Given the following partial ANOVA based on a sample of 15 observations :dfSSofRegression2138Residual12Total14916Calculate the coefficient of determination r2:Select one:O a. 0.15Ob. 0.85Oc. 0.13Od. 6.64目1JUN1112stvT

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Answered: Given the following partial ANOVA based…

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 following is the ANOVA obtained from a regression analysis: ANOVA Source of Variation REGRESSION ERROR TOTAL Degree of Freedom 1 104 Sum of Squares 14.6 440.9 Mean Squares F-calculated === Your are interested to evaluate the following hypothesis: Ho: B 1 B 2 ---against--- HA: B1 B2 1. How many observation were used in this experiment? (place your answer in the box below) 2. Evaluate the F calculated for the regression, and indicate if you are going to reject(R) or fail to reject(R) the null hypothesis using an a= 0.01 (R/FR; place your answer right beside the calculated number of treatments)
Note : please solve within 40 minutes.Clear explaination is required. Consider the following ANOVA table for a multiple regression model. Source df SS MS F Regression 4 3000 750 5 Residual 35 5250 150 Total 39 8250 significance level = 0.05 Then find : a)What is the size of this sample? b) Calculate the adjusted multiple coefficients of determination. What's the easiest way to do this in excel? Thank you.
7. Consider the following ANOVA table for a multiple regression model: Source df SS MS F Regression 118.8475 59.42375 40.92168993 Residual 13.0692 1.452133333 Total 11 131.9167 a) Complete the remaining entries in the table. e) Test the significance of the overall regression model using a=0.05.
Consider the following computer output of a multiple regression analysis relating annual salary to years of education and years of work experience. Regression Statistics Multiple R 0.73360.7336 R Square 0.53810.5381 Adjusted R Square 0.51800.5180 Standard Error 2140.27632140.2763 Observations 49 ANOVA dfdf SSSS MSMS F� Significance F� Regression 22 245,430,999.7671245,430,999.7671 122,715,499.8836122,715,499.8836 26.789226.7892 1.9E-081.9E-08 Residual 4646 210,716,007.0084210,716,007.0084 4,580,782.76114,580,782.7611 Total 4848 456,147,006.7755456,147,006.7755 Coefficients Standard Error t� Stat P-value Lower 95%95% Upper 95%95% Intercept 14276.146814276.1468 2,531.84252,531.8425 5.63865.6386 0.0000010040.000001004 9179.81229179.8122 19,372.481419,372.4814 Education (Years) 2349.95952349.9595 338.5500338.5500 6.94126.9412 0.0000000110.000000011 1668.49371668.4937 3031.42533031.4253 Experience (Years) 833.6183833.6183…
Consider the following Stata regression output (some values are deliberately removed). Variable | Obs Mean Std. Dev. Min Маx lwage points | rebounds | assists 269 6.952296 .8813761 5.010635 8.655214 269 10.21041 5.900667 1.2 29.8 269 4.401115 2.892573 2.092986 .5 17.3 269 269 2.408922 1682.193 12.6 3533 minutes | 893.3278 33 Source | SS df MS Number of obs F(, Prob > F Model | Residual | = R-squared Adj R-squared Root MSE %3D 0.4146 Total | lwage | Сoef. Std. Err. P>|t| [95% Conf. Interval .0795364 points rebounds .0277761 .0637763 .0204514 3.12 0.002 0.252 0.227 -.0230647 -.0000747 .087425 .0003133 assists .0321805 .0280576 1.15 minutes .0001193 .0000985 1.21 _cons Answer the following questions. Please round your answers to 2 decimal places. Model SS : ; Residual S : ; Total SS :
J 1
A study was conducted to examine how fear of public speaking varies across year of tertiary education (1st, 2nd or 3rd). Subjects were interviewed at the end of each of year of their tertiary education for fear of public speaking (measured on a 10 point metric scale). Which of the following would be an appropriate statistical test to conduct, which addresses these hypotheses? Group of answer choices: Factorial ANOVA Mixed ANOVA Multiple Regression Within Subjects ANOVA Single Factor ANOVA
Below you are given a partial Excel output based on a sample of 16 observations. ANOVA df SS MS F Regression 4,853 2,426.5 Residual 485.3 [row intentionally left blank] Coefficients Standard Error Intercept 12.924 4.425 x1 -3.682 2.630 x2 45.216 12.560 Refer to Exhibit 3. The test statistic used to determine if there is a relationship among the variables equals: Select one: a. .2 b. -1.4 c. .77 d. 5
32.
Our environment is very sensitive to the amount of ozone in the upper atmosphere. The level of ozone normally found is 4.6 parts/million (ppm). A researcher believes that the current ozone level is at an excess level. The mean of 14 samples is 4.9 ppm with a variance of 1.2 Does the data support the claim at the 0.01 level? Assume the population distribution is approximately normal. Step 2 of 5 : Find the value of the test statistic. Round your answer to three decimal places.
Let's study the relationship between brand, camera resolution, and internal storage capacity on the price of smartphones. Use α = .05 to perform a regression analysis of the Smartphones01CS dataset, and then answer the following questions. When you copy and paste output from MegaStat to answer a question, remember to choose to "Keep Formatting" to paste the text. a. Did you find any evidence of multicollinearity and variance inflation among the predictors. Explain your answer using a VIF analysis. b. Copy and paste the normal probability plot for your analysis. Is there any evidence that the errors are not normally distributed? Explain. c. Copy and paste the Residuals vs. Predicted Y-values. Does the pattern support the null hypothesis of constant variance for the errors? Explain. d. Study the residuals analysis. Which observations, if any, have unusual residuals? e. Study the residuals analysis. Calculate the leverage statistic. Which observations, if any, are high leverage…
Refer to the below table. Using an alpha = 0.05, test the claim that IQ scores are the same for children in three different blood lead level groups: low lead level, medium lead level, and high lead level). One-Way Analysis of Variance Summary Table for IQ Measurements for Children among Three Blood Lead Level Groups: Low Lead Level, Medium Lead Level, and High Lead Level. Source df SS MS F p Between-group (treatment) 2 469.1827 2677.864 2.30 0.104 Within-group (error) 118 203.6918 11745.05 Total 120

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Given the following partial ANOVA based on a sample of 15 observations: df SS of Regression 138 Residual 12 Total 14 916 Calculate the coefficient of determination r2: Select one: O a. 0.15 Ob. 0.85 Oc. 0.13 Od. 6.64