The graph below displays the heights of children from age 5-15. Why shouldn't we use the regressionline shown to predict a child height of age 13?The shape is non-linear, so we should not use a line to predict values.An outlier is skewing our regression line, making it inaccurate.13 is not in our data's range/domain so this would be extrapolation.We could use the line to accurately predict height.

The regression equation line is using for prediction and it estimates the association between two…

Answered: The graph below displays the heights of…

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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1. Develop a simple linear regression equation for starting salaries using an independent variable that has the closest relationship with the salaries. Explain how you chose this variable. 2. Present the simple lincar regression equation, identify and explain the cocfficient of determination, intercept and regression coefficient, and significance of F-test. Based on this analysis, is your regression equation "good for use"? Explain. 3. Provide a numeric example of how this regression equation may be used to predict students starting salaries. 4. Develop a multiple regression equation for starting salaries using School Ranking, GPA, and Experience as independent variables. Is this regression equation "good for use? Explain. 5. If the multiple regression equation in the previous question is not "good for use", how would you suggest improving this multiple regression equation? Present the improved multiple regression equation. Give a numeric example of how this improved multiple…
(4) Does how wide a possum's belly is (in cm) tell you how long a possum's tail is (in cm)? That's what re- searchers in Australia wanted to know. They built a simple linear regression model treating X as the possum's belly girth and Y as the possum's tail length. Below and at the top of the next page are all the results you will need to answer the ensuing questions, including: ● The straight line of best fit: ŷ = 30.2 + .21x • The correlation between the observed y and predicted ŷ: ry,ŷ .294 • At the top of the next page are three residual plots: (a) the plot of belly girth vs. the residuals, (b) the plot of the residuals in order of collection, (c) the histogram of the residuals = These are real data (source) consisting of measurements on each of 104 mountain brushtail possums, trapped at seven sites from Southern Victoria to central Queensland, Australia.
A Realtor in Massachusetts is analyzing the relationship between the sale price of a home (in $), it's the square footage (in ft2), the number of bedrooms, the number of bathrooms, and a colonial dummy variable (colonial equals 1 if a colonial-style home; 0 otherwise). The relater collects data of 36 sales in Massachusetts for analysis. The attached file “quiz_dataset1” contains the data. Build a parsimonious linear regression model. Use the model to predict the price of a 2100 ft2 colonial style home with two bedrooms and two bathrooms. Select one: a. 570268.3 b. 554347.1 c. 535347.1 d. None of the above
please assist, the question states to "click on the datafile logo to reference the data" but the data is listed already in the question. when clicking that link, it opens up the same information in excel so please do not reject because the data is there already.
Consider the following image of the data. Is the linear trend line a reasonable fit to the data points? Is it linear? +H R²=0.9467 No. The dashed line is well outside many of the error bars. No. The R^2 value is less than 1. Yes. The R^2 value is approximately 1. Yes. The data points have error bars and the line goes through them.
A negative correlation between variables X and Y will always result in a positive slope in the linear regression model. Cannot tell from the given information. False True
A rural state wants to encourage high school graduates to continue their education and attend college. The state collected information on a random sample of high school seniors from across the state 7 years ago and is now observing how many years of education they completed. They believe students decide to achieve more education when they are more capable, have easier access to college education, and the opportunity cost of attending are lower. To explore the factors that affect the years of education completed they have used multiple regression to estimate the years of completed education as a function of: Unemployment rate - the unemployment rate in the county (3.9 – 16.8) County Hr Wage - average starting hourly manufacturing wage in the county Test - student score on college admission test (0 to 100 scale) Dist to college - Distance to near college (measured in 100’s of miles) Tuition - Tuition charged at nearest state university (measured in $1000s)…
Canada's trade balance with the rest of the world has been steadily increasing over the past several years. It is well known that the U.S. is Canada's leading trade partner. Is there a predictable linear relationship between our total trade balance and our trade balance with the U.S.? Develop a regression model to predict the trade balance with the U.S. by our trade balance with the rest of the world. Comment on the strength of the model. Develop a time-series trend line for trade balance with the rest of the world by using the time periods given. Forecast total trade balance with the rest of the world for 2020 using this equation. Trade Balance ($ millions) Year Rest of the World U.S. 2010 2,290 4,991 2011 2,486 4,709 2012 2,776 4,641 2013 2,648 5,220 2014 3,068 5,041 2015 3,688 4,750 2016 4,111 4,321 2017 4,172 4,699 2018 3,669 4,777 2019 3,493 4,809 question 1: y̅ = ?+?x question…
Suppose there is a significant correlation between variables. describe 2 instances in which it might be inappropriate to use the linear regression model for prediction
Please assist not sure of my anwer. It’s statistics.
The following regression equation is based on the analysis of four variables: SM_DOLLARS is the dollar amount of a watershed conservation agency's weekly spending on social media ads. RADIO_ADS is the number of radio advertisements aired weekly by the agency. WS_DOLLARS is the dollar amount of the agency’s weekly spending on web search ads. The variable WEB_VISITS is the number of weekly visitors to their educational website. These data have been recorded every week for the past three years. WEB_VISITS (expected) = 109 + 0.2*SM_DOLLARS + 1.5*RADIO_ADS + 1.1*WS_DOLLARS The data meet the assumptions for regression analysis, and the regression results, including the coefficients, were found to be statistically significant. How many additional weekly web visits would you predict when the agency increases its weekly spending on social media ads by $150 without changing the amount spent on radio ads or web search ads? (Round your answer to the nearest whole number.)
The following regression equation is based on the analysis of four variables: SM_DOLLARS is the dollar amount of a watershed conservation agency's weekly spending on social media ads. RADIO_ADS is the number of radio advertisements aired weekly by the agency. WS_DOLLARS is the dollar amount of the agency’s weekly spending on web search ads. The variable WEB_VISITS is the number of weekly visitors to their educational website. These data have been recorded every week for the past three years. WEB_VISITS (expected) = 208 + 1.5*SM_DOLLARS + 0.5*RADIO_ADS + 0.8*WS_DOLLARS The data meet the assumptions for regression analysis, and the regression results, including the coefficients, were found to be statistically significant. How many additional weekly web visits would you predict when the agency increases its weekly spending on social media ads by $108 without changing the amount spent on radio ads or web search ads? (Round your answer to the nearest whole number.)

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