Looking at the data for Pearson's Correlation, which one has the strongest and which one is the lowest? - RegressionVariables Entered/RemovedaVariablesEnteredVariablesRemovedModelMethodpoliticalpartyrepublican,crimerate,povertyrate,raceEntera Dependent Variable: incarcerationb. All requested variables entered.Model SummaryAdjusted RSquareStd. Error ofModelR.R Squarethe Estimate759576539134.39297a. Predictors (Constant), politicalpartyrepublican, crimerate,povertyrate, raceANOVASum ofModelSquaresdfMean SquareSig.Regression1106091.869276522.967415.310000Residual812766.1314518061.470Total1918858.00049a. Dependent Variable: incarcerationb. Predictors: (Constant), politicalpartyrepublican, crimerate, povertyrate, raceCoefficientsStandardizedUnstandardized CoefficientsCoefficientsModelStd. ErrorBetat.Sig.1(Constant)-253.584156.001-1.626.111povertyrate8669-.5355.671.3973.526.001race1.579-.043-.339.736crimerate.468.183.3052.555.014politicalpartyrepublican10.8512.934a. Dependent Variable: incarceration.4113.698.001ch /VARIABLES=incarceration povertyrate race politicalpartyrepublican crimeratĐISON TIVLOMI=LNIHd/Correlations[DataSet0]CorrelationspoliticalpartyrepublicanincarcerationpovertyrateIncarceration'Pearson Corelation603-1521.00050Sig (2-tailed)000N.Pearson Corelation09povertyrate-311444E091.Sig (2-tailed)00009.394Pearson Correlation-152-3111.Sig. (2-tailed)067000*09politicalpartyrepublicanPearson Correlation1.Sig. (2-tailed)001337005crimeratePearson Correlation444-.0300669851.Sig. (2-tailed)001836000000Correlation is significant at the 0.01 level (2-tailed).* Correlation is significant at the 0.05 level (2-tailed).REGRESSIONASIMISIT ONISSIN//STATISTICS COEFE OUIS R ANOVA/NOORIGINVDEPENDENT incarceration/METHOD=ENTER povertyrate race crimerate politicalpartyrepublican.RegressionVariables Entered/Removed"

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Answered: Looking at the data for Pearson's…

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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A personality psychologist was interested in the relationship between height and income. They found there was a relationship and the effect size was: r = 0.08 in correlational units. Calculate a binomial effect size display(BESD) and then tell me what percentage of people who are tall (like me) you would predict to make more money than the average person. The correlation was randomly generated by eClass, so don't start daydreaming about how much or little you or I will make. Answer:
Which of the following is the strongest correlation? -0.90 +0.50 +0.95 -0.99
ab! ourses The accompanying technology output was obtained by using the paired data consisting of foot lengths (cm) and heights (cm) of a sample of 40 people. Along with the paired sample data, the technology was also given a foot length of 13.6 cm to be used for predicting height. The technology found that there is a linear correlation between height and foot length. If someone has a foot length of 13.6 cm, what is the single value that is the best predicted height for that person? "se Hom E Click the icon to view the technology output. abus The single value that is the best predicted height is cm. endar (Round to the nearest whole number as needed.) entation ktbook a-F on unouncem ssignment: cudy Plan radebook Hydrogen Report.pdf Enter your answer in the answer box. Screen Shot 20-11...9.53 P 5,981 10 tv MacBook Pro (5
What is the formula for calculating Pearson's correlation coefficient?
Which of the following best describes the Pearson correlation for these data? X Y 2 5 5 1 3 4 4 2
Respond to the following questions about Pearson’s correlation. Which is a stronger correlation, r = 0.48 or r = -0.56? Explain.
Would A be the answer?
In running a Pearson correlation, you notice that there are a few outliers. Please describe the next course of action, if any, for the dataset and explain your reasoning.
Which of the following best describes the Pearson correlation for these data?
This is an exercise. What is Pearson’s correlation coefficient? And how to use it?
An exercise scientist wants to know if there is a relationship between explosive leg power and standing jump height. To determine if a relationship exists, a group of 30 participants' (N = 30) Leg power was measured by recording each participants' single squat weight max (in pounds). Next, each participant was asked to perform a standing jump and their Jump height was recorded (in meters). Using the variables “Leg power” and “Jump height”, conduct a Pearson's Correlation, at α = 0.05, to determine if leg power and jump height are correlated. Identify the correct null hypothesis A. H0: There is no relationship between leg power and jump height B. H0: There is a relationship between leg power and jump height C. HA: There is a relationship between leg power and jump height D. HA: There is no relationship between leg power and jump height
An exercise scientist wants to know if there is a relationship between explosive leg power and standing jump height. To determine if a relationship exists, a group of 30 participants' (N = 30) Leg power was measured by recording each participants' single squat weight max (in pounds). Next, each participant was asked to perform a standing jump and their Jump height was recorded (in meters). Using the variables “Leg power” and “Jump height”, conduct a Pearson's Correlation, at α = 0.05, to determine if leg power and jump height are correlated. What does this type of correlation indicate about how leg strength and jump height change together? A. Both leg strength and jump height increase and decrease together B. There is no relationship between leg strength and jump height C. As leg strength increases, jump height decreases D. As jump height increases, leg strength decreases

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