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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A student used multiple regression analysis to study how family spending (y) is influenced by income (x) family size (x2), and addition to savings(x3). The variables y, x1, and x3. The variables y, x1, and x3 are measured in thousands of dollars . The following results were obtained. ANOVA df SS Regression 3 45.9634 Residual 11 2.6218 Total Coefficient Standard Error Intercept 0.0136 X1 0.7992 0.074 X2 0.2280 0.190 X3 -0.5796 0.920 Write out the estimated regression equation for the relationship between the variables. Compute coefficient of determination. What can you say about the strength of this relationship? Carry out a test to determine whether y is significantly related to the independent variables. Use a 5% level of significant. Carry out a test to see if X3 and y are significantly related. Use a 5% level of significance
Give an interpretation of the term “R-sq” and comment on its value.
894c3d916%#10001 MAT 213 OL1 Elementary Statistics Shayjunna Jones &| 0| 02/03/21 4:56 PM Homework: Homework 3 Save Score: 0 of 3 pts 17 of 20 (3 complete) ▼ HW Score: 10%, 6 of 60 pts 4.2.50 Question Help v Use the given data to do the following. a. Find the regression equation for the data points. b. Graph the regression equation and the data points. 4 3 y 4 -1 9 3 a. Find the regression equation for the data points. y = (Round to two decimal places as needed.) Enter your answer in the answer box and then click Check Answer. Check Answer 1 part remaining Clear Al 4:56 PM 2/3/2021 Cip
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.
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.
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
Consider the accompanying data set of dependent and independent variables a. Perform a general stopwise regression using a 0.05 for the p-value to enter and to remove independent variables from the regression model b. Perform a residual analysis for the model developed in part a to verify that the regression conditions are met Click the icon to view the data a. Use technology to perform the general stepwise regression What is the resulting regression equation? Note that the coefficient is 0 for any variable that was removed or not significant -0.69 (050), (050)+(018) - X (Round to two decimal places as needed) • Data Table: y 63 43 51 49 40 42 23 37 30 27 20 31 FR 74 63 78 3534 52 44 47 35 17 15 20 17 Print X₂ 21 259. 15 9 38 18 17 5 40 27 30 33 x₂ 22 aadosa 2NNG 29 20 17 13 17 8 15 10 10 Done 1
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.
12 A regression was run to determine if there is a relationship between hours of study per week (xx) and the final exam scores (yy).The results of the regression were: y=ax+b a=5.531 b=23.45 r2=0.378225 r=0.615 Use this to predict the final exam score of a student who studies 8.5 hours per week, and please round your answer to a whole numbe
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.