The following data presents the number oftransistors per chip for Intel computer chipsbetween 1971 and 1995.1 Moore's Lawpredicts that these values will growexponentially. Let's check on the accuracy ofMoore's Law.Year1971197219741976197819821985198919931995ChipModelNameIntel 4004Intel 8008Intel 8080Intel 8085Intel 8086Intel 80286Intel 80386Intel 80486yearIntelPentiumIntelPentiumProNumber ofTransistors perChip230035004500650029000134000275000118023531000005500000Part A: What was the rate of growth intransistors from 1972 to 1974?transistors peryearPart B: What was the rate of growth from1978 to 1982?transistors per Part C: What was the rate of growth from1993 to 1995?transistors peryearPart D: Which of the following bestdescribes the growth rate of transistors?O It appears approximately linearO It appears approximately exponentialO Neither

Given, Year (X) Number of Transistor per chip (Y) 1971 2300 1972 3500 1974 4500 1976 6500…

Answered: The following data presents the number…

Advanced Engineering Mathematics

10th Edition

ISBN:9780470458365

Author:Erwin Kreyszig

Publisher:Erwin Kreyszig

Chapter2: Second-order Linear Odes

Section: Chapter Questions

Problem 1RQ

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1. Studies of a specific enzyme activity showed that the time the enzyme activity before complete inactivity is exponentially distributed with a mean of 10 hours. Determine the failure rate.
The table shows the leading causes of death in a certain country in a recent year. The population of the country was 311311 million. If you lived in a typical city of 500,000, how many people would you expect to die of cancercancer each year? Cause Deaths Cause Deaths Heart disease 596 comma 700596,700 Alzheimer's disease 84 comma 90084,900 Cancer 575 comma 300575,300 Diabetes 73 comma 40073,400 Chronic respiratory diseases 143 comma 100143,100 Pneumonia/Influenza 53 comma 40053,400 Stroke 128 comma 600128,600 Kidney disease 45 comma 30045,300 Accidents 122 comma 500122,500 Suicide 38 comma 60038,600 About people would be expected to die of cancer each year. (Type a whole number. Round to the nearest person as needed.)
Given that Log(total primary energy consumption per capita)=8.303336 +-.9599562*log(GDP per capita USD) + .0954567*square of log(GDP per capita) + -.2392021*log(pump price for gasoline USD) + .006395*log(annual precipitation mm) Questions 1)Describe if MLR 5 is likely to hold or not ? 2)Describe if MLR 4 is likely to hold or not ?
Comment on the statistical significance of the estimated logit model.
Consider the logit regression log(odds(QualExam) = ßo + B, • ParEduc + B, • Awards. where QualExam is a binary variable that indicates passing the exam if equal to 1, and failing the exam if 0, ParEduc indicates the parents' education level, and Awards is a binary variable that indicates having experience of obtaining award(s) if equal to 1, and not having experience if 0. Given the parents' average education level unchanged, the odds ratio is expected to be _ for an individual with awards to pass the exam comparing to those without awards. For an individual without awards and the parents' education level of 4, the estimated probability of passing the exam is approximately_. ParEduc Awards Intercept -10.53 2.98 0.48 O A. 0.48; 80%. O B. 1.616; 80%. O C. 1.616; 4%. O D. 0.48; 4%.
Would you include ros in a final model explaining CEO compensation in terms of firm performance?
A psychologist believed that as children get older, they engage in more “inactive activities” such as playing computer games, watching TV and surfing the internet. She collected data from 10 children. She recorded the age of each child and the amount of activity (in hours) the child engaged in per week. Age Activity 7 4.3 9 3.2 10 1 13 1.5 12 1.2 15 0.8 9 3 12 4 15 1.5 14 1.9 Using excel, obtain a scatterplot of the data. Discuss the relationship between age and activity based on the scatterplot. Using excel, obtain the simple linear regression linear regression line predicting activity from age. Interpret the intercept and slope of the regression line. By hand, predict the amount of activity for age = 14. By hand, calculate the residual for age = 14 Using excel output, test the null hypothesis that the slope coefficient is zero using the p-value approach. Write one sentence interpreting the meaning of this result. Make sure to report the test statistic…
Q1. 807 people were randomly selected and data was collected on whether or not they smoked cigarettes in the past 12 months (smoked = 1 if they did, smoked = 0 otherwise), their years of schooling (educ), the natural log of their annual income in dollars (In[income]), age in years (age), and race (white = 1 if white, white = 0 otherwise). The following logit models were estimated and the results are summarized in the table below. The numbers in parentheses are the standard errors. (1) logit (2) logit (3) sample average constant 1.43 0.02 (0.40) (1.06) educ -0.10 -0.11 12 (0.03) (0.03) In[income] 0.16 10.8 (0.11) age -0.02 -0.02 41 (0.0004) | (0.0004) white 0.02 0.7 (0.23) log-likelihood -525 -524 In column (2), the regression equation for smokeď* is: smoked* = x'3 + e = B1 + B2educ + B3 In[income] + B4age + B5white + e In column (3), the sample averages of the regressors are listed. For example, 12 is the sample average of educ, 10.8 is the average of In[income], etc. (a) Using the…
Interpolate by means of Gauss backward formula, the population of a town for the year 1974, give that: Year: 1939 1949 1959 1969 1979 1989 Population: (in thousands) 12 15 20 27 39 52
5) Cobalt-60 is used in many common industrial applications and in radiotherapy in hospitals. Large sources of cobalt-60 are increasingly used for sterilization of spices and certain foods. The powerful gamma rays kill bacteria and other pathogens, without damaging the product. After the radiation ceases, the product is not left radioactive. This process is sometimes called "cold pasteurization. The following is the experimental data taken for Cobalt-60 Time (year) 2 70 Mass (g) And the model for radioactive decay is: •(†Ð)* m = m₂ Σ... - [(Σ.) (Σ.)]/n Σx² - (Σxi)²/n 6 45 b₁ = bo = ý – b c) Find mo and the half-life of this material 11 22 a) Linearize the proposed equation b) Find the equation of the regression line by least squares estimates 14 12 17 10 21 4
2
Consider the logit regression log(odds(QualExam)) = B, + B, * ParEduc + B2 + Awards, where QualExam is a binary variable that indicates passing the exam if equal to 1, and failing the exam if 0, ParEduc indicates the parents' education level, and Awards is a binary variable that indicates having experience of obtaining award(s) if equal to 1, and not having experience if 0. Given the parents' average education level unchanged, the odds ratio is expected to be for an individual with awards to pass the exam comparing to those without awards. For an individual without awards and the parents' education level of 4, the estimated probability of passing the exam is approximately_. Intercept ParEduc Awards -10.53 2.98 0.48 O A. 1.616; 4%. O B. 1.616; 80%. O C. 0.48; 4%. O D. 0.48; 80%.

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