6 Researchers collected data on activity in an area of the brain that responds to physical pain and thestress from social exclusion. Results from a simple linear regression are given as follows.Simple Linear Regression Results:Dependent Variable: brain,Independent Variable: distressSample size: 13R (Correlation coefficient): 0.8782R-sq0.7712991%3!ParameterEstimateInterceptSlope-0.126084540.060782436We conclude that:for every additional distress input, the brain activity increases by 0.061 units on average.for every additional -0.126 distress input, the brain activity increases by 0.061 units on average.for every additional 0.061 distress input, the brain activity increases by 1 unit on average.

Answer:----. Date:----6/10/2021 since slope is change in dependent variable with 1…

Answered: We conclude that: for every additional…

Big Ideas Math A Bridge To Success Algebra 1: Student Edition 2015

1st Edition

ISBN:9781680331141

Author:HOUGHTON MIFFLIN HARCOURT

Publisher:HOUGHTON MIFFLIN HARCOURT

Chapter4: Writing Linear Equations

Section: Chapter Questions

Problem 12CR

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randomly selected middle school students. Using this data, consider the equation of the regression line, yˆ=b0+b1x, for predicting the overall grade average for a middle school student based on the number of hours spent unsupervised each day. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Hours Unsupervised 1.5 2 3 3.5 4 4.5 5.5 Overall Grades 99 96 84 75 68 65 60 Table Step 1 of 6 : Find the estimated slope. Round your answer to three decimal places.
randomly selected middle school students. Using this data, consider the equation of the regression line, yˆ=b0+b1x, for predicting the overall grade average for a middle school student based on the number of hours spent unsupervised each day. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Hours Unsupervised 1.5 2 3 3.5 4 4.5 5.5 Overall Grades 99 96 84 75 68 65 60 Table Find the estimated slope, y interscept and correlation coefficient Round your answers to three decimal places.
Listed below are the chest sizes (in inches) and weights (in pounds) of randomly selected bears that were anesthetized and measured (based on data from Gary Alt ena Minitab, Inc.). Since it is much more difficult to weigh a bear than to measure its chest size, the presence of a correlation could result in a method for estimating weight based on chest size.a) Calculate the correlation coefficient and test the significance at 5% probability;b) Determine the simple linear regression equation;c) Find the prediction for the weight (in pounds) of a bear with a chest size of 50 inches.
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The fish in my pond have mean length 14 inches with a standard deviation of 2 inches and mean weight 4 pounds with a standard deviation of .8 pounds. The correlation coefficient of length and weight is .7. If the length of a particular randomly selected fish is reported to be 17 inches, then what should we predict for the weight of that fish using simple linear regression?
Show that an interaction term of a dummy variable and a regressor changes the slope of a regression line..
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Give an example of a research question that would be suitable for computing a: a) Correlation coefficient b) Regression line
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