a. What is the probability that the lifetime X of the first component exceeds 3? b. What are the marginal pdf's of X and Y? Are the two lifetimes independent? x. What is the probability that the lifetime of at least one component exceeds 3?
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a. What is the
b. What are the marginal
x. What is the probability that the lifetime of at least one component exceeds 3?
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- What does the y -intercept on the graph of a logistic equation correspond to for a population modeled by that equation?Respiratory Rate Researchers have found that the 95 th percentile the value at which 95% of the data are at or below for respiratory rates in breath per minute during the first 3 years of infancy are given by y=101.82411-0.0125995x+0.00013401x2 for awake infants and y=101.72858-0.0139928x+0.00017646x2 for sleeping infants, where x is the age in months. Source: Pediatrics. a. What is the domain for each function? b. For each respiratory rate, is the rate decreasing or increasing over the first 3 years of life? Hint: Is the graph of the quadratic in the exponent opening upward or downward? Where is the vertex? c. Verify your answer to part b using a graphing calculator. d. For a 1- year-old infant in the 95 th percentile, how much higher is the walking respiratory rate then the sleeping respiratory rate? e. f.Table 6 shows the population, in thousands, of harbor seals in the Wadden Sea over the years 1997 to 2012. a. Let x represent time in years starting with x=0 for the year 1997. Let y represent the number of seals in thousands. Use logistic regression to fit a model to these data. b. Use the model to predict the seal population for the year 2020. c. To the nearest whole number, what is the limiting value of this model?
- Table 2 shows a recent graduate’s credit card balance each month after graduation. a. Use exponential regression to fit a model to these data. b. If spending continues at this rate, what will the graduate’s credit card debt be one year after graduating?Suppose a logistic regression model is fitted for the probability of car ownership for residents of a certain city in Oman (Y=1 if a resident owns a car, Y=0 if a resident does not own a car). Suppose the explanatory variables used are x1=no. of years a resident spent in schooling and x2 is gender of the resident of the city (x2=1 for a male and x2=0 for a female resident) a) Interpret el and e82 b) if BO= -1.6, B1=0.4 and B2=3, estimate the probability of a resident in the city owning a car.Please help me find likelihood, log-likelihood and MLEs of two parameters, thank you.
- Suppose that we are working with some doctors on heart attack patients. The dependent variable is whether the patient has had a second heart attack within 1 year (yes=1). We have two independent variables, one is age of the patient and the other is a score on anxiety scale (a higher score means more anxious). After applying logistic regression model, we have the following output: Deviance Residuals: Min 10 Median 30 Мах |-1.064 0.000 0.000 0.000 1.446 Coefficients: Estimate Std. Error z value Pr (>|z|) (Intercept) -471.441 223186.509 -0.002 0.998 Age 6.394 3057.349 0.00 0.99 Anxiety 0.002 1.347 611.470 0.998 (Dispersion parameter for binomial family taken to be 1) Null deviance: 27.7259 on 19 degrees of freedom Residual deviance: AIC: 9.7087 3.7087 on 17 degrees of freedom Number of Fisher Scoring iterations: 23 a. Determine the estimated logistic regression equation. b. Calculate the odds ratio and interpret.For the horseshoe crab data, fit the logistic regression model for π = probability of a satellite, using weight as the predictor. Data source (Horseshoe crab data set of Table 4.3): https://users.stat.ufl.edu/~aa/cda/data.html Note: In the original data set, weights are in grams rather than kg. Correct the unit by adding a new column which convert weight into kg and run the logistic regression with correct weight variable. a. Report the ML prediction equation. b. Find at the weight values 1.20, 2.44, and 5.20 kg, which are the sample minimum, mean, and maximum. c. Find the weight at which = 0.50 d. Ignore part d e. Construct a 95% confidence interval to describe the effect of weight on the odds of a satellite. Interpret. f. Conduct the Wald or likelihood-ratio test of the hypothesis that weight has no effect. Report the P -value and interpret.Assume that you have collected cross-sectional data for average hourly earnings (ahe), the number of years of education (educ) and gender of the individuals (you have coded individuals as "1" if they are female and "0" if they are male; the name of the resulting variable is DFemme). Having faced recent tuition hikes at your university, you are interested in the return to education, that is, how much more will you earn extra for an additional year of being at your institution. To investigate this question, you run the following regression: ahe= -4.58 + 1.71×educ N = 14,925, R2 = 0.18, SER = 9.30 a. Interpret the regression output. b. Being a female, you wonder how these results are affected if you entered a binary variable (DFemme), which takes on the value of "1" if the individual is a female, and is "0" for males. The result is as follows ahe= = -3.44 - 4.09×DFemme + 1.76×educ N = 14,925, R2 = 0.22, SER = 9.08 Does it make sense that the standard error of the regression decreased…
- 3) Fresh tomatoes were stored at 15°C. Lycopene (orange-red pigment) contents were determined for a period of 10 weeks at 15°C. The results are presented in Table 3. Lycopene degradation during storage of tomatoes is known to give the straight line in a semi-log graphic paper. Table 3- Lycopene contents in fresh tomatoes stored at 15°C Storage time (week) Lycopene content (mg/kg) 33 2 23 6 5.3 10 2.1 a) Find out the equation describing the degradation of lycopene in tomatoes at 15°C, using the "linear regression analysis. Note: Give units wherever it is applicable!!!! Σxy- (Σx Σy / n ) ΣyΣx%- Σx Σxy b = Ex² - [ (Ex)² / n) ] η (Σχ'- [ (Σx)>/ n) ] )3) Fresh tomatoes were stored at 15°C. Lycopene (orange-red pigment) contents were determined for a period of 10 weeks at 15°C. The results are presented in Table 3. Lycopene degradation during storage of tomatoes is known to give the straight line in a semi-log graphic paper. Table 3– Lycopene contents in fresh tomatoes stored at 15°C Storage time (week) Lycopene content (mg/kg) 33 2 23 6 5.3 10 2.1 Find out the lycopene concentration in tomatoes after 4 months of storage at 15°C. Note: Give units wherever it is applicable!!!! Σxy- (Σx Σy / ) ΣyΣx?- Σx Σxy Ex² - [ (Ex)² / n) ] n (E x² - [ (Ex)² / n) ] )A researcher collected a data set for a random sample of 930 individuals living in and around London, with data collected over 1-year period. The Table below reports the OLS coefficient estimates (intercept not reported) and standard errors (in parentheses), where the dependent variable is [100xIn(well-being)]. Commuting time/60 -0.267 (0.039) -0.14 (0.040) Age Age squared/100 0.12 (0.040) Hours worked -0.0053 (0.001) log real income 0.0267 (0.009) Married or cohabiting 0.589 (0.032) Num. of children. -0.051 (0.015) Saves Degree 0.299 (0.022) -0.022 (0.035) The explanatory variables are: Commuting time = Number of minutes of commuting time per day; Age= Age in years; Hours worked = Hours worked per week; Log of real household income = 100xLn(real household income measured in £10,000s); Num. of children = Number of children under the age of 18; Save regularly = 1 if save regularly, 0 otherwise; University degree = 1 if has a University degree, 0 otherwise. Calculate the test statistics…