Aa -21 TAaBbCcDd AaBbCcDd AaBbC AaBbCcl Aaaly AI Normal 1 No Spac. Heading 1Heading 2TitleParagraphStyles1134. 15.168.19. 1 10. I11 12 131415.1 16. 17181. The following are data on the observed compressivestrength in psi of 50 samples of concrete interlockingblocks.136 92 115 118 121 137 132 120 104 125119 115 101 129127 103 110 126 118 82 104 137 12087 108 110 133 135 12695146 126 110 119 105 132 126 118 100 113106 125 117 102 146 129 124 11395148Determine the following:1. Range2. Class Interval3. Class limits4. Frequency5. Relative Frequency* Accessibility: Investigate28°C近

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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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Consider the following regression equation specied for 2-period panel data: where i = 1; 2; :::N and t = 1; 2. If you expect that β_1 is positive, but the correlation between Δx_i and Δu_i is negative, thenwhat is the bias in the OLS estimator of β_1 in the first-differenced equation?
The Road Department is trying to see whether they should buy road treatments ( in tons) for storms based on the number of inches of snow for each recorded. Use Pearson r at alpha- 0.05 to test the hypothesis. ILLUSTRATE THE NORMAL CURVE inches in snow 1.5 1.7 3.7 2.8 4.6 2.4 3.1 2.9 3.6 4.2 3.1 number of tons 805 905 1235 1000 1302 998 1102 1305 1456 1600 1005
1. Model 1: OLS, using observations 1-706 Dependent variable: RST Coefficient Std. Error t-ratio p-value const 3586.38 38.9124 92.17 <0.0001 *** TOTWRK −0.150746 0.0167403 −9.005 <0.0001 *** Mean dependent var 3266.356 S.D. dependent var 444.4134 Sum squared resid 1.25e+08 S.E. of regression 421.1357 R-squared 0.103287 Adjusted R-squared 0.102014 F(1, 704) 81.08987 P-value(F) 1.99e-1810538.19 Log-likelihood −5267.096 Akaike criterion 10538.19 Schwarz criterion 10547.31 Hannan-Quinn 10541.71 RSTi =3586.38−0.150746 x TOTWRKi , R2=0.103287,SER=421.1357 (38.9124) (0.0167403) Question? could you please help with this question below. 3) By observing the GRETL output in Part (1) above, provide a Detailed explanation of the coefficient of determination. Based on your analysis, is this a good model? Why or why not?
1. Model 1: OLS, using observations 1-706 Dependent variable: RST Coefficient Std. Error t-ratio p-value const 3586.38 38.9124 92.17 <0.0001 *** TOTWRK −0.150746 0.0167403 −9.005 <0.0001 *** Mean dependent var 3266.356 S.D. dependent var 444.4134 Sum squared resid 1.25e+08 S.E. of regression 421.1357 R-squared 0.103287 Adjusted R-squared 0.102014 F(1, 704) 81.08987 P-value(F) 1.99e-1810538.19 Log-likelihood −5267.096 Akaike criterion 10538.19 Schwarz criterion 10547.31 Hannan-Quinn 10541.71 RSTi =3586.38−0.150746 x TOTWRKi , R2=0.103287,SER=421.1357 (38.9124) (0.0167403) Question? A- The researcher claims that the model lacks a fundamental principle, namely, the impact of the worker’s gender on their efficiency. The researcher further claims that Men on average take more resting time than women. To clarify the researcher’s claim, add the binary variable MALE into your model, and write down the estimated results…
1. Model 1: OLS, using observations 1-706 Dependent variable: RST Coefficient Std. Error t-ratio p-value const 3586.38 38.9124 92.17 <0.0001 *** TOTWRK −0.150746 0.0167403 −9.005 <0.0001 *** Mean dependent var 3266.356 S.D. dependent var 444.4134 Sum squared resid 1.25e+08 S.E. of regression 421.1357 R-squared 0.103287 Adjusted R-squared 0.102014 F(1, 704) 81.08987 P-value(F) 1.99e-1810538.19 Log-likelihood −5267.096 Akaike criterion 10538.19 Schwarz criterion 10547.31 Hannan-Quinn 10541.71 RSTi =3586.38−0.150746 x TOTWRKi , R2=0.103287,SER=421.1357 (38.9124) (0.0167403) Question? Test the significance of the slope coefficient of the regression in Part (1) above. Use 5% level of significance on: a. Level of significance approach (show your calculations of t-ratio) b. P-value approach (show your calculation of p-value) please show the complete steps as well as the interpretation(s) involved in each of the above…
1. Model 1: OLS, using observations 1-706 Dependent variable: RST Coefficient Std. Error t-ratio p-value const 3586.38 38.9124 92.17 <0.0001 *** TOTWRK −0.150746 0.0167403 −9.005 <0.0001 *** Mean dependent var 3266.356 S.D. dependent var 444.4134 Sum squared resid 1.25e+08 S.E. of regression 421.1357 R-squared 0.103287 Adjusted R-squared 0.102014 F(1, 704) 81.08987 P-value(F) 1.99e-1810538.19 Log-likelihood −5267.096 Akaike criterion 10538.19 Schwarz criterion 10547.31 Hannan-Quinn 10541.71 ????=3586.38−0.150746 ? ???????,?2=0.103287,???=421.1357 (38.9124) (0.0167403) Question? could you please help with this question below. 3) By observing the GRETL output in Part (1) above, provide a detailed explanation of the coefficient of determination. Based on your analysis, is this a good model? Why or why not?
