Exam 25: Multiple and Logistic Regression

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Tail-feather length is a sexually dimorphic trait in long-tailed finches; that is, the trait differs substantially for males and for females of the same species. A bird's size and environment might also influence tail-feather length. Researchers studied the relationship between tail-feather length (measuring the R1 central tail feather) and weight in a sample of 20 male long-tailed finches raised in an aviary and 5 male long-tailed finches caught in the wild. The data are displayed in the scatterplot below. ​  Tail-feather length is a sexually dimorphic trait in long-tailed finches; that is, the trait differs substantially for males and for females of the same species. A bird's size and environment might also influence tail-feather length. Researchers studied the relationship between tail-feather length (measuring the R1 central tail feather) and weight in a sample of 20 male long-tailed finches raised in an aviary and 5 male long-tailed finches caught in the wild. The data are displayed in the scatterplot below. ​   The following model is proposed for predicting the length of the R1 feather from the bird's weight and origin: R1Length<sub>i</sub> = β<sub>0</sub> + β<sub>1</sub> (BodyWeight<sub>i</sub>) + β<sub>2</sub> (Aviary0Wild1<sub>i</sub>) + ε<sub>i</sub> Where the deviations ε<sub>i</sub> were assumed to be independent and Normally distributed with mean 0 and standard deviation σ . This model was fit to the sample of 25 male finches. The following results summarize the least-squares regression fit of this model:  \begin{array}{l} \begin{array}{lrr} \text { Predictor } & \text { Coef } & \text { SE Coef } \\ \text { Constant } & 35.79 & 16.95 \\ \text { BodyWeight } & 2.826 & 1.030 \\ \text { Aviary0Wild1 } & -10.453 & 4.996 \end{array}\\ \mathrm{S}=9.62172 \quad \mathrm{R}-\mathrm{Sq}=30.2 \% \end{array}    \begin{array}{l} \text { Analysis of Variance }\\ \begin{array}{lrrr} \text { Source } & \text { DF } & \text { SS } & \text { MS } \\ \text { Regression } & 2 & 879.79 & 439.90 \\ \text { Residual Error } & 22 & 2036.71 & 92.58 \\ \text { Total } & 24 & 2916.50 & \end{array} \end{array}  To determine whether the bird's origin affects tail-feather length in this model, we test the hypotheses H<sub>0</sub>: β<sub>2</sub> = 0, H<sub>α</sub>: β<sub>2</sub> ≠ 0 in this particular model. Using the estimates provided, what is the P-value for this test? The following model is proposed for predicting the length of the R1 feather from the bird's weight and origin: R1Lengthi = β0 + β1 (BodyWeighti) + β2 (Aviary0Wild1i) + εi Where the deviations εi were assumed to be independent and Normally distributed with mean 0 and standard deviation σ . This model was fit to the sample of 25 male finches. The following results summarize the least-squares regression fit of this model: Predictor Coef SE Coef Constant 35.79 16.95 BodyWeight 2.826 1.030 Aviary0Wild1 -10.453 4.996 =9.62172-=30.2\% Analysis of Variance Source DF SS MS Regression 2 879.79 439.90 Residual Error 22 2036.71 92.58 Total 24 2916.50 To determine whether the bird's origin affects tail-feather length in this model, we test the hypotheses H0: β2 = 0, Hα: β2 ≠ 0 in this particular model. Using the estimates provided, what is the P-value for this test?

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A study examined the effect of body weight and origin (aviary or wild) on the length of the R1 central tail feather in male finches. The following model is proposed for predicting the length of the R1 feather from the bird's weight (in grams) and origin (aviary = 0, wild = 1): R1Lengthi = β0 + β1 (BodyWeighti) + β2 (Aviary0Wild1i) + εi Where the deviations εi were assumed to be independent and Normally distributed with mean 0 and standard deviation σ. This model was fit to a sample of 25 male finches. Using software, we obtained the following output: Predictor Coef SE Coef Constant 35.79 16.95 BodyWeight 2.826 1.030 Aviary0Wild1 -10.453 4.996 Predicted Values for New Observations NewObs Fit SE Fit 95\% 95\% PI 1 76.22 4.32 (67.26,85.17) (54.35,98.09) 2 86.67 2.75 (80.96,92.38) (65.91,107.43) Values of Predictors for New Observations NewObs BodyWeight Aviary0Wild 1 18.0 1.0 2 18.0 0.0 What is the best description of the interval (67.26, 85.17)?

