1. Consider the following data for two variables, x and y.222426303540y122133354036a. Develop an estimated regression equation for the data of the form ŷ = b, + b,x.Use the results from part (a) to test for a significant relationship between x andUse α.05.b.у.c. Develop a scatter diagram for the data. Does the scatter diagram suggest an estimatedregression equation of the form ŷ = bo + b,x + b,x²? Explain.d. Develop an estimated regression equation for the data of the form ŷ = bo + bpx +b,x².Refer to part (d). Is the relationship between x, x², and y significant? Use a = .05.Predict the value of y when x = 25.е.f.

Answered: vanat 22 24 26 30 35 40 y 12 21 33 35…

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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x = number of traffic tickets y = Cost of auto insurance A test of linear correlation between traffic tickets and insurance cost produces a linear correlation coefficient of r = 0.705. Determine weather the regression line has a positive slope, or a negative slope
Canvas -→ X C0| ® E Exhibit 6 Part of an Excel regression output relating salary for programmer (dependent variable) with three independent variables: Test score, Graduate Degree, and Experience is shown below. Coeffic. Std. Err. t Stat P-value Intercept 7.9448 7.3808 1.0764 0.2977 Test Score 1.1476 0.2976 4.8561 0.0850 Grad. 0.1969 0.0899 2.1905 0.0011 Degree Experience -2.2804 1.9866 -1.0479 0.3679 Refer to Exhibit 6. Based on the Excel output, at 95% confidence (alpha = 0.05) which of the independent variables in the output contributes significantly to explain salary? O Experience Grad Degree Intercept Test score
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Write the linear model to test the hypothesis that there is no treatment effect. Clearly describe each term in the model, and the range of the subscripts. Write the null hypothesis that you are testing. Call: lm(formula = score ~ list, data = hearing) Residuals: Min 1Q Median 3Q Max -14.7500 -5.5833 -0.2083 6.3333 16.4167 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 32.750 1.612 20.315 < 2e-16 *** listList2 -3.083 2.280 -1.352 0.17955 listList3 -7.500 2.280 -3.290 0.00142 ** listList4 -7.167 2.280 -3.144 0.00225 ** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 7.898 on 92 degrees of freedom Multiple R-squared: 0.1382, Adjusted R-squared: 0.1101 F-statistic: 4.919 on 3 and 92 DF, p-value: 0.00325

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