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the k=5, te the Cnd the Following regression Problem with, n= 54, Included statistic For Four results 5 =4,0 ases Cases ei 1000 1,T32 9,000 0,9o00 50 ד 0 0,2500 O 2500 Compute Di Influential cases Lotlvential 234

the k=5, te the Cnd the Following regression Problem with, n= 54, Included statistic For Four results 5 =4,0 ases Cases ei 1000 1,T32 9,000 0,9o00 50 ד 0 0,2500 O 2500 Compute Di Influential cases Lotlvential 234

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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You may need to use the appropriate technology to answer this question. Following is a portion of the computer output for a regression analysis relating y = maintenance expense (dollars per month) to x = usage (hours per week) of a particular brand of computer termina Analysis of Variance SOURCE Regression Error Total Predictor Constant X DF Adj SS 1 1575.76 8 349.14 9 1924.90 Regression Equation Y = 6.1092 +0.8951 X O Ho: B₁ * 0 H₂: B₁ = 0 Coef SE Coef 0.9361 0.1490 (a) Write the estimated regression equation. ý =| 6.1092+ 0.8951r O Ho: B₁ 20 H₂: B₁ <0 |0 Ho: Boo Hà Bo=0 |0 Ho: Bo=0 = 0 Ha: Bo #0 6.1092 0.8951 Ho: B₁ = 0 H₂: B₁ * 0 (b) Use a t test to determine whether monthly maintenance expense (dollars per month) is related to usage (hours per week) at the 0.05 level of significance. State the null and alternative hypotheses. Adj MS 1575.76 43.64 Find the value of the test statistic. (Round your answer to two decimal places.) 36.11 X
I need F please Asap plz Part F only
Big Time Corporation wanted to determine the relationship between its monthly operating costs and a potential cost driver, machine hours. The output of a regression analysis showed the following information (note: only a portion of the regression analysis results is presented here) SUMMARY OUTPUT Regression Statistics Multiple R 0.998041188 R Square 0.996086212 Adjusted R Square 0.994781616 Standard Error 917.1714273 Observations 5 ANOVA df SS MS F Significance F Regression 1 642276389.7 642276389.7 763.5208906 0.000104039 Residual 3 2523610.281 841203 427 Total 4 644800000 Coefficients Standard Errort Stat P-value Intercept 6679.617454 931.8558049 7.168080532 0.005592948 3714.03639 X Variable 9.796772265 0.354545968 27.63188178 0.000104039 8.66844876 What is variable cost per machine hour (rounded to the nearest cent)? OA. $6,679.62 OB. $917.17 OC. $9.80 OD. $.99 Lower 95% Upper 95.0% 9645.198517 10.92509577
eBook Given are five observations for two variables, z and y. 15 13 17 19 51 58 50 23 16 The estimated regression equation for these data is y= 62.15 - 2.05z. a. Compute SSE, SST, and SSR. %3D SSE (to 2 decimals) SST (to 2 decimals) SSR (to 2 decimais) b. Compute the coefficient of determination r, Comment on the goodness of fit. (to 3 decimals) The least squares line provided an Select your answer it; % of the variability in y has been explained by the estimated regression equation (to 1 decimal). c. Compute the sample correlation coefficient. Enter negative value as negative number. (to 3 decimals) 0 Jcon Key 4 Question 2 of
Take a look at the scatterplot below with the corresponding best fit regression line. My grumpiness (0-100) 8 2 9 T 5 6 True False 7 My sleep (hours) A reasonable estimate of the "R" value for the scatterplot is approximately R = 0.03. Time (min.) 0 2 4 6 Distance (ft.) 0 6 12 18 Do you think it would be appropriate to use interpolation to find the distance at 7 or 9 minutes? Yes, they fall within the data points we have. Yes, they fall outside of the data points we have. No, they fall within the data points we have. No, they fall outside of the data points we have.
Using the regression model from the previous problem, what is the interpretation of the slope coefficient associated with Age? Assume Salary is measured in $1,000's.
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10.2-7 Question Help Use the given data to find the equation of the regression line. Round the final values to three significant digits, if necessary. 1 3 7 9. y 143 116 100 98 90 O A. V = 140.4 – 6.2x OB. y = - 150.7 + 6.8x O C. y = - 140.4 + 6.2x O D. V = 150.7-6.8x
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a. b. Shown below is a portion of a computer output for a regression analysis relating y (dependent variable) and x (independent variable). ANOVA Regression Residual Total Intercept Variable x df 1 13 14 Coefficients 16.156 -0.903 SS 50.58 55.42 106.00 Standard Error 1.42 0.26 1 Stat Perform a t test and determine whether y and x are related. Use a= .05. Compute the coefficient of determination and fully interpret the meaning. Be specific.
Are the heights of individuals affected by the heights of their parents. Regression Statistics Multiple R R Square Adjusted R Squar 0.631071992 0.398251859 0.365724932 Standard Error 2.914527039 Observations 40 ANOVA df MS F Significance F Regression Residual 2 208.0084392 104.0042 12.24376 8.30181E-05 37 314.2953108 8.494468 Total 39 522.30375 Coefficients Standard Error t Stat P-value Intercept Mother's Height Father's Height 9.804326378 12.39987353 0.79068 0.43417 0.657952815 0.147476295 4.461414 7.34E-05 0.200358437 0.138223638 1.449524 0.155615 1. Write the regression equation that represents the above equation. 2. Is this a good predictor equation? Why or why not (use appropriate statistics/hypothesis test to prove your point)? 3. Use the equation to predict the height of someone whose mother is 52 inches tall and whose father is 70 inches tall.