Researchers used the simple linear regression model to study the relationship between social media screen time during a week andend-of-week quiz results. As one of the researchers, find the predicted quiz score if a student spends 18.46 hours during the nextweek using social media. Use the data in the table below to build the estimated regression equation and then use this equation tofind the predicted quiz score:Social media weekly time, hours14.53.730.434.232.716.813.64,720,64.9Round your answer to 2 decimals.LessIQuiz results, out of 10076.898.4557.645.574.7875.793.6757.835.794.8

Social media time in hours (x)Quiz results out of…

Answered: Researchers used the simple linear…

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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Using your favorite statistics software package, you generate a scatter plot with a regression equation and correlation coefficient. The regression equation is reported as Y = 71.95x + 52.94 and the r = - 0.148. - What proportion of the variation in y can be explained by the variation in the values of x? r2 =
The chart below shows the correlation between degrees Fahrenheit and degrees Celsius. 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 ºC when ºF = 47.Part d: Does this answer seem reasonable? Explain.
A temperature dataset gives y mean April temperature (Fahrenheit) and x = geographic latitude for 20 U.S. cities. The simple linear regression equation for the sample is ŷ = 119 - 1.64x. (a) The value of latitude for a certain city is 41. What is the predicted April temperature for this city? (Round your answer to the nearest tenth.) degrees Fahrenheit (b) The mean April temperature for this city is 48. What is the residual for the city? (Round your answer to the nearest tenth.) degrees Fahrenheit You may need to use the appropriate table in the Appendix of Tables to answer this question.
Using your favorite statistics software package, you generate a scatter plot with a regression equation and correlation coefficient. The regression equation is reported as y=-59.97x+67.54 and the r=-0.31 What percentage of the variation in y can be explained by the variation in the values of x? r² = % (Report exact answer, and do not enter the % sign) .
The number of megapixels in a digital camera is one of the most important factors in determining picture quality. But, do digital cameras with more megapixels cost more? The following data show the number of megapixels and the price ($) for 10 digital cameras(Consumer Reports, March 2009). Use these data to develop an estimated regression equation that can be used to predict the price of a digital camera given the number of megapixels. Brand and Model Megapixels Price (S) Canon PowerShot SD1100 IS Casio Exilim Card EX-510 8 180 200 230 10 Sony Cyber-shot DSC-T70 Pentax Optio M50 Canon PowerShot G10 120 470 15 8 Canon PowerShot A590 IS Canon PowerShot El 140 180 10 12 Fujifilm FinePix FOOFD Sony Cyber-shot DSC-W170 Canon PowerShot A470 310 10 250 110
Mike Jimenez is president of the teachers’ union for Preston School District. In preparing for upcoming negotiations, he would like to investigate the salary structure of classroom teachers in the district. He believes there are three factors that affect a teacher’s salary: years of experience, a rating of teaching effectiveness given by the principal, and whether the teacher has a master’s degree (0 = No, 1 = Yes). A random sample of 20 teachers resulted in the following data. Salary ($ Thousands) Years of Experience Principal's Rating Master's Degree 51.1 8 95 0 43.6 5 84 0 39.3 2 100 1 53 15 51 1 48.6 11 53 0 55 14 81 1 52 9 85 0 46.8 7 52 1 58.6 22 68 1 41.7 3 97 1 35.7 1 83 0 40.6 5 71 0 61.8 23 77 1 46.7 17 61 0 48.4 12 83 1 43.6 14 71 0 51.8 8 59 1 40.7 4 93 0 42.8 2 58 1 42.9 8 90 0 d. Develop a correlation matrix and state the correlation for each independent variable as it relates to the dependent variable. (Round all…
Develop an estimated multiple regression equation that relates risk of a stroke to a person's age, systolic blood pressure and whether the person is a smoker. x1=persons age x2systrolic blood pressure x3=whether person is a smoker or non smoker
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Use Excel Spreadsheets, Google Sheets, or GeoGebra to create a scatter plot for the data below. (You will upload your graph in the next question.) Use the graph to answer the questions below the table. The following data are the morning and evening high tide levels for Charleston, SC from January 1-14,2017. The information for the PM high tide for January 4 is missing. Create a scatter plot. Find the regression line and use it to estimate the PM high tide for January 4. Then find the correlation coefficient. (NOTE: The first column identifies the day. This data will not be used in the scatter plot.) Day AM High (in feet), xx PM High (in feet), yy 1 5.6 4.8 2 5.5 4.8 3 5.4 4.9 4 5.2 5 5.0 5.1 6 5.2 5.0 7 5.4 4.9 8 5.7 5.0 9 6.0 5.1 10 6.3 5.3 11 6.4 5.4 12 6.5 5.4 13 6.4 5.4 14 6.2 5.3 Source: SCDHEC.gov
The data show the chest size and weight of several bears. Find the regression equation, letting chest size be the independent (x) variable. Then find the best predicted weight of a bear with a chest size of 58 inches. Is the result close to the actual weight of 572 pounds? Use a significance level of 0.05. Chest size (inches) 46 57 53 41 40 40 Weight (pounds) 384 580 542 358 306 320 LOADING... Click the icon to view the critical values of the Pearson correlation coefficient r. What is the regression equation? y=nothing+nothingx (Round to one decimal place as needed.)
A real estate company wanted to see the relationship between home prices and square footage of homes for sale. The regression line is ŷ = –802,456 + 144x, where x is the square footage of a home. Complete the statement based on the information. For every -square-foot increase, the predicted home price is predicted to increase by $
The data show the chest size and weight of several bears. Find the regression equation, letting chest size be the independent (x) variable. Then find the best predicted weight of a bear with a chest size of 48 inches. Is the result close to the actual weight of 538 pounds? Use a significance level of 0.05.

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