An HR expert at an insurance company is considering the introduction of personality tests in theprocess of recruiting sales agents. It is hypothesised that certain personality traits may contribute tobeing a successful sales agent, and therefore personality profiles can be used to select the bestapplicants for open positions. The introduction of personality measures in the recruitment process,however, is only meaningful if there is evidence that certain personality characteristics are related tojob performance. To test whether success in this occupation is related to personality features, theexpert conducts a study, in which a random sample of currently employed sales agents fill in aquestionnaire measuring three traits: (1) extraversion, (2) conscientiousness and (3) agreeableness.Each trait is measured on a scale ranging between 0 and 10 points. To test whether the annual totalvalue of insurance policies sold by an agent can be predicted from personality scores, a multiplelinear regression model is constructed, in which an agent's 12-month total sales (in thousand euros)serve as the dependent variable and the agent's scores on the three personality dimensions functionas the independent variables. The analysis is conducted in SPSS. A partial printout of the analysis isshown below.Model1RegressionResidualSum ofSquares12583.535ANOVA41471.862dfMean SquareFTotal28a. Dependent Variable: Sales (thousand euros)b. Predictors: (Constant), Agreeableness, Conscientiousness, ExtraversionSig. Model1(Constant)ExtraversionAnswer:Coefficients9.1265.5655.4793.001a. Dependent Variable: Sales (thousand euros)ConscientiousnessAgreeablenessUnstandardized CoefficientsBStd. Error23.1502.1292.5392.113StandardizedCoefficientsBeta.449.365.241t.3942.6142.1581.420Sig..697.015.041.168Calculate the estimated standard error of the regression model. (Your answer must be accuratewithin ±0.001 thousand euros).

From ANOVA table, SSregression = 12583.535 and SStotal = 41471.862 Thus, SSresidual = SStotal -…

Answered: Calculate the estimated standard error…

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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Provide an example of a research project in your hometown where you would choose to use linear regression. What variable would be your Y-score, and what variable would be your X-score? What does a regression line represent? What is the standard error of estimate?
A regression was run to determine if there is a relationship between hours of study per week (x) and the final exam scores (y). The results of the regression were: y=ax+b a=5.865 b=25.06 r²=0.81 r=0.9 Use this to predict the final exam score of a student who studies 4.5 hours per week, and please round your answer to a whole number.
A regression was run to determine if there is a relationship between hours of study per week (x) and the final exam Scores (y). The results of the regression were: y=ax+b a=6.179 b=28.96 r²=0.937024 r=0.968 Use this to predict the final exam score of a student who studies 4 hours per week, and please round your answer to a whole number.
Perform a linear regression analysis on the following data and determine the "a" coefficient (i.e., slope): Y 4.99 22.19 1.96 9.89 2.98 11 9 40.46 4.04 18.93 6.06 25 0.88 0.19 8.02 34.02 6.97 28.03
The regression line, y = 23.4 -0.07x, passes the horizontal axis at 334.3 -0.07 O -23.4 0.07 23.4 O-334.3
Using your dataset, run a regression of Y=GPA and X=# Friends.(do not need your actual data, just the regression results)a) State what this regression is attempting to analyze. “By running this regression, we areattempting to show.....”b) Write out the regression equation and describe what it shows (if Friends increase by 1, then. . . ).c) Find your hypothesized GPA when the # friends equals 17.d) Is the slope of # of Friends significantly different from zero?Include Ho, Ha, decision rule, t statistic from table, tc, decision, and conclusion.e) Is the r-squared of # of Friends significantly different from zero?Include Ho, Ha, decision rule, F statistic from table, Fc, decision, and conclusion.
A regression was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y). The results of the regression were: y = 26.699 -0.639*x R² = 0.5 r = -0.704 Predict the number of situps a person who watches 6 hours of TV can do (round your answer to three decimal places).
2. Participants were kept awake for a certain number of hours before given a visuospatial task. Researchers measured how many correct responses each participant had. Results are shown below. Use alpha = .01. Number of Correct Hours Kept Awake (X) Responses (Y) 21 X = 10 SS, = 422 Y = 10 SS, = 690 2 4 19 %3D 6. 13 SPxy =-525 ху 5 20 S, =5.70 S, = 7.29 9. 11 10 9. 14 5 15 5 17 2 18 1 17 1 13 4 18 8 11 A. Graph the data. B. State the hypotheses. C. Make a decision about the null. a. Calculate Pearson's r i. Decision about null hypothesis? b. Calculate effect size i. Interpret effect size. D. State your conclusion. E. Relate your conclusion to the research. F. Calculate the regression formula. G. If someone was kept awake for 9 hours, what is the predicted number of correct responses?
Use B = -15.812961, and ₁ = 0.096300 to determine the equation of the estimated regression line. (Round your numerical answers to four decimal places.) ŷ =
xx=02

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Calculate the estimated standard error of the regression model. (Your answer must be accurate within ±0.001 thousand euros). Answer: