In a study by Alicikus et al. (A-7), long-term control of prostate cancer receivingradiotherapy was examined in patients after 10 years. The authors using Cox regressionanalysis to analyze these data, which resulted in the data summarized in the table below.For these data:(a) Calculate the parameter estimates for the Cox regression model.(b) Provide an explanation of the hazard ratios (HR) and their meaning.(c) For age, provide an alternative measure for the HR and provide its meaning in termsof the percent change in years.Hazard Ratio (HR)VariableAgeHormone therapy(yes vs. no)Pre-PSA, >10ng/mL vs. 10ng/mLTumorclassification..892.411.421.0295% CI for HR(96, 1.08)(44, 1.81)(1.19, 4.88)(1.17, 1.71)p-value.75.015<.001.51

Given::- In a study by Alicikus et al. (A-7), long-term control of prostate cancer receiving…

Answered: In a study by Alicikus et al. (A-7),…

Calculus For The Life Sciences

2nd Edition

ISBN:9780321964038

Author:GREENWELL, Raymond N., RITCHEY, Nathan P., Lial, Margaret L.

Publisher:GREENWELL, Raymond N., RITCHEY, Nathan P., Lial, Margaret L.

Chapter1: Functions

Section1.EA: Extended Application Using Extrapolation To Predict Life Expectancy

Problem 5EA

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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?
A researcher interested in explaining the income levels of St. Lucian workers, developed the following multiple linear regression model: INC = a + BEDU + YEXP+ ST RN where INC = monthly income, EDU = the number of years of formal education, EXP = the number of years of workforce experience and TRN = the number of weeks spent in job training. A sample of 35 workers was processed using MINITAB and the following is an extract of the output obtained: %3D PredictorCoef StDev t-ratio Constant 3315.7 1371.6 2.41 EDU 116.53 26.07 4.47 EXP 282.96 222.8 1.27 TRN -318.12125.74-2.53 S= 2.05 R-sq = 77.3% R-sq(adj) = 75.2% The coefficient y is significant at the 5% level of significance. Select one: O True False
Suppose researchers are interested in exploring the factors which affect depression for Australian adults. The researchers recruited a sample of 99 Australian adults and collected data on several variables which may influence depression. Note that here depression is represented by a score, with higher values representing higher levels of depression. The variables for this study are listed below: Age Gender (0 = female, 1 = male) Stress level Anxiety level Depression Why would conducting a multiple linear regression analysis be appropriate here? Group of answer choices A) Multiple linear regression can be appropriate here because we have one metric dependent variable and several metric or dichotomous independent variables B) Multiple linear regression can be appropriate here because we have one metric dependent variable and several categorical independent variables C) Multiple linear regression can be appropriate here because we have one categorical dependent variable and…
Bill wants to explore factors affecting work stress. He would like to examine the relationship between age, number of years at the workplace, perceived social support, and work stress. He collects data on the variables from 100 employees (males and females) working in banks. The research question is How accurately can work stress be predicted from linear combination of the predictors (age, social support, number of years at the workplace)? Conduct a multiple regression analysis to answer the following questions: State the hypothesis for this study.
Suppose researchers are interested in exploring the factors which affect depression for Australian adults. The researchers recruited a sample of 99 Australian adults and collected data on several variables which may influence depression. Note that here depression is represented by a score, with higher values representing higher levels of depression. The variables for this study are listed below: Age Gender (0 = female, 1 = male) Stress level Anxiety level Depression Prior to running their analysis (multiple regression), the researchers would like to check the regression assumptions for the data. Which of the assumptions below is not an assumption for multiple regression? A) Sphericity B) Linearity and Additivity C) Equal variances D) Normality E) Metric scales
