0.75 0.5- 0.25 0. -1 3 PC3 (hot) b) 0.75 8 0.5 0.25 -1 0. 3. PC3 (room temp.) Download : Download high-res image (153KB) Download : Download full-size image Figure 2. Logistic regression illustrating effects of PC3 (total sidling courtship) on the probability of copulation in H. clypeatus at (a) high temperature and (b) room temperatur In both panels, points are jittered slightly in the Y direction to prevent overplotting. Shadee area indicates 95% confidence interval. Probability of copulation

0.75 0.5- 0.25 0. -1 3 PC3 (hot) b) 0.75 8 0.5 0.25 -1 0. 3. PC3 (room temp.) Download : Download high-res image (153KB) Download : Download full-size image Figure 2. Logistic regression illustrating effects of PC3 (total sidling courtship) on the probability of copulation in H. clypeatus at (a) high temperature and (b) room temperatur In both panels, points are jittered slightly in the Y direction to prevent overplotting. Shadee area indicates 95% confidence interval. Probability of copulation

College Algebra

1st Edition

ISBN:9781938168383

Author:Jay Abramson

Publisher:Jay Abramson

Chapter6: Exponential And Logarithmic Functions

Section6.8: Fitting Exponential Models To Data

Problem 3TI: Table 6 shows the population, in thousands, of harbor seals in the Wadden Sea over the years 1997 to...

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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?
Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s pole vault height has fallen below the value predicted by the regression line in Example 2. This might have occurred because when the pole vault was a new event there was much room for improvement in vaulters’ performances, whereas now even the best training can produce only incremental advances. Let’s see whether concentrating on more recent results gives a better predictor of future records. (a) Use the data in Table 2 (page 176) to complete the table of winning pole vault heights shown in the margin. (Note that we are using x=0 to correspond to the year 1972, where this restricted data set begins.) (b) Find the regression line for the data in part ‚(a). (c) Plot the data and the regression line on the same axes. Does the regression line seem to provide a good model for the data? (d) What does the regression line predict as the winning pole vault height for the 2012 Olympics? Compare this predicted value to the actual 2012 winning height of 5.97 m, as described on page 177. Has this new regression line provided a better prediction than the line in Example 2?
The following data resulted from a study commissioned by a large management consulting company to investigate the relationship between amount of job experience (months) for a junior consultant and the likelihood of the consultant being able to perform a certain complex task. (image w/ success and failure) Interpret the accompanying MINITAB logistic regression output, and sketch a graph of the estimated probability of task performance as a function of experience. (2nd image)
A researcher is attempting to explore the relationship between study duration per day (0 - 8) and the passing of the science course (Pass=1, Fail=0). For this purpose, a sample of 36 students from Alabama University are chosen. Summary of the logistic regression model has been included. Please help me understand and choose from the following below, which is the correct representation of the related logistic regression model in the summary photo attached. - PASS = 0.57 + 3.69 X SDURATION - PASS= 3.69 +0.57 X SDURATION - PASS/FAIL=3.69 + 0.57 x SDURATION - P(PASS/F(FAIL)=3.69+ 0.57 X SDURATION - PASS/FAIL= 0.57 + 3.69 x SDURATION - ln{P(PASS/F(FAIL)}=0.57 + 3.69 X SDURATION - ln{P(PASS/F(FAIL)}= 3.69+ 0.57 X SDURATION
Explain which characteristic of the STA leads to a consideration of a logistic model as opposed to a linear regression mode.
Table 4.12 refers to ratings of agricultural extension agents in North Carolina. In each of five districts, agents were classified by their race and by whether they qualified for a merit pay increase. a. Conduct the Cochran–Mantel–Haenszel test of the hypothesis that the merit pay decision is independent of race, conditional on the district. Interpret. b. Show how you could alternatively test the hypothesis in (a) using a test about a parameter in a logistic regression model. c. What information can you get from a model-based analysis that you do not get from the CMH test?
A jar contains 7 gold, 5 silver, 4 blue, 3 red, and 1 green marbles. Two marbles are to be randomly drawn from the jar. What is the probability a sliver marble is drawn, not returned to the jar, and then a blue marble is drawn? Given the power regression model y = 25x^1.2, which is the linear regression model after transforming to a log-log graph?
Table 4.8 shows results of an eight-center clinical trial to compare a drug to placebo for curing an infection. At each center, subjects were randomly assigned to treatments. Analyze these data, available in the Infection data file at the text website. Using logistic regression, describe and make inference about the treatment effect.
Consider a hypothetical regression predicting if someone will be married or not by the age of 40, MARRIED? (1 means this person is married by the age of 40 and 0 means this person is not married by the age of 40). The regression is as follows (all variables are statistically significant): MARRIED? = 0.2 + 0.03*EDUCATION - 0.01*BMI Where EDUCATION is the number of years of education someone's had and BMI is their body mass index. Suppose someone had 20 years of education and a BMI of 25. What is the predicted value of MARRIAGE? 0.35, which makes sense even though MARRIED? can only be a zero or one 0, because the calculated value is 0.35 so we round down. 0.55, which makes sense even though MARRIED? can only be a zero or one. Calculating a predicted value should not be done here because the dependent variable is a dummy variable. 1, because the calculated value is 0.55 so we round up.
Consider a hypothetical regression predicting if someone will be married or not by the age of 40, MARRIED? (1 means this person is married by the age of 40 and 0 means this person is not married by the age of 40). The regression is as follows (all variables are statistically significant): MARRIED? = 0.2 + 0.03*EDUCATION - 0.01*BMI Where EDUCATION is the number of years of education someone's had and BMI is their body mass index. Suppose someone had 20 years of education and a BMI of 25. What is the predicted value of MARRIAGE? a 0, because the calculated value is 0.35 so we round down. b 0.55, which makes sense even though MARRIED? can only be a zero or one. c 0.35, which makes sense even though MARRIED? can only be a zero or one d Calculating a predicted value should not be done here because the dependent variable is a dummy variable. e 1, because the calculated value is 0.55 so we round up.
Consider a hypothetical regression predicting if someone will be married or not by the age of 40, MARRIED? (1 means this person is married by the age of 40 and 0 means this person is not married by the age of 40). The regression is as follows (all variables are statistically significant): MARRIED? = 0.2 + 0.03*EDUCATION - 0.01*BMI Where EDUCATION is the number of years of education someone's had and BMI is their body mass index. Suppose someone had 20 years of education and a BMI of 25. Complete this sentence: For every additional year of education someone has:... a ...their chance of getting married by 40 increases by 0.03 percentage points. b ...their chance of getting married by 40 increases by 3 percentage points. c ...their chance of getting married by 40 increases by 0.03. d ...their chance of getting married by 40 increases by 3%. e This regression means nothing because the dependent variable is a dummy variable.