. QUESTION 7When considering the regression modely,=P+P,+7, +P",+u,Which of the above will not be evidence of heteroscedasticity in the error terms?A statistically significant relationship between the residuals of the model and xA statistically significant relationship between the squared residuals of the model and xA statistically significant relationship between the squared residuals of the model and In (x)The question is flawed, all three of the above indicate the presence of heteroscedasticity.

The Heteroscedasticity can also be identified by constructing residual plots between the explanatory…

Answered: When considering the regression model…

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

Section1.2: The Least Square Line

Problem 8E

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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?
If your graphing calculator is capable of computing a least-squares sinusoidal regression model, use it to find a second model for the data. Graph this new equation along with your first model. How do they compare?
Respiratory Rate Researchers have found that the 95 th percentile the value at which 95% of the data are at or below for respiratory rates in breath per minute during the first 3 years of infancy are given by y=101.82411-0.0125995x+0.00013401x2 for awake infants and y=101.72858-0.0139928x+0.00017646x2 for sleeping infants, where x is the age in months. Source: Pediatrics. a. What is the domain for each function? b. For each respiratory rate, is the rate decreasing or increasing over the first 3 years of life? Hint: Is the graph of the quadratic in the exponent opening upward or downward? Where is the vertex? c. Verify your answer to part b using a graphing calculator. d. For a 1- year-old infant in the 95 th percentile, how much higher is the walking respiratory rate then the sleeping respiratory rate? e. f.
Find the equation of the regression line for the following data set. x 1 2 3 y 0 3 4
What type of standard errors would you use to estimate regression (5)? Does your inference about the parameters change if you use different standard errors?
The individual residual scores from a sample of participants regarding the difference between the predicted Y values from a regression equation and the actual Y from the data are provided here. Y - Ŷ = 3,8,1,2,2. What is the value for the standard error of estimate?
True or false: “If the errors in a regression model contain ARCH, they must be serially correlated.”
Is the regression equation for predicting Y in terms of X the same as the regression line for predicting X in terms of Y?
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.
Which of the following statements are not correct? O The coefficient of determination, denoted by r^2 is interpreted as the proportion of observed y variation that cannot be explained by the simple linear regression model. O The higher the value of the coefficient of determination, the more successful is the simple linear regression model in explaining y variation. If the coefficient of determination is small, an analyst will usually want to search for an alternative model (either a nonlinear model or a multiple regression model that involves more than a single independent variable). The coefficient of determination can assume any value between 0 and 1, inclusive.
What steps should be taken (mention all of them) in order to successfully specify/estimate a regression equation?
True or false (Explain why.): A school administrator is running a regression of fall quarter final grades (Y ) on undergraduate grades (X). Since he only has data on graduate students, he does not have people who performed poorly in their undergraduate studies. His sample is therefore nonrandom (selection on X) and her estimates will be biased. True or false (Explain why): Because the IV estimator uses only a subset of the variation in an explanatory variable, precision always increases when compared to OLS.

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