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Researchers investigated the effects of acute stress on emotional picture processing and recall by randomly assigning 40 adult males to receive either a stressful cold stimulus or a neutral warm stimulus before viewing pictures. The researchers computed the relative brainwave amplitude (in microvolts, μV) for each subject viewing unpleasant versus neutral pictures. They also recorded how many pictures the subjects were able to recall 24 hours later. The following model is proposed for predicting the brainwave relative amplitude ("amplitude") from the number of images recalled ("recall"), the indicator variable reflecting the nature of the stimulus ("stress"), and an interaction term ("recall*stress"): Amplitudei = β0 + β1 (recalli) + β2 (stressi) + β3 (recall*stressi) + εi Where the deviations εi are assumed to be independent and Normally distributed with mean 0 and standard deviation σ. This model was fit to the sample of 40 adult males. Using software, we obtained the following output: Predictor Coef SE Coef Constant 4.795 1.329 recall -0.1139 0.1069 stress -4.262 2.205 recall* stress 0.4337 0.1677
 Predicted Values for New Observations
 New          Obs   Fit  SEFit  95%CI  95%
 PI                  
   1  3.731  0.624  (2.465,4.996)          
   2  3.656   0.508  (2.625,4.686)          
Values of Predictors for New Observations New
 New          Obs recall stress recall*stress
   1 10.0 1.00 10.00        
   2 10.0 0.00 0.00        
Which of the following statements best describes the interval (-0.804, 8.115)?

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Researchers investigated the effects of acute stress on emotional picture processing and recall by randomly assigning 40 adult males to receive either a stressful cold stimulus or a neutral warm stimulus before viewing pictures. The researchers computed the relative brainwave amplitude (in microvolts, μV) for each subject viewing unpleasant versus neutral pictures. They also recorded how many pictures the subjects were able to recall 24 hours later. The following model is proposed for predicting the brainwave relative amplitude ("amplitude") from the number of images recalled ("recall"), the indicator variable reflecting the nature of the stimulus ("stress"), and an interaction term ("recall*stress"): Amplitudei = β0 + β1 (recalli) + β2 (stressi) + β3 (recall*stressi) + εi Where the deviations εi are assumed to be independent and Normally distributed with mean 0 and standard deviation σ. This model was fit to the sample of 40 adult males. Using software, we obtained the following output: Predictor Coef SE Coef Constant 4.795 1.329 recall -0.1139 0.1069 stress -4.262 2.205 recall* stress 0.4337 0.1677
 Predicted Values for New Observations
 New          Obs   Fit  SEFit  95%CI  95%
 PI                  
   1  3.731  0.624  (2.465,4.996)          
   2  3.656   0.508  (2.625,4.686)          
Values of Predictors for New Observations New
 New          Obs recall stress recall*stress
   1 10.0 1.00 10.00        
   2 10.0 0.00 0.00        
Which of the following statements best describes the interval (2.465, 4.996)?

