8. There is a linear relationship between the price of an inkjetprinter and the pages per minute that can be printed. Theregression equation, which explains 55% of the variabilityin price, is:Predicted price = -94.22 + 90.88 (pages per minute).This means the correlation, or r, between price and pagesper minute must be equal to approximatelyA. 0.28.B. 0.91.c. 0.30.D. 0.74.E. None of the above answers are correct

Given : The regression equation, which explains 55% of the variability in price, is: Predicted price…

Answered: 8. There is a linear relationship…

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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In a quantitative analysis where widowed people suffering with bereavement suffer with a poor quality of life. Would Widowed Individuals be the Unit of Analysis? The quality of life be the dependent variable, therefore making bereavement the independent variable?
Based on the analyst's data and regression line, complete the following. For these data, values for earnings per share that are greater than the mean of the values for earnings per share tend to be paired with current stock prices that are (greater than, lesser than) the mean of the current stock prices. According to the regression equation, for an increase of one dollar in earnings per share, there is a corresponding increase of how many dollars in current stock price?
You run a linear regression to predict BMI (body mass index) from average minutes of physical activity per day. You calculate the slope of the regression to be -.054. Interpret the slope in terms of the independent and dependent variables.
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.
The table below shows the average weekly wages (in dollars) for state government employees and federal government employees for 10 years. Construct and interpret a 98% prediction interval for the average weekly wages of federal government employees when the average weekly wages of state government employees is $878.The equation of the regression line is y=1.342x+55.816. Wages [state]- 712, 746, 786, 803, 841, 879, 918, 926, 954, 979 Wages [federal]- 1,024, 1,048, 1,098, 1,135, 1,195, 1,240, 1,270, 1,299, 1,340, 1,376 Construct and interpret a 98% prediction interval for the average weekly wages of federal government employees when the average weekly wages of state government employees is $878. Select the correct choice below and fill in the answer boxes to complete your choice. (Round to the nearest cent as needed.) PICK THE CORRECT ANSWER [A] We can be 98% confident that when the average weekly wages of state government employees is $878, the average weekly wages of…
While attempting to measure its risk exposure for the upcoming year, an insurance company notices a trend between the age of a customer and the number of claims per year. It appears that the number of claims keep going up as customers age. After performing a regression, they find that the relationship is (claims per year) = 0.385*(age) + 5.218. If a customer is 44.315 years old and they make an average of 11.239 claims per year, the residual is -11.04. Interpret this residual in terms of the problem. Question 10 options: 1) The number of claims per year is 11.04 claims less than what we would expect. 2) The age is 11.04 years larger than what we would expect. 3) The age is 11.04 years less than what we would expect. 4) The number of claims per year is 11.239 claims less than what we would expect. 5) The number of claims per year is 11.04 claims…
Bloomberg Intelligence listed 50 companies to watch in 2018 (www.bloomberg.com/features/companies-to-watch-2018). Twelve of the companies are listed here with their total assets and 12-month sales. Total Assests ($ billions) 134.47 12-Month Sales ($ billions) Total Assests ($ billions) 12-Month Sales Company Eni SpA Discovery Communications Dong Energy Company AMC Entertainment ($ billions) 68.13 6.59 9.81 4.19 16.15 DSV 6.14 10.54 20.5 Alaska Air Boohoo.com 9.16 10.7 6.9 AMD 3.37 4.62 0.235 0.545 25.11 Cabot Oil & Gas 5.22 1.58 Danone 52.12 Engie 166.84 72 Experian 7.69 4.34 E Click here for the Excel Data File a. Draw a scatter diagram. 1. On the graph below, use the point tool to plot the point corresponding to the first Sales and her Earnings Sales1). 2. Repeat the process for the remainder of the sample (Sales 2, Sales 3, ... ). 3. To enter exact coordinates, dooble-click on the point and enter the exact coordinates of x and y. Tools 90 80 point 2 point 3 70 Ch13_40 18e.xlsx…
A linear model was fit to predict weekly Sales of frozen pizza (in pounds) from the average Price ($/unit) charged by a sample of stores in a city in 39 weeks over a three -year period. The average Sales were 63, 200 pounds (SD = 10, 670 pounds), and the correlation between Price and Sales was -0.529. If the Price in a particular week was two SD higher than the mean Price, how much pizza would you predict was sold that week?
Nine pairs of data yield a regression equation of y=0.93x + 19.4, with r= 0.967 and an average y value of 64.70. What is best predicted value for y when x =65? Is it 79.85, 25.74, 89.61, 57.82, 70.55, 96.70, 19.40, or 64.70?
1.
The correlation between hours of sleep and hours of study is r = 0.14. (a) Calculate r². (Round your answer to four decimal places.) ,2 = = (b) Choose a sentence that interprets value r². 1.96% of the variation in hours of sleep is not explained by hours of study. Variation in hours of study explains about 1.96% of the observed variation in hours of sleep. 1.96% of the variation in hours of sleep is not explained by the variation in hours of study. Hours of study explains about 1.96% of the observed variation in hours of sleep.
Develop a scatterplot and explore the correlation between customer age and net sales by each type of customer (regular/promotion). Use the horizontal axis for the customer age to graph. Find the linear regression line that models the data by each type of customer. Round the rate of changes (slopes) to two decimal places and interpret them in terms of the relation between the change in age and the change in net sales. What can you conclude? Hint: Rate of Change = Vertical Change / Horizontal Change = Change in y / Change in x

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