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…

Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018

18th Edition

ISBN:9780079039897

Author:Carter

Publisher:Carter

Chapter4: Equations Of Linear Functions

Section: Chapter Questions

Problem 4SGR

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A study by a merchant investigated the relation between the advertising costs AC (in hundreds of dollars) and the sales, Sales, also in hundred of dollars. Fifteen observations were obtained in which the AC and Sales were recorded and input into StatCrunch which produced the regression report below Simple linear regression results: Dependent Variable: SalesIndependent Variable: AC Sales = 147.61 + 5.43 ACSample size: 15R (correlation coefficient) = 0.77103651R-sq = 0.594Estimate of error standard deviation: 49.63 Parameter Estimate Std. Err. Alternative DF T-Stat P-value Intercept 147.61006 47.80385 ≠ 0 13 3.08782 0.0086 Slope 5.4339623 1.2447062 ≠ 0 13 4.36565 0.0008 Compute the predicted value Sales when AC is 35
Simple linear regression results: Dependent Variable: Heart. DiseaseIndependent Variable: BikingHeart. Disease = 18.115809 - 0.20845321 BikingSample size: 68R (correlation coefficient) = -0.94616452R-sq = 0.8952273Estimate of error standard deviation: 1.5273175 Correlation between Biking and Heart. Disease is: -0.94616452. Correlation between Smoking and Heart. Disease is: 0.32421. 1) State r 2 (i.e., the coefficient of determination) for “Biking” and “Heart.Disease” and explain what this value means in context of the data set.
Let price denote a price index for the goods sold by a restaurant, advert the amount spent on advertising, sales the sales for the restaurant, and consider the following two regressions First regression: sales = B1 + B2price + B3price? + B4advert + ßsadvert? + e, Second regression: sales = B1 + B2price + B3price? + e We estimate both regressions using a sample of 105 observations. The sum of square residuals (E ê) from the first regression equals 50, while the sum of square Li=1 residuals from the second regression equals 70. Suppose we are interested in testing the null hypothesis that expected sales do not depend on advertising. What is the F- statistic for this null hypothesis? Recall the F-statistic is given by ((SSER - SSEU)/J)/(SSEy/(n – K)). O a. -15 O b. 42 Oc. 21 O d. 20 O e. All other options are incorrect.
What is the relationship between diamond price and carat size? 307 diamonds were sampled and a straight-line relationship was hypothesized between y = diamond price (in dollars) and x = size of the diamond (in carats). The simple linear regression for the analysis is shown below: Least Squares Linear Regression of PRICE Interpret the standard deviation of the regression model. a) We expect most of the sampled diamond prices to fall within $1117.56 of their least squares predicted values. b) We can explain 89.25% of the variation in the sampled diamond prices around their mean using the size of the diamond in a linear model. c) For every 1-carat increase in the size of a diamond, we estimate that the price of the diamond will increase by $1117.56. d) We expect most of the sampled diamond prices to fall within $2235.12 of their least squares predicted values.
A study by a merchant investigated the relation between the advertising costs AC (in hundreds of dollars) and the sales, Sales, also in hundred of dollars. Fifteen observations were obtained in which the AC and Sales were recorded and input into StatCrunch which produced the regression report belowSimple linear regression results:Dependent Variable: Sales Independent Variable: AC  Sales = 147.61 + 5.43 AC Sample size: 15 R (correlation coefficient) = 0.77103651 R-sq = 0.594 Estimate of error standard deviation: 49.63 Compute the predicted value Sales when AC is 35
when a regression is used as a method of predicting dependent variables from one or more independent variables. How are the independent variables different from each other yet related to the dependent variable?
The following result perspective in RapidMiner shows a multiple linear regression model. Based on the diagram, the model for our dependent variable Y is Predicted Y= (Insulation *0.420)+(Temperature *0.071)+(Avg_Age*0.065)+(Home_Size *0.311)+7.589 Attribute Insulation Temperature Avg Age Home Size (Intercept) O True O False Coefficient 3.323 -0.869 1.968 3.173 134.511 Std. Error 0.420 0.071 0.065 0.311 7.589 Std. Coefficient 0.164 -0.262 0.527 0.131 ? Tolerance 0.431 0.405 0.491 0.914 ? t-Stat 7.906 -12.222 30.217 10.210 17.725

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