Fundamentals of Biostatistics
8th Edition
ISBN: 9781305268920
Author: Bernard Rosner
Publisher: Cengage Learning
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Chapter 11, Problem 4P
To determine
Describe about the meaning of coefficient of determination.
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PARTS 3c-Question 3 only!
For a sample of over 160 college students, the following variables were measured:
Y = height
X1 = mother’s height
X2 = father’s height
X3 = 1 if male, 0 if female
The goal is to predict student’s height using the mother’s and father’s heights, and sex, where sex is categorized using the variable “male” = 1 if male, 0 if female.
Question 1
The regression model is __________________________________________
Regress command results (remember that we now have n = 165 cases; we removed one outlier):
Source | SS df MS Number of obs = 165
-------------+------------------------------ F( 3, 161) = 104.38
Model | 1679.17741 3 559.725803 Prob > F = 0.0000
Residual | 863.31653 161 5.36221447 R-squared = 0.6604
-------------+------------------------------ Adj R-squared = 0.6541
Total | 2542.49394 164 15.5030118…
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?
Suppose that you perform a hypothesis test for the slope of the population regression line with the null hypothesis H0: ß1 = 0 and the alternative hypothesis Ha: ß1 ? 0. If you reject the null hypothesis, what can you say about the utility of the regression equation for making predictions?
Chapter 11 Solutions
Fundamentals of Biostatistics
Ch. 11.12 - What is the difference between an ordinary...Ch. 11 - Hematology The data in Table 11.17 are given for 9...Ch. 11 - Hematology The data in Table 11.17 are given for 9...Ch. 11 - Hematology The data in Table 11.17 are given for 9...Ch. 11 - Prob. 4PCh. 11 - Prob. 5PCh. 11 - Prob. 6PCh. 11 - Hematology The data in Table 11.17 are given for 9...Ch. 11 - Prob. 8PCh. 11 - Pulmonary Function Suppose the correlation...
Ch. 11 - Prob. 10PCh. 11 - Prob. 11PCh. 11 - Prob. 12PCh. 11 - Hypertension The Update to the Task Force Report...Ch. 11 - Prob. 14PCh. 11 - Prob. 31PCh. 11 - Prob. 32PCh. 11 - Prob. 49PCh. 11 - Refer to Data Set BONEDEN.DAT at www.cengagebrain...Ch. 11 - Prob. 92PCh. 11 - Prob. 93PCh. 11 - Prob. 94PCh. 11 - Diabetes A group of 10-year-old boys were first...Ch. 11 - Prob. 118PCh. 11 - Prob. 119PCh. 11 - Prob. 120PCh. 11 - Prob. 121P
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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?arrow_forwardThe following fictitious table shows kryptonite price, in dollar per gram, t years after 2006. t= Years since 2006 0 1 2 3 4 5 6 7 8 9 10 K= Price 56 51 50 55 58 52 45 43 44 48 51 Make a quartic model of these data. Round the regression parameters to two decimal places.arrow_forward
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