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 2 Sales q (billions) 0.5 1 2 Find the regression line (round coefficients to one decimal place). a(p) = Jse the regression line to estimate the demand (in millions of units sold) when the selling price was $310. million Need Help? Read It Master It

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 2 Sales q (billions) 0.5 1 2 Find the regression line (round coefficients to one decimal place). a(p) = Jse the regression line to estimate the demand (in millions of units sold) when the selling price was $310. million Need Help? Read It Master It

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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Use the scatterplot of Vehicle Registrations below to answer the questions Vehicle Registrations in the United States, 1925- 2011 Vehicles millions 300 y = 3.0161x - 5819.5 R? = 0.9695 250 200 150 100 50 1920 -50 1940 1960 1980 2000 2020 Year Write a sentence explaining the value of the slope for this regression line. For every increase in year, the number of vehicle registrations in the US increases by 3.0161 million. For every increase in year, the number of vehicle registrations in the US increases by 5819.5. For every increase in vehicle registrations in the US, the number of years increases by 5819.5. For every increase in vehicle registrations in the US, the number of years increases by 3.0161 million. Registrations (in millions)
6. The table below shows varying prices of music sold on a streaming service. Determine the linear regression to estimate the price of albums sold, use your regression to estimate the price of albums sold if 300,000 albums are selling each week and state the coefficient of determination, r², and interpret its meaning in the context of this problem. Weekly sales (in thousands) Price ($) 440 $8 380 250 180 $8.50 $10 $11.50
What is the dollar amount of change in sales for each new year according to the regression (slope) model? Do not use commas or $ in your answer.
A5 The model below explains CEO salary (salary) expressed in thousands of pounds as a function of a firm’s sales in million pounds (sales), the return on a firm’s equity (roe), and the industry in which the firm is operating. There are two industries in the sample, services and consumer goods. These industries are captured respectively by two dummy variables, service and goods. The regression based on a sample of 209 firms is as follows: salary = 4.5 + 0.27sales + 0.015roe – 0.08service (0.24) (0.03) (0.05) (0. 05) R-squared = 0.35 SSR(Residual Sum of Squares) = 42.9 Implement an F-test for whether the determinants of CEO salaries differ significantly between service and consumer goods industries. What do you conclude?
Plzz explain
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 1.40 0.68 0.49 1.32 18.00 6.00 2.00 8.00 12.30 6.30 4.80 7.80 2.46 1.89 0.00 0.00 Time Fare Tip 12.71 1.65 1.80 27.00 11.00 25.00 36.80 9.80 16.30 0.00 1.96 1.50 ... 2.47 D 18.00 14.30 4.29 The regression equation is y = 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 22 minutes is $ (Round to the nearest cent as needed.) How does the result compare to the actual tip amount of $5.05?
Prices and mileage of 48 Dodge 2500 Pickup trucks with Cummins diesel engines. Prices ranged from $28,925 to $68,650. Mileage ranged from 315 to 186,466. While I did not share the trucks were 2013 to 2020 models. Please show all work. Interpret b0 in the context of this problem. Interpret b1 in the context of this problem. What percent of total variation in prices is explained by the regression model? Provide evidence to support your answer. Price Mileage 52293 34450 50987 23372 62988 21738 37500 81176 66000 23118 47601 71143 47750 18214 42000 32303 50987 23372 28925 143737 42050 58561 51738 9691 51500 23969 44988 48781 29500 114809 46975 16918 63999 15507 46975 16918 43222 50459 38386 87192 42000 68722 62250 14042 62998 21738 32130 103644 52293 34450 52900 68883 53172 3656 53494 315 39594 88441 63246 23631 58250 34974 59391 18383 50650 42737 54976 84200 68650 14401 51114 47433 43900 74106 37450…
Statistics - Regression Analysis
How much should a healthy Shetland pony weigh? Let x be the age of the pony (in months), and let y be the average weight of the pony (in kilograms). x 3 6 12 20 26 y 60 95 140 160 189 (a) Make a scatter diagram of the data and visualize the line you think best fits the data. Flash Player version 10 or higher is required for this question. You can get Flash Player free from Adobe's website. (b) Would you say the correlation is low, moderate, or strong? low moderate strong Would you say the correlation is positive or negative? positive negative (c) Use a calculator to verify that x = 67, x2 = 1265, y = 644, y2 = 93,546, and xy = 10,544. Compute r. (Round your answer to three decimal places.) As x increases from 3 to 26 months, does the value of r imply that y should tend to increase or decrease? Explain your answer. Given our value of r, y should…
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
B-4 A toy store manager would like to know how the price can affect the sales of toys. In his data, he obtained 100 pairs of prices (unit: £) and volume of sales (unit: hundreds) for the toys. A simple linear regression was run with Sales as the dependent variable and Price as the independent variable with the following results. Regression Statistics R Square Adj. RSqr StErr of EST 0.876 0.767 0.701 4.184 Summary Table Variable Coeff. Std. Err Intercept 64.742 11.310 Price -2.645 0.602 (a) Write down the estimated regression equation. Explain this regression equation. (b) What's the value for R Square? Explain the meaning of this value. (c) Test the significance of the model at a=0.05 level. Write down the hypothesis you are testing, the statistic you use to evaluate the test, and your conclusion. (d) Use the regression output to construct a 99% confidence interval for the predicted sales when the price is set to be 15. (e) A colleague in the toy store claims that £1 increase in the…
Using the data, run a regression where you control for “Promotion,” and test the effect of “Wins” on “Attendance”. Does “Promotion” have a significant effect on “Attendance”? Wins Promotion Attendance 4 29500 36300 6 55700 40100 6 71300 41200 8 87000 53000 6 75000 44000 7 72000 45600 5 55300 39000 7 81600 47500