Let X be a continuous rv with the following cdf.F(x) =1+ In0 < x < 71X > 7[This type of cdf is suggested in the article "Variability in Measured Bedload-Transport Rates"† as a model for a certain hydrologic variable.](a) What is P(X < 5)? (Round your answer to three decimal places.)(b) What is P(5 < X < 6)? (Round your answer to three decimal places.)(c) What is the pdf of X?

Answered: Image /qna-images/answer/f1be90e7-dd06-4cff-990d-d77cf8d4fe33.jpg

Answered: F(x) = + In 0 < xs7 x > 7 [This type of…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

See similar textbooks

Similar questions

The lecturer is interested to see what variables, if any will help determine the number of hours spent on studying statistics. Test and find any regression models that can help determine the number of hours spent on studying. (Hint run Study hrs. on…)(a) On average, how much do males study more(less) than females?(b) Do people who enjoy the Big Bang Theory impact on the number of hours studies?(c) Is age a determining factor on the number of hours studied?(d) Do students with a stronger math background study more or less? Also state how much more or less Answer all questions.. Quiz Results EQR Study Hrs Age Sex BBT MB MC AuHS LM 15 10 3 19 0 0 1 1 0 1 14 15 4 24 0 0 1 0 0 1 9 15 1 20 0 10 1 0 0 1 6 10 3 21 0 0 1 1 0 1 14 15 4 21 0 9 1 0 0 1 12 10 6 21 0 2 0 1 0 1 12 13 2 21 1 8 1 0 0 0 15 15 0 20 0 8 1 0 0 1 12 15 3 20 0 10 1 0 0 1 13 15 0.2 19 0 8 1 0 0 1 15 15 2 20 0 6 1 0 1 1 12 14 5 20 0 5 1 1 1 1 14 15 7 22 0 8 0 0 0 0 7 7 10 21 1 7 0 0 1 0 11 15…
1. You estimated a regression with the following output. Source | SS df MS Number of obs = 210 -------------+---------------------------------- F(1, 208) = 940.28 Model | 5529353.01 1 5529353.01 Prob > F = 0.0000 Residual | 1223155.7 208 5880.55624 R-squared = 0.8189 -------------+---------------------------------- Adj R-squared = 0.8180 Total | 6752508.71 209 32308.6541 Root MSE = 76.685 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 13.66711 .4457062 30.66 0.000 12.78843 14.54579 _cons | 103.7139 41.86814 2.48 0.014 21.17362 186.2542…
Q1: The data points below are related to a chemi-thermo-mechanical pulp from mixed density hardwoods. They relate Y (specific surface area of the fibres in cm/g) to the % NaOH (sodium hydroxide) used as a pretreatment chemical and the treatment time (min) for different batches of pulp. The variables are present at three different levels. In this case, it is preferred (for reasons that we will discuss later in the course) to code the levels as shown in the last two columns of the table below, designated by Xı and X2. Y SODIUM ΤΙME Xi X2 HYDROXIDE 5.95 3 30 -1 5.60 3 60 -1 5.44 3 90 -1 1 6.22 9. 30 -1 5.85 9 60 5.61 9. 90 1 8.36 15 30 1 -1 7.30 15 60 1 6.43 15 90 1 1 a. Using the variables Y, X1 and X2 (not actual time and sodium hydroxide! You will see why later!), fit the following multiple linear regression model to the data: (Model A) Y = (b0) + (b1) X1 + (b2) X2; subsequently, estimate the parameters and examine the residual plot (residuals vs Y hat). What does this residual plot…
Table 8: Regression of Weight Loss on Hours of Exercise and Caloric Intalk Variable se b 95% CI Hours of Exercise 0.27 0.11 0.05, 0.49 Caloric Intake -0.002 .0008 -0.0035, -0.0004 Intercept 8.50 0.73 7.05, 9.95 Note: R? = .35, adj. R? = .35, F = 22.18, df = 2,117; n = 120 %3D swer the following questions about this regression analysis. Which predictors of weight loss are statistically significant, if either? What is the predicted weight loss for someone who consumes 2,000 calori hour per day on average? What is the literal interpretation for the slope of hours of aerobic exercise
You estimated a regression with the following output. Source | SS df MS Number of obs = 248 -------------+---------------------------------- F(1, 246) > 99999.00 Model | 1.2864e+09 1 1.2864e+09 Prob > F = 0.0000 Residual | 980713.166 246 3986.63889 R-squared = 0.9992 -------------+---------------------------------- Adj R-squared = 0.9992 Total | 1.2874e+09 247 5212005.66 Root MSE = 63.14 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 38.34444 .0675026 568.04 0.000 38.21148 38.4774 _cons | 49.99803 5.986441 8.35 0.000 38.20681 61.78925…
3. A study of beer consumption using the annual data from 1980 -2001 produced the following regression: Y = 0.41 +0.052.X₁; −0.047X2; +0.032X3; −0.018X4i (0.027) (0.011) (0.009) (0.008) (0.009) R² = 0.94 F = 66.58 DW statistic=1.32, figures in the brackets are standard errors Where: Y₁ = Annual aggregate beer consumption in year t (billion pints) X₁₁ = Real disposable income income in year t ($ billions at 2001 prices) X2i = Price of beer in year t (index number with 2001 = 100) X31 Price of wine and sprits in year t (index number with 2001 = 100) = X4₁ = Price of cigarettes in year t (index number 2001 = 100) (a) What is the effect of a change in the price of beer on beer consumption? Does it have the correct sign? Explain. (b) Comment on the signs of the other three coefficients. Are they as expected? (c) Tests the significance of the coefficients on each of the variables Xit i = 1, 2, 3, 4. What assumptions have you made in carrying out these tests? (d) What conclusion do you draw…
3. Suppose that the following import function for Turkey is estimated for Turkey between 1980-2015. Import, a, + a,GDP; + ażER¸ + ut In order to measure the impact of 2001 crisis the regression is estimated based on the whole and two subsamples and the following RSS are obtained. Time period: 1980-2000 , RSS1= 69 Time period: 2001-2015, RSS2 =35 Time period: 1980-2015 , RSS = 160 Carry out the Chow test whether the regressions for the two periods are different at 5% significance level. (35 P)
Question 38. You have performed a linear regression analysis to explore sunflowers' growth (in meters per month) depending on the watering (in litres per day). You have estimated the regression coefficient to be ß = 1.6. What can you conclude? a) There is a signifleant correlation between watering and growth. b) An average sunflower growths 1.6 meters per month. c) If you give it an additional litre of water per day, there will be an additional average growth of 1.6 meters per month. d) According to the model assumptions, an additional litre of water per day will result in additional 19.2 meters of growth after one year. e) You should consider further influencing quantities.
The estimated regression equation for a model involving two independent variables and 10 observations follows. ý = 22.1370 + 0.5303Xq + 0.4920X2 (a) Interpret b₁ in this estimated regression equation. O b₁ = 0.5303 is an estimate of the change in y corresponding to a 1 unit change in x₂ when x₁ is held constant. O b₁ = 0.5303 is an estimate of the change in y corresponding to a 1 unit change in x₁ when X₂ is held constant. O b₁ = 22.1370 is an estimate of the change in y corresponding to a 1 unit change in x₁ when x₂ is held constant. O b₁ = 0.4920 is an estimate of the change in y corresponding to a 1 unit change in x₂ when x₁ is held constant. O b₂ = 0.4920 is an estimate of the change in y corresponding to a 1 unit change in x₁ when x₂ is held constant. Interpret b₂ in this estimated regression equation. O b₂ = 0.4920 is an estimate of the change in y corresponding to a 1 unit change in x₂ when x₁ is held constant. O b₂ = 22.1370 is an estimate of the change in y corresponding to a…
2. You estimated a regression with the following output. Source | SS df MS Number of obs = 332 -------------+---------------------------------- F(1, 330) = 2.32 Model | 71599.1822 1 71599.1822 Prob > F = 0.1284 Residual | 10170207.7 330 30818.8111 R-squared = 0.0070 -------------+---------------------------------- Adj R-squared = 0.0040 Total | 10241806.9 331 30942.0147 Root MSE = 175.55 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | .7575183 .4969893 1.52 0.128 -.2201484 1.735185 _cons | 28.87757 42.80371 0.67 0.500 -55.32498 113.0801…
Regression (1) in Table 1 estimates the effect of additional educationby regressing ln (W age) on Exp, Exp2, W ks, Ed and all the dummyvariables listed above. Does the estimated regression suggest that additional years of schooling increases the wage?
4. (2) Using data from 1950 to 1996 (N = 47 observations) the following equation for explaining wheat yield in the Mullewa Shire of Western Australia was estimated as YIELD, = 0.1717+0.01117t+0.05238Rain, (se) (0.1537) (0.00262) (0.01367) ... Where YIELD, is wheat yield in tonnes per hectare in year t; t =1, 2, . 47 is a trend variable to capture technolo change, and RAIN, is total rainfall in inches from May to October (the growing season) in year t. a) Interpret the coefficient estimates of t and Rain. b) Using a 5% significance level, test the null hypothesis that technological changes increase mean yield by 0.01 tonnes per hectare against the one-tailed alternative H₁ : ₂ >0.01. c) Using a 5% significance level, test the null hypothesis that an extra inch of rainfall increases mean yield by 0.03 tonnes per hectare against the one-tailed alternative H₁ : B3 > 0.03. d) Adding the square of rainfall to the equation yields YIELD, = -0.6759+0.011671t+0.2229 Rain, -0.008155 Rain? (se)…