SOLVE QUESTIONS D & E ONLY!!!! Consider the accompanying data on flexural strength (MPa) for concrete beams of a certain type. 8.3 7.4 7.0 6.8 7.8 9.7 5.0 6.3 6.8 9.0 7.7 7.3 7.4 11.8 6.3 7.7 11.6 7.2 11.3 9.7 10.7 7.0 7.9 8.7 7.9 8.1 6.5 (a) Calculate a point estimate of the mean value of strength for the conceptual population of all beams manufactured in this fashion. [Hint: Σxi = 218.9.] (Round your answer to three decimal places.) MPa(b) Calculate a point estimate of the strength value that separates the weakest 50% of all such beams from the strongest 50%. MPa(c) Calculate a point estimate of the population standard deviation ?. [Hint: Σxi2 = 1851.35.] (Round your answer to three decimal places.) MPa(d) Calculate a point estimate of the proportion of all such beams whose flexural strength exceeds 10 MPa. [Hint: Think of an observation as a "success" if it exceeds 10.] (Round your answer to three decimal places.)(e) Calculate a point estimate of the population…
Can you solve the following: and provide workings?: 1) Let there be a simple regression yt = (alpha) + (beta)x + (ε), where ε~?(0, σ^2) Let ε(hat) be an OLS estimator of ε. Prove Sum(ε(hat)) = 0 2)Let the share price of a listed company, S, have the following distribution:S~N(100,225) a)Suppose that, when the price of this share falls below the 10th percentile of itsdistribution, it is a signal for buying. What is the maximum price at which you wouldbuy this share? b) Suppose that, when the price of this share rises beyond the 90th percentile of itsdistribution, it is a signal for selling. What is the minimum price at which you wouldsell this share?
You have obtained a sub-sample of 1744 individuals from the Current Population Survey (CPS) and are interested in the relationship between weekly earnings and age. The regression, using heteroskedasticity-robust standard errors, yielded the following result: = 239.16 + 3.75× Age, R2 = 0.15, SER = 287.21., where Earn and Age are measured in dollars and years respectively. Interpret the intercept? Interpret the slope coefficient b) Is the effect of age on earnings large? The average age in this sample is 37.5 years. What is annual income in the sample? (e) Interpret the measures of fit.
1)A new starch polymer foam with high shock absorbent properties is expected to perform better thanthe old foam which can withstand a maximum impact of 1.56 Joule (J). A simple destructive testto quantify shock absorption ability of the new foam is conducted. Table 5 shows the impact energyabsorbed (in J) that the new foam can withstand for each test. Table 51.61 1.5 1.65 1.7 1.4 1.59 1.65 1.5 Does the recorded data suggests that the new foam performs better than the old foam? Use 2.5%level of significance. 2) A number of 28 university students who smokes completed a questionnaire inquiring their smokinghistory and behavior. The result shows that the average smoking initiation age of this group is 21.6years with a variance of 18.49. Based on past record, the smoking initiation age of universitystudent has a mean of 20.74 years with a standard deviation of 5.1 years. Conduct a necessaryanalysis to determine if the smoking initiation age remain the same as the previous record.…
Consider the following population linear regression model of individual food expenditure: Y = 50 + 0.5X + u, where Y is weekly food expenditure in dollars, X is the individual’s age, and 50+0.5X is the population regression line. Suppose we generate artificial data for 3 individuals using this model. This artificial sample, which consists of 3 observations, is shown in the following table: Answer the following questions. Show your working. (a) What are the values of V1 and V4? (b) Suppose we know that in this artificial sample, the sample covariance between X and Y is 150, and the sample variance of X is 100. Compute the OLS regression line of the regression of Y on X. (Hint: Assume these summary statistics and the OLS regression line continue to hold in parts (c)-(e).) (c) What are the values of V5 and V7?
A dietitian wishes to see if a person’s cholesterol level will be changed if the diet is supplemented by a certain mineral. Four subjects were pre-tested, and they took the mineral supplement for a 6-week period. The results are shown in the table. Is there sufficient evidence to conclude that the population mean of cholesterol levels has been changed after six weeks at α=0.2α=0.2? Assume that the differences are from an approximately normally distributed population. Subject Cholestrol Level (mg/dl) Cholestrol Level after 6 Weeks (mg/dl) dd ¯dd¯ (d−¯d)2(d-d¯)2 1 206 217 11 2 219 184 -35 3 202 204 2 4 213 205 -8 Total -30 a) Calculate the mean, the sum of the squared deviation from the mean, and the standard deviation of differences. Do not include the unit for each answer: ¯d=d¯= (do not round) ∑(d−¯d)2=∑(d-d¯)2= (do not round) sd=sd= (rounded to one decimal place) b) Perform the hypothesis test in the following steps: Step 1.…

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