office revenue in 2013. Let x represent the number of years since 2000. Millions of dollars Box office revenue 32- 31- 30+ 29- 28- 27+ 2004 2005 2006 2007 2008 2009 Year 28.9 28.7 29.4 30.8