(Multiple Choice)
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Tail-feather length is a sexually dimorphic trait in long-tailed finches; that is, the trait differs substantially for males and for females of the same species. A bird's size and environment might also influence tail-feather length. Researchers studied the relationship between tail-feather length (measuring the R1 central tail feather) and weight in a sample of 20 male long-tailed finches raised in an aviary and 5 male long-tailed finches caught in the wild. The data are displayed in the scatterplot below. ​  Tail-feather length is a sexually dimorphic trait in long-tailed finches; that is, the trait differs substantially for males and for females of the same species. A bird's size and environment might also influence tail-feather length. Researchers studied the relationship between tail-feather length (measuring the R1 central tail feather) and weight in a sample of 20 male long-tailed finches raised in an aviary and 5 male long-tailed finches caught in the wild. The data are displayed in the scatterplot below. ​   The following model is proposed for predicting the length of the R1 feather from the bird's weight and origin: R1Length<sub>i</sub> = β<sub>0</sub> + β<sub>1</sub> (BodyWeight<sub>i</sub>) + β<sub>2</sub> (Aviary0Wild1<sub>i</sub>) + ε<sub>i</sub> Where the deviations ε<sub>i</sub> were assumed to be independent and Normally distributed with mean 0 and standard deviation σ . This model was fit to the sample of 25 male finches. The following results summarize the least-squares regression fit of this model:  \begin{array}{l} \begin{array}{lrr} \text { Predictor } & \text { Coef } & \text { SE Coef } \\ \text { Constant } & 35.79 & 16.95 \\ \text { BodyWeight } & 2.826 & 1.030 \\ \text { Aviary0Wild1 }-10.453 & 4.996 \end{array}\\ \mathrm{S}=9.62172 \quad \mathrm{R}-\mathrm{Sq}=30.2 \% \end{array}    \begin{array}{l} \text { Analysis of Variance }\\ \begin{array}{lrrr} \text { Source } & \text { DF } & \text { SS } & \text { MS } \\ \text { Regression } & 2 & 879.79 & 439.90 \\ \text { Residual Error } & 22 & 2036.71 & 92.58 \\ \text { Total } & 24 & 2916.50 & \end{array} \end{array}  Suppose we wish use the ANOVA F test to test the following hypotheses: H<sub>0</sub>: β<sub>1</sub> = β<sub>2</sub> = 0 H<sub>α</sub>: At least one of β<sub>1</sub> or β<sub>2</sub> is not 0 What is the value of the F statistic for these hypotheses? The following model is proposed for predicting the length of the R1 feather from the bird's weight and origin: R1Lengthi = β0 + β1 (BodyWeighti) + β2 (Aviary0Wild1i) + εi Where the deviations εi were assumed to be independent and Normally distributed with mean 0 and standard deviation σ . This model was fit to the sample of 25 male finches. The following results summarize the least-squares regression fit of this model: Predictor Coef SE Coef Constant 35.79 16.95 BodyWeight 2.826 1.030 Aviary0Wild1 -10.453 4.996 =9.62172-=30.2\% Analysis of Variance Source DF SS MS Regression 2 879.79 439.90 Residual Error 22 2036.71 92.58 Total 24 2916.50 Suppose we wish use the ANOVA F test to test the following hypotheses: H0: β1 = β2 = 0 Hα: At least one of β1 or β2 is not 0 What is the value of the F statistic for these hypotheses?

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Tail-feather length is a sexually dimorphic trait in long-tailed finches; that is, the trait differs substantially for males and for females of the same species. A bird's size and environment might also influence tail-feather length. Researchers studied the relationship between tail-feather length (measuring the R1 central tail feather) and weight in a sample of 20 male long-tailed finches raised in an aviary and 5 male long-tailed finches caught in the wild. The data are displayed in the scatterplot below. ​  Tail-feather length is a sexually dimorphic trait in long-tailed finches; that is, the trait differs substantially for males and for females of the same species. A bird's size and environment might also influence tail-feather length. Researchers studied the relationship between tail-feather length (measuring the R1 central tail feather) and weight in a sample of 20 male long-tailed finches raised in an aviary and 5 male long-tailed finches caught in the wild. The data are displayed in the scatterplot below. ​   The following model is proposed for predicting the length of the R1 feather from the bird's weight and origin: R1Length<sub>i</sub> = β<sub>0</sub> + β<sub>1</sub> (BodyWeight<sub>i</sub>) + β<sub>2</sub> (Aviary0Wild1<sub>i</sub>) + ε<sub>i</sub> Where the deviations ε<sub>i</sub> were assumed to be independent and Normally distributed with mean 0 and standard deviation σ . This model was fit to the sample of 25 male finches. The following results summarize the least-squares regression fit of this model:  \begin{array}{l} \begin{array}{lrr} \text { Predictor } & \text { Coef } & \text { SE Coef } \\ \text { Constant } & 35.79 & 16.95 \\ \text { BodyWeight } & 2.826 & 1.030 \\ \text { Aviary0Wild1 } & -10.453 & 4.996 \end{array}\\ \mathrm{S}=9.62172 \quad \mathrm{R}-\mathrm{Sq}=30.2 \% \end{array}    \begin{array}{l} \text { Analysis of Variance }\\ \begin{array}{lrrr} \text { Source } & \text { DF } & \text { SS } & \text { MS } \\ \text { Regression } & 2 & 879.79 & 439.90 \\ \text { Residual Error } & 22 & 2036.71 & 92.58 \\ \text { Total } & 24 & 2916.50 & \end{array} \end{array}  Using a significance level of 0.05, what should you conclude about long-tailed finches, based on your calculations for a confidence interval for β<sub>1</sub> and for a test of hypothesis on β<sub>2</sub>? The following model is proposed for predicting the length of the R1 feather from the bird's weight and origin: R1Lengthi = β0 + β1 (BodyWeighti) + β2 (Aviary0Wild1i) + εi Where the deviations εi were assumed to be independent and Normally distributed with mean 0 and standard deviation σ . This model was fit to the sample of 25 male finches. The following results summarize the least-squares regression fit of this model: Predictor Coef SE Coef Constant 35.79 16.95 BodyWeight 2.826 1.030 Aviary0Wild1 -10.453 4.996 =9.62172-=30.2\% Analysis of Variance Source DF SS MS Regression 2 879.79 439.90 Residual Error 22 2036.71 92.58 Total 24 2916.50 Using a significance level of 0.05, what should you conclude about long-tailed finches, based on your calculations for a confidence interval for β1 and for a test of hypothesis on β2?