The data in the table represent the number of licensed drivers in various age groups and the number of fatal accidents within the age group by gender. Complete parts (a) through (c) below. Click the icon to view the data table. ... . (a) Find the least-squares regression line for males treating the number of licensed drivers as the explanatory variable, x, and the number of fatal crashes, y, as the response variable. Repeat this procedure for females. Find the least-squares regression line for males. y=x+O Data for licensed drivers by age and gender. %3D (Round the x coefficient to three decimal places as needed. Round the constant to the nearest integer as needed.) Find the least-squares regression line for females. y = Number of Number o X+ Number of Male Fatal Number of Female Fatal (Round the x coefficient to three decimal places as needed. Round the constant to the nearest integer as needed.) Licensed Drivers Crashes Licensed Drivers Crashes (b) Interpret the slope of the…
If a scatterplot is created in excel, and a line of regression is fit along with a derived functional form, what does it mean to describe and interpret them? What conclusions would be made about relationships between two recorded variables?
Demand Estimation for The San Francisco Bread Company Consider the hypothetical example of The San Francisco Bread Company, a San Francisco-based chain of bakery/cafes. San Francisco Bread Company has initiated an empirical estimation of customer traffic at 30 regional locations to help the firm formulate pricing and promotional plans for the coming year. Annual operating data for the 30 outlets appear in the attached Table 1. The following regression equation was fit to these data: Qi = b0 + b1Pi + b2Pxi + b3Adi + b4Ii + uit. Where: Q is the number of meals served, P is the average price per meal (customer ticket amount, in dollars), Px is the average price charged by competitors (in dollars), Ad is the local advertising budget for each outlet (in dollars), I is the average income per household in each outlet’s service area, ui…
Please help me understand this problem more in depth. A researcher is investigating possible explanations for deaths in traffic accidents. He examined data from 2000 for each of the 52 cities randomly selected in the US. The data included information on the following variables: Deaths: The number of deaths in traffic accidents per city Income: The median income per city As part of his study, he ran the following simple linear regression model attached in photo. Question: Based on the above results, the researcher tested the hypotheses: Ho: B1=0 versus B1 not equal to 0, versus using T test. What do we know about the test statistic of the test? Based on the approximate p-value, what's the conclusion?
Demand Estimation for The San Francisco Bread Company Consider the hypothetical example of The San Francisco Bread Company, a San Francisco-based chain of bakery/cafes. San Francisco Bread Company has initiated an empirical estimation of customer traffic at 30 regional locations to help the firm formulate pricing and promotional plans for the coming year. Annual operating data for the 30 outlets appear in the attached Table 1. The following regression equation was fit to these data: Qi = b0 + b1Pi + b2Pxi + b3Adi + b4Ii + uit. Where: Q is the number of meals served, P is the average price per meal (customer ticket amount, in dollars), Px is the average price charged by competitors (in dollars), Ad is the local advertising budget for each outlet (in dollars), I is the average income per household in each outlet’s service area, ui…
Demand Estimation for The San Francisco Bread Company Consider the hypothetical example of The San Francisco Bread Company, a San Francisco-based chain of bakery/cafes. San Francisco Bread Company has initiated an empirical estimation of customer traffic at 30 regional locations to help the firm formulate pricing and promotional plans for the coming year. Annual operating data for the 30 outlets appear in the attached Table 1. The following regression equation was fit to these data: Qi = b0 + b1Pi + b2Pxi + b3Adi + b4Ii + uit. Where: Q is the number of meals served, P is the average price per meal (customer ticket amount, in dollars), Px is the average price charged by competitors (in dollars), Ad is the local advertising budget for each outlet (in dollars), I is the average income per household in each outlet’s service area, ui…
Bill wants to explore factors affecting work stress. He would like to examine the relationship between age, number of years at the workplace, perceived social support, and work stress. He collects data on the variables from 100 employees (males and females) working in banks. The research question is How accurately can work stress be predicted from linear combination of the predictors (age, social support, number of years at the workplace)? Conduct a multiple regression analysis to answer the following questions: What is the relationship of age, number of years, and social support with work stress? Is the regression significant? If yes, what does it indicate?

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