Consider the following econometric modelbirthweight = Bo+B₁smoking + uwhere birthweight is a baby's weight at birth in grams and smoking is a dummy variable indicating whether the mother wassmoking during pregnancy (smoking = 1) or not (smoking = 0).What does B1 show?a.an estimate of the causal effect of smoking on birth weightb. the causal effect of smoking on birth weightC. the birth weight of a baby with a smoking motherd.the causal effect of birth weight on smoking

Consider the following econometric model birthweight = β0 + β1 smoking + u where birthweight is…

Answered: Consider the following econometric…

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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1. The Gross Horsepower of a car can influence the mileage of the car. A random sample of 32 different cars’ Gross Horsepower and mileage (Miles/gallon) is collected. From the data, suppose you got the following results: Pearson’s correlation coefficient: -0.90 The fitted regression line: Ŷ = 30. 10 − 0. 07 X a. Identify the Independent and dependent variable. Give a justification of your answer.b. Interpret the value Pearson’s correlation coefficient. c. From the fitted regression line interpret the slope parameter. d. Find the value of R and comment. e. From the fitted model find the values of Y for given values of X as 110. How do you interpret this value?
A company wishes to estimate a regression line for the relationship between sales and advertising. а. To arrive at its decision regarding the best model to use, the company has calculated the following three correlation coefficients. i. ii. The sales in any month depend on that month's advertising with r = 0.28 The sales in any month depend on 50% of the previous month's advertising and 50% of that month's advertising with r= 0.68 The sales in any month depend on the previous month's advertising with r = 0.92 iii. Interpret each of the above correlation coefficients and state which of the suggested models you would choose as the basis for predicting sales. Justify your answer.
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5. The National Center for Health Statistics published data on heights and weights. We obtained the following data from 10 randomly selected males 8-12 years of age. * y Height 69 Weight 151 72 154 70 160 200 Weight go IGO Regression Equation: Correlation r = 67 153 140 (20 O 75 201 66 126 a. If the researcher uses "Height" to predict "Weight" then the response variable is weight b. Use your calculator to create a scatter plot. Copy the scatter plot below (draw it). Make sure to label the horizontal axis and vertical axis, and provide the minimum and maximum values (displayed on your graph on your calculator) on each axis.. 70 174 71 185 60 70 Height 80 c. Is the association between the two variables positive or negative, or no association? (circle one) 68 143 g. Identify the slope of the regression line: and mention both men's heights and weights.)" d. Use your calculator to find the regression line (or line of best fit) and the correlation r. (Make sure something has a hat on it.) 65…
8. For the following data: a. Find the regression equation for predicting Y from X. b. Calculate the Pearson correlation for these data. Use r and SS, to compute SS standard error of estimate for the equation. and the residual Y 3 3 6. 9. 8 4 3 7 10 9.
Assuming that the dependent variable is coping skills in difficult situations, which of the following statements is incorrect? B Std Error Beta t Sig -.466 .241 1.036 .302 .200 .048 .140 2.082 .030 .950 .087 .740 10.97 .000 a. Problem solving has a regression coefficient of 0.20. Therefore as problem solving increases by one unit coping skills increases by 0.20. b. As motivation increases by one unit coping skills increases by 0.95. c. If problem solving and motivation were both zero, coping skills in difficult situations would decrease by 0.466. d. At the 5% level of significance, the constant term, problem solving and motivation are all significant to the model.
For the following data: a. Find the regression equation for predicting Y from X. b. Calculate the Pearson correlation for these data. Use r2 and SS_Y to compute SSresidual and the standard error of estimate for the equation. Y 3 3 4 3 7 10 5 9.
27. What percentage of the variability is accounted for by the regression model?A. 1% B. 2% C. 98% D. 99%28. Is there a linear relationship between the dependent variable and the independent variables?A. Cannot be knownB. MaybeC. NoD. Yes29. What will be the value of y if x = 0?A. -12.87 B. -0.08 C. 0.70 D. 0.99
Demand for soccer balls at a new sporting goods store is forecasted using the following regression equation: Y = 98 + 2.2X where X is the number of months that the store has been in existence. Let April be represented by X = 4. April is assumed to have a seasonality index of 1.15. What is the forecast for soccer ball demand for the month of April (rounded to the nearest integer)? 1) None of the provided options.2) 1073) 1234) 1155) 100
After gathering data about the number of starfish and measuring the pollution in areas of the ocean you find a negative linear correlation between pollution levels and number of starfish. What can you conclude based on this information? a. There is a confounding variable that is affecting both pollution and starfish. b. As pollution rises the number of starfish falls c. That pollution is causing starfish to die, leading to the negative correlation d. That pollution is supporting starfish, leading to the negative correlation
b. Consider the following data, Study Hours (Y) Sleeping Hours (X) 2 4 8. 10 13 7 10 8 7 7 i) Calculate and analyze the fitted regression line between the number of study hours and the number of sleeping hours of different intakes of CSE students. ii) Find the coefficient of determination and interpret your data. iii) Predict study hour when he/she sleeps 11 hours.

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