The data below il exponential cune of the fom fiting 3 5 1.6 4.5 13.8 40.2125.0 3 00.0 a) Msing a svitable trans formation plot the Satter diagram diugram of the transformed dala. b) Find the least square regression une and estimate the Constants a and b. c) Obtain slope of the regression. 95% Confidence inteval for the のメ

The data below il exponential cune of the fom fiting 3 5 1.6 4.5 13.8 40.2125.0 3 00.0 a) Msing a svitable trans formation plot the Satter diagram diugram of the transformed dala. b) Find the least square regression une and estimate the Constants a and b. c) Obtain slope of the regression. 95% Confidence inteval for the のメ

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 weight and systolic blood pressure of 30 randomly selected males in the age group 25-30 areshown below. Assuming that weight and blood pressure (BP) are normally distributed.a) Find a regression line relating systolic blood pressure wo weightb) Estimate the coefficient of determination and interpret your findingsc) Test the hypothesis that p=0d) Test the hypothesis that p=0.6.e) Find the 95% CI for pf) Consider the weight and blood pressure and fit a non-intercept model to the data andcompare it to the model obtain in part (a). which model would you consider superior?Subject Weight Systolic BP Subject Weight Systolic BP1 165 130 16 172 1532 167 133 17 159 1283 180 150 18 168 1324 155 128 19 174 1495 212 151 20 183 1586 175 146 21 215 1507 190 150 22 195 1638 210 140 23 180 1569 200 148 24 143 12410 149 125 25 240 17011 158 133 26 235 16512 169 135 27 192 16013 170 150 28 187 15914 156 131 29 200 13715 185 127 30 220 140
Data on 11 randomly selected athletes was obtained concerning their cardiovascular fitness (measured by time to exhaustion running on a treadmill) and performance in a 20-km ski race. Both variables were measured in minutes and a regression analysis was performed. Ski 88 2.5 . Treadmill Coefficients Estimate Std. Error 0.41 (Intercept) Treadmill 1.11 88 -2.5 Is there sufficient evidence to conclude that there is a linear relationship between cardiovascular fitness and ski race performance? (a). The test statistic is (b). The p-value is (use four decimal places) (use four decimal places) (c). At the 2% significance level, we Select an answer the null hypothesis and conclude that there is Select an answer evidence to conclude that there is a linear relationship between cardiovascular fitness and ski race performance.
Pick one or more and look at the image below A) estimated beta (slope) for this relationship is unlikely to be zero B) r-squared value for this relationship must be negative C) 20 is a plausible value for the population mean mpg for a vehicle with 50 hp
Use the Stata output below. The data comes from students in an undergraduate economics course. The regression of interest is: final =B₁ + B₁ atndrte + ß₂hwrte + Â¸priGPA+ Â¹ ACT .reg final atndrte hwrte priGPA ACT Source Model 3094.70776 11929.9465 Residual Total final SS atndrte hwrte priGPA ACT _cons 15024.6543 df 773.676939 4 669 17.832506 MS 673 22.324895 Coef. Std. Err. .0138725 .0183476 .010863 1.906347 .3750236 .3990516 .0535332 9.225908 1.46863 Number of obs F(4, 669) Prob > F R-squared Adj R-squared Root MSE t P>|t| ||||||||||||| 674 [95% Conf. Interval] Approximate the p-value from the null hypothesis that all of the slope parameters are equal to zero.
ruan Use wage1.txt data set for this question. Consider the following regression: logtwage)-Bo-Beduc-3.exper+u. Suppose we want to test whether the return of an additional year in school will pay more than spending an additional year in the labor market Which of the following is the correct set of hypotheses for this test? Ho: 1-2 HA: 1-20 31 Ho: 1-2 HA: 1-8 270 Ho: 1-220HA: 1-³ 20 Ho: 1-2 HA: 1-820 0.91
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The amount of gas used by a typical household in a week (Q, in litres) is found to have a strongcorrelation with the price of gas (P, in $). For 6 observations, the following data was collected:Q P11.4 4.0014 3.5016 3.0018 2.5020 2.00(a) Create a regression relationship for this data(b) Perform a hypothesis test to determine whether the slope coefficient obtained in part (a) above is statistically significant(c) Use the regression equation in part (a) above to predict the quantity of gas used by a household when the price is $3.75 per litre(d) Calculate the price elasticity of demand for gas at a price of $3.75 (e) Using a further calculation, discuss how well the regression equation in part (a) above fits the data
From the results shown above, write the regression equation.
The regression output is reported in Table 4. Why does Stata omit hhsize3? Would it make any difference if Stata omitted hhsize3 instead of hhsize1?
An automobile rental company wants to predict the yearly maintenance expense (Y) for an automobile using the number of miles driven during the year () and the age of the car (, in years) at the beginning of the year. The company has gathered the data on 10 automobiles and run a regression analysis with the results shown below:. Summary measures Multiple R 0.9689 R-Square 0.9387 Adj R-Square 0.9212 StErr of Estimate 72.218 Regression coefficients Coefficient Std Err t-value p-value Constant 33.796 48.181 0.7014 0.5057 Miles Driven 0.0549 0.0191 2.8666 0.0241 Age of car 21.467 20.573 1.0434 0.3314 Use the information above to estimate the annual maintenance expense for a 10 years old car with 60,000 miles.
1:49 AA O www-awn.aleks.com : d on the same scale from a population, O REGRESSION AND CORRELATION Linear relationship and the.. y u V 1.0 3.0 1.0 7.2 11 10 10 9- 84 2.0 | 4.4 2.0 9.0 3.0 | 3.0 3.0 | 7.0 4.0 4.9 4.0 5.6 6- 5.0 4.3 5.0 8.1 4. 4- 6.0 | 7.0 6.0 4.8 2. 7.0 | 6.1 7.0 4.3 8.0 7.8 8.0 | 7.1 9.0 | 7.0 Figure 1 9.0 6.0 Figure 2 10.0 8.3 10.0 4.3 t mn w 1.0 10.0 1 10- x 9- 1.0 3.8 11 2.0 | 5.8 10- 94 2.0 9.0 3.0 | 8.1 3.0 | 8.0 7 4.0 7.0 4.0 | 9.2 6- 5. 5.0 10.3 4. 5.0 6.0 4- 3 3 6.0 9.7 6.0 5.0 2. 7.0 | 9.8 7.0 | 4.0 8.0 | 7.9 8.0 3.0 9.0 2.0 Figure 4 |10.0 1.0 9.0 | 6.6 Figure 3 10.0 3.7 Answer the following questions. The same response answer for more than one question. 1. Which data set has an apparent negative, but not perfect, linear relationship between its two variables? Choose one 2. In which data set is there evidence of a strong nonlinear relationship between the two variables? Choose one 3. Which data set indicates the strongest negative linear Choose one relationship…
Given the Analysis of Variance (ANOVA) for the simple linear regression as below: Table Q5: ANOVA Source df MS F Regression 1 252378 E Error A 5482 D Total 24 в (a) Calculate A, B, C, D and E. (b) At a = 0.05 test whether the regression model are fit.