Mark Gershon, owner of a musical instrument distributorship, thinks that demand for guitars may be related to the number of television appearances by the popular group Maroon 5 during the previous month Qcollected the data shown in the following table:^Page 1 of 1escThis exercise contains only parts b, c, and d.b) Using the least-squares regression method, the equation for forecasting is (round your responses to four decimal places):c) The estimate for guitar sales if Maroon 5 performed on TV 9 times =d) The correlation coefficient (r) for this model =The coefficient of determination (2) for this model =The percentage of variation in sales that can be explained by TV appearances =!FI123OWLQ*12E2013$Maroon 5 TV AppearancesDemand for Guitars4(round your response to four decimal places).(round your response to four decimal places).R888F4%5sales (round your response to two decimal places).12FOY =T6MacBook Air% (round your response to two decimal places).FOY.3 562&7deF7XUM8006 5 7 684 11 6DIFaI-(9ODDFO0)-OdF10PF11=F12

Answered: Image /qna-images/answer/4b9a244e-9858-4ab2-b0aa-edb2da5ec651.jpg

Answered: Mark Gershon, owner of a musical…

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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C. Give an example for each of the following: a. Two variables in which you have enough reasons to explain their cause-and-effect relationship. State the independent (x) and dependent variable (y). Provide also description of the cause-and-effect relationship. b. Two variables from which a considerable possible nature of linear correlation may exist. Explain briefly the possible nature of the relationship. State both variables and give a short description of their relationship.
The data provides information on life expectancy and the number of televisions per thousand people in a sample of 22 countries, as reported by the world almanac and book of facts 2006 A. Describe the Direction and strength of the correlation between the two variables B. Because of the association between the variables, someone might mistakenly conclude that simply sending televisions to the countries with the lower life expectancies would cause their inhabitants to live longer. Comment on this argument and the reason televisions are likely associated with longer life expectancies C. In general, if two variables are strongly associated, does it follow there must be a cause and effect relationship between them? Explain
Susan, a personal trainer, was interested in whether or not there was a linear relationship between the number of visits her clients made to the gym each week and the average amount of time her clients exercised per visit. She took the following data. Client 1 2 3 4 5 6 Number of visits per week 1 3 4 2 3 5 Average time spent exercising per visit (hours) 2 1.5 1 2 2 0.30 Is the correlation coefficient significant? Group of answer choices No Yes Perhaps Insufficient Information to make a decision
If the linear association between two numerical variables is studied and the correlation coefficient is 0.32, it can be concluded that:a. The relationship between the variables is inverse and weak. b. The percentage of variability observed in the data that is explained by the error is 89.76%. c. The percentage of observed variability in the data that is explained by the model is 32%. d. None is correct Please explain clearly, thank you
A local store kept track of the number of advertisements it placed in local newspapers and the number of stereo systems it sold each week. Week 1 2 3 4 5 6 7 8 Advertisements, x 7 5 4 2 8 5 5 3 Stereos Sold, y 23 15 12 8 2 7 9 6 a) Create a scatter plot for the data showing Advertisements Vs. Stereos Sold. b) Determine the value of r, the correlation coefficient for the set of data and classify the type of correlation. c) What does this correlation coefficient suggest about the effectiveness of the advertisements? Explain
If we find that there is a linear correlation between the concentration of carbon dioxide in our atmosphere and the global temperature, does that indicate that changes in the concentration of carbon dioxide cause changes in the global temperature? Choose the correct answer below. A. No. The presence of a linear correlation between two variables does not imply that one of the variables is the cause of the other variable. B. Yes. The presence of a linear correlation between two variables implies that one of the variables is the cause of the other variable.
At question c it reads - competitor number 14(row12)
After gathering data about the number of starfish and measuring the pollution in areas of the ocean you find a negative linear correlation between pollution levels and number of starfish. What can you conclude based on this information? a. There is a confounding variable that is affecting both pollution and starfish. b. As pollution rises the number of starfish falls c. That pollution is causing starfish to die, leading to the negative correlation d. That pollution is supporting starfish, leading to the negative correlation
There appears to be a problem with one of your contact methods. Please check your SettingS Enter the following table into your calculator or the Desmos Grapher and create the scatterplot. X 2 60 33 2,100 36 2,860 49 14,440 65 10,560 79 567,860 79 570,560 97 2,238,200 95 3,674,760 98 5282120 1. Is there a correlation for the data? If so, is it strong or weak? 2. What shape of function does the scatterplot show? 3. Use your calculator or the Desmos Grapher to find the equation for the line/curve of best fit. Now, use the equation editor to give the equation for the line/curve of best fit. 4. Determine if this equation is a good predictor of the data, using the appropriate number as proof/reasoning.
The following table shows the approximate number of wireless telephone subscriptions in recent years.

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