(Multiple Choice)
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Tail-feather length is a sexually dimorphic trait in long-tailed finches; that is, the trait differs substantially for males and for females of the same species. A bird's size and environment might also influence tail-feather length. Researchers studied the relationship between tail-feather length (measuring the R1 central tail feather) and weight in a sample of 20 male long-tailed finches raised in an aviary and 5 male long-tailed finches caught in the wild. The data are displayed in the scatterplot below. ​  Tail-feather length is a sexually dimorphic trait in long-tailed finches; that is, the trait differs substantially for males and for females of the same species. A bird's size and environment might also influence tail-feather length. Researchers studied the relationship between tail-feather length (measuring the R1 central tail feather) and weight in a sample of 20 male long-tailed finches raised in an aviary and 5 male long-tailed finches caught in the wild. The data are displayed in the scatterplot below. ​   The following model is proposed for predicting the length of the R1 feather from the bird's weight and origin: R1Length<sub>i</sub> = β<sub>0</sub> + β<sub>1</sub> (BodyWeight<sub>i</sub>) + β<sub>2</sub> (Aviary0Wild1<sub>i</sub>) + ε<sub>i</sub> Where the deviations ε<sub>i</sub> were assumed to be independent and Normally distributed with mean 0 and standard deviation σ . This model was fit to the sample of 25 male finches. The following results summarize the least-squares regression fit of this model:  \begin{array}{l} \begin{array}{lrr} \text { Predictor } & \text { Coef } & \text { SE Coef } \\ \text { Constant } & 35.79 & 16.95 \\ \text { BodyWeight } & 2.826 & 1.030 \\ \text { Aviary0Wild1 }-10.453 & 4.996 \end{array}\\ \mathrm{S}=9.62172 \quad \mathrm{R}-\mathrm{Sq}=30.2 \% \end{array}    \begin{array}{l} \text { Analysis of Variance }\\ \begin{array}{lrrr} \text { Source } & \text { DF } & \text { SS } & \text { MS } \\ \text { Regression } & 2 & 879.79 & 439.90 \\ \text { Residual Error } & 22 & 2036.71 & 92.58 \\ \text { Total } & 24 & 2916.50 & \end{array} \end{array}  What percent of variation in tail-feather length can be explained by the regression model? The following model is proposed for predicting the length of the R1 feather from the bird's weight and origin: R1Lengthi = β0 + β1 (BodyWeighti) + β2 (Aviary0Wild1i) + εi Where the deviations εi were assumed to be independent and Normally distributed with mean 0 and standard deviation σ . This model was fit to the sample of 25 male finches. The following results summarize the least-squares regression fit of this model: Predictor Coef SE Coef Constant 35.79 16.95 BodyWeight 2.826 1.030 Aviary0Wild1 -10.453 4.996 =9.62172-=30.2\% Analysis of Variance Source DF SS MS Regression 2 879.79 439.90 Residual Error 22 2036.71 92.58 Total 24 2916.50 What percent of variation in tail-feather length can be explained by the regression model?

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A study looked for a protein marker that might help predict active flare-ups in a particular chronic disease. A sample of 32 individuals with the disease had some blood taken to determine their disease status (active flare-up or inactive) and the relative abundance of the protein marker in their blood. We use logistic regression to model disease status as a function of relative abundance. Software gives the following information: For Log Odds of Active/Inactive Term Estimate Std Error Intercept -6.186 2.416 Relative abundance 6.229 2.651 We want to know if the slope of the logistic regression model is significant. What is the value of the test statistic?

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