Find the equation for the line of best fit, by clicking on “calculate least squares regression line.” Include this equation as well as a screenshot of the scatter plot containing the line of best fit. Two Quantitative VariablesVariableExplanatory|x axisResponse y axisBegin analysis Edit inputsScatterploty axisResidual2.4-2.2.2-1.81.6-1.4-1.2-10.80.6-0.60.50.40.30.20.10-0.1-0.2-0.3-0.4OName00•OO500 1000 1500 2000 2500 3000 3500 4000 4500x axisO500 1000 1500-0.3 -0.2 -0.1Calculate CorrelationObservations (separated by commas or spaces)Keep individuals in the same order.4734, 4376, 4068, 3300, 3153, 2842, 2460, 2389, 2376, 16151.73, 2.348, 1.487, 1.104, 1.223, 1.188, 1.204, 1.277, 1.331, |Reset everythingHide regression Hide residual plotCalculate correlation r=0.759Regression ModelsT0nCalculate least-squares regression lineEquation= 0.74494 + 0.0002x 18 0.282 0.576S²2000 2500 3000 3500 4000 4500x axis0.1ResidualHide dotplot of residuals0.20.3Calculate other regression model: [QuadraticPerform Inference0.40.50.6Include more detailed regression outputCalculate other regression modelInference procedure: [Simulate sample correlationSimulates the distribution of the sample correlation when the responses are randomly shuffled to different explanatory values.Number of samples to add: |The applet will graph all of your results until you hit "Reset simulation."Add samplesReset simulationAdjust color, rounding, and percent/proportion preferences | Back to menu

Consider the given information that shows the correlation analysis between two variables.

Find the equation for the line of best fit, by clicking on “calculate least squares regression line.” Include this equation as well as a screenshot of the scatter plot containing the line of best fit.

Find the equation for the line of best fit, by clicking on “calculate least squares regression line.” Include this equation as well as a screenshot of the scatter plot containing the line of best fit.

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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The least-squares regression equation is y=620.6x+16,624 where y is the median income and x is the percentage of 25 years and older with at least a bachelor's degree in the region. The scatter diagram indicates a linear relation between the two variables with a correlation coefficient of 0.7004. Interpret the slope.
To determine the effectiveness of group study sessions, a college instructor gathers data on hours of attendance and exam scores for students in the class. Which variable, hours of attendance or exam scores, would be the response variable for a least-squares regression equation? is it hours of attendance or exam scores?
A wildlife biologist has spent the past 5 years studying the relationship between the length (inches) and the average weight (lb) of the striped bass. His results are summarized in the table below. Length (inches) 14 18 22 26 Weight (lb) 3 |11 14 Find the least squares regression line y = ax + b for the data in the table, where x represents the length of the fish in inches and y represents the weight in pounds. Use this regression line to estimate the average weight of a striped bass that is 21 inches in length. Oy=0.75(21) - 9.5 = 6.25 lb O y = 0.65(21) - 10.5 = 3.15 lb y = 0.95(21) - 10.5 = 9.45 lb y = 0.95(21)-8.5 = 11.45 lb y = 0.85(21) 12.5 = 5.35 lb
An bus station has determined that the relationship between the number of passengers on a bus and the total weight of luggage stored in the baggage compartment can be estimated by the least squares regression equation y=115+24xy=115+24x. Predict the weight of luggage for a flight with 60 passengers. Answer: pounds Speak
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Explain how the coefficient of multiple determination, R2, is used as a descriptive measure in multiple linear regression.
Suggest a multiple regression model with the total number of wins as the response variable and the average points scored and the average relative skill as predictor variables. This regression model will help your coach predict how many games your team might win in a regular season based on metrics like the average points scored and average relative skill. This model is more practical because you expect more than one performance metric to determine the total number of wins in a regular season. Create this multiple regression model. Make the following edits to the code block below: Null Hypothesis (statistical notation and its description in words) Alternative Hypothesis (statistical notation and its description in words)
Heights (in centimeters) and weights (in kilograms) of 7 supermodels are given below. Find the regression equation, letting the first variable be the independent (x) variable, and predict the weight of a supermodel who is 167 cm tall. \begin{array}{c|ccccccc} \mbox{Height} & 178 & 176 & 166 & 178 & 174 & 172 & 174 \cr \hline \mbox{Weight} & 58 & 54 & 47 & 57 & 55 & 53 & 54 \cr \end{array} The regression equation is \hat{y} = + x . The best predicted weight of a supermodel who is 167 cm tall is .
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Which equation is the best fit for the data in the table? X Y 3 12 5 27 8 48 11 O y=-.0026x^2+.33x+1.16 O y=1.94x+2.140 O y=1.732vx-1 O y=1.732x-1
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.)