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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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
Assuming the following table of the cost (X) and selling (Y) price of flight tickets in dollars ($): Cost Price (X) Sell Price (Y) 200 300 230 250 500 510 540 700 670 730 320 500 190 412 280 355 625 635 Compute the following: 1. Plot a scatter plot of the data (manual plot or use any software like excel). (2 marks) 2. Pearson correlation coefficient (r). 3. The relation type and strength. 4. Linear regression formula (9, = Bo +B,X,). 5. The estimated of the ticket selling price when the cost is $500. 6. The error of the ticket selling price estimate when the cost is $500. 7. What is the percent of cost price variable interpret the variation of sell price variable (How much the coefficient of determination)? (2 marks) (Imark) (2 marks) (I mark) (I mark) (I mark)
Two separate tests are designed to measure a student's ability to solve problems. Several students are randomly selected to take both tests and the results are shown below. Test A (x) 43 65 73 34 99 78 65 Test B (y) 39 60 62 20 85 70 54 1. Calculate r, the linear correlation coefficient. Identify the values for each part below. Part a. The value of n Part b. The sum of the x-values Part c. The sum of the y-values Part d. The sum of the x-squared values Part e. The sum of the y-squared values Part f. The sum of the (xy) values Part g. The final value of r rounded top three decimal places
2.) The table shown below are the raw scores obtained by eight (8) students. Is there a relationship in their scores in Mathematics and in Biology? Use Pearson Product-Moment Coefficient of Correlation to establish the relationship. Student 1 3 4 6. 8. Mathematics (x) 6. 3. 4 3 Biology (y) 8. 3. 7 4
2. A study was conducted to determine the relationship between a person’s monthly income in Omani Rials and the number of meals that person eats away from home per month. The data are shown below.
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.
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.
. Differentiate between correlation vs. causation. When two variables are correlated (such as strength and running speed), they are associated with one another. Explain how, even if there is a correlation between the two, one might not cause the other. Provide an example of an association between variables where a causal relationship makes sense conceptually. Why can't correlations be used as a tool to prove a causal relationship between variables rather than just an association?
2. An aerobic exercise instructor submitted to the shipping company the data given in the following table, which shows the recommended maximum exercion heart rate for individuals of the given ages. See table below. If the Chief engineer is 62 years old, predict. naximum exercise heart rate. Age (x years) Maximum heart rate (y beats per minute) 20 40 60 170 153 136 A. What is the coefficient of correlation of the age and the maximum rate of the chief engineer? a. -100 b. 100 C. -0.99 d. 0.65
If the correlation coefficient obtained from data pairs of variables X and Y is 0.98 the strength and direction of the relationship between X and Y is which of the following: (i) very strong positive (ii) moderate positive (iii) moderate negative (iv) very weak negative

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