13. A standard logistic regression models the effect of age (20-29, 30-39, 40-49, 50-59, 60-69) on experience of back pain (modeling yes vs. no). If age is included as a continuouscovariate (with values 1, 2, 3, etc., as scores) and the regression parameter associated withage is estimated to be 0.21 with standard error of 0.08, what are the odds of backpain fora 35-year-old (e.g., in the 30-39 range) relative to the odds for a 25-year-old? Enter youranswer to two numbers after the decimal place.

Step 1:Step 2: For a 35-year-old, the odds of experiencing back pain are about 1.23 times higher…

Answered: 13. A standard logistic regression…

Advanced Engineering Mathematics

10th Edition

ISBN:9780470458365

Author:Erwin Kreyszig

Publisher:Erwin Kreyszig

Chapter2: Second-order Linear Odes

Section: Chapter Questions

Problem 1RQ

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A researcher interested in explaining the level of foreign reserves for the country of Barbadosestimated the following multiple regression model using yearly data spanning the period 2001 to2016:??=?+????+????+????Where FR = yearly foreign reserves ($000’s), OIL = annual oil prices, EXP = yearly total exports($000’s) and FDI = annual foreign direct investment ($000’s). The sample of data was processedusing MINITAB and the following is an extract of the output obtained: Predictor Coef StDev t-ratio p-valueConstant 5491.38 2508.81 2.1888 0.0491OIL 85.39 18.46 4.626 0.0006EXP -377.08 112.19 * 0.0057FDI -396.99 160.66 -2.471 ** S = 2.45 R-sq = 96.3% R-sq(adj) = 95.3% Analysis of VarianceSource DF SS MS F pRegression 3 1991.31…
Suppose that you are modelling cooling costs (y) as a function of the daily high temperature (x) for a building, using a simple linear regression model. You have data consisting of pairs of observations (x, y) over many years. Suppose that from this data you find a Pearson correlation between x and y of 0.5. If tomorrow the temperature is 3 standard deviations above the mean, how many standard deviations above the mean would you expect the cooling costs to be? If it is true that, tomorrow, the temperature is as given in the previous problem, then the expectation that you gave is the exact cooling cost that will occur. True O False
Many high school students take either the SAT or the ACT. However, some students take both. Data were collected from 100 students who took both college entrance exams. The average SAT score was 920 with a standard deviation of 170. The average ACT score was 20 with a standard deviation of 5. The correlation coefficient r between these two variables equals 0.82. To predict the SAT score from a student’s ACT score, what is the equation of the least-squares regression line? Please show your work.
What is Regression toward the Mean?
Is the regression equation for predicting Y in terms of X the same as the regression line for predicting X in terms of Y?
A researcher interested in explaining the level of foreign reserves for the country of Barbadosestimated the following multiple regression model using yearly data spanning the period 2001 to2016: ??=?+????+????+???? Where FR = yearly foreign reserves ($000’s), OIL = annual oil prices, EXP = yearly total export ($000’s) and FDI = annual foreign direct investment ($000’s). The sample of data was processed using MINITAB and the following is an extract of the output obtained: Predictor Coef StDev t-ratio p-valueConstant 5491.38 2508.81 2.1888 0.0491OIL 85.39 18.46 4.626 0.0006EXP -377.08 112.19 * 0.0057FDI -396.99 160.66 -2.471 **S = 2.45 R-sq = 96.3% R-sq(adj) = 95.3% Analysis of VarianceSource…
Suppose there is a significant correlation between two variables. Describe a case under which it might be inappropriate to use the linear regression equation for prediction.
A 10-year study conducted by the American Heart Association provided data on how age, blood pressure, and smoking relate to the risk of strokes (Dataset "Stroke"). Risk is interpreted as the probability (times 100) that a person will have a stroke over the next 10-year period. For the smoker variable, 1 indicates a smoker and 0 indicates a nonsmoker. a. Develop an estimated regression equation that can be used to predict the risk of stroke given the age and blood-pressure level. b. Consider adding two independent variables to the model developed in part (a), one for the interaction between age and blood-pressure level and the other for whether the person is a smoker. Develop an estimated regression equation using these four independent variables. c. At a 0.05 level of significance, test to see whether the addition of the interaction term and the smoker variable contributes significantly to the estimated regression equation developed in part (a). d. Refer to the model developed in part…
You are analyzing a dataset with 932 datapoints. You decide to create a linear regression model with this dataset, using 18 predictor variables. Using this information, what is the degrees of freedom associated with the sum of squares total (total sum of squares) of your analysis?
Determine the new equation after rotated parallel to the principal axes for the equation below and also determine the angle of rotation! 2x^2 + 4xy − y^2 = 24
Low-Density Lipoprotein (or LDL) is considered the "bad" cholesterol because it forms plaques in arteries, raising the risk for heart attack and stroke. Suppose you are interested in predicting LDL (in mg/dL) using one's age (in years). Assume that LDL levels and age are normally distributed and that the correlation between the two variables is 0.534. Based solely on the regression effect, a person whose age is at the 60th percentile is predicted to have an LDL level ____. [Source: Ldl cholesterol: Definition, risks, and how to lower it. (2020, March 09). Retrieved from https://www.webmd.com/heart-disease/ldl-cholesterol-the-bad-cholesterol] Question 4 options: A) above the 60th percentile B) exactly at the 60th percentile C) exactly at the 40th percentile D) I need more information to answer this E) below the 60th percentile
True/false (please explain). Suppose that a regression of Y on X₁ is unbiased, and the true slope coefficient is 2. Another variable X2 is correlated with Y, but it is uncorrelated with X₁. In expectation, the default t-statistic on B₁ will be larger in the multivariate regression that includes X₂ than in the bivariate regression that omits X₂.

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