SEE MORE QUESTIONS

Recommended textbooks for you

MATLAB: An Introduction with Applications

Statistics

ISBN:

9781119256830

Author:

Amos Gilat

Publisher:

John Wiley & Sons Inc

Probability and Statistics for Engineering and th…

Statistics

ISBN:

9781305251809

Author:

Jay L. Devore

Publisher:

Cengage Learning

Statistics for The Behavioral Sciences (MindTap C…

Statistics

ISBN:

9781305504912

Author:

Frederick J Gravetter, Larry B. Wallnau

Publisher:

Cengage Learning

MATLAB: An Introduction with Applications

Statistics

ISBN:

9781119256830

Author:

Amos Gilat

Publisher:

John Wiley & Sons Inc

Probability and Statistics for Engineering and th…

Statistics

ISBN:

9781305251809

Author:

Jay L. Devore

Publisher:

Cengage Learning

Statistics for The Behavioral Sciences (MindTap C…

Statistics

ISBN:

9781305504912

Author:

Frederick J Gravetter, Larry B. Wallnau

Publisher:

Cengage Learning

Elementary Statistics: Picturing the World (7th E…

Statistics

ISBN:

9780134683416

Author:

Ron Larson, Betsy Farber

Publisher:

PEARSON

The Basic Practice of Statistics

Statistics

ISBN:

9781319042578

Author:

David S. Moore, William I. Notz, Michael A. Fligner

Publisher:

W. H. Freeman

Introduction to the Practice of Statistics

Statistics

ISBN:

9781319013387

Author:

David S. Moore, George P. McCabe, Bruce A. Craig

Publisher:

W. H. Freeman

SEE MORE TEXTBOOKS