Problem 5a. Suppose that a time-series regression model, y~ x₁ + t, was fitted to 40 data points, and theDurbin-Watson statistic was found as 3.28. Are the error terms independent? Apply the Durbin-Watson test (state the hypothesis, critical values, conclusion, etc.) to test positive and/or negativecorrelation.b. A linear regression analysis for a transformed model was studied: y* =y¹/³. If a 95%prediction interval for y* is (2.0, 3.0), find the 95% prediction interval for y.c. True or False? The presence of unusual observations may affect the slope of the regressionline as well as the significance of the independent variable.d. True or False? The presence of multicollinearity affects the sizes and the signs of coefficients,but does not affect the significance of the independent variables.e. True or False? Suppose you solved a first-order linear regression model: y~ x₁ + x2 + x3 + x4.If VIF values of those independent variables are 1.33, 18.22, 18.69, and 1.62, respectively,multicollinearity will be detected.

a. Durbin-Watson Test:Hypotheses:Null Hypothesis (H0): No serial correlation (error terms are…

Answered: Problem 5 a. Suppose that a time-series…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

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A pharmaceutical company is examining the relationship between dosage of a new pain-relief medication and the time it takes for patients to experience pain relief from a headache. Various dosages ranging from 200 to 1,200 milligrams (mg) in 100-milligram increments were given to 41 randomly selected patients, and the time until relief was measured in minutes. A check of the conditions necessary for inference for the slope of a regression line shows that they are met. A 95 percent confidence interval for the slope of the regression line relating the dosage, x, to the time until relief, y, is given by (−4.15,−2.27). Based on the confidence interval, which of the following claims is supported?
7. If a person's score on a questionnaire has been found to predict observed social skills, and the linear prediction rule uses a regression constant of 16 and a regression coefficient of 3, the predicted level of soci skills for a person with a score of 10 on the questionnaire is a. 10 b. 46 C. 160 d. 190
Where we observe multicollinearity in a multiple regression analysis, two of our independent variables, x-1 and x-4, are so highly correlated they’re almost indistinguishable with respect to their relationship with our dependent (y) variable. Why is that a problem?
If the R-squared for a regression model relating the outcome y to an explanatory variable x is 0.9. This implies that there is a positive linear relationship between y and x. True or false?
Suppose that you run a regression of Y, on X, with 110 observations and obtain an estimate for the slope. Your estimate for the standard error of ₁ is 1. You are considering two different hypothesis tests: The first is a one-sided test: Ho: B1-0, Ha: 31>0, a = .05 The second is a two-sided test: Ho: 31-0, Ha: B1 0,a = .05 (a) What values of , would lead you to reject the null hypothesis in the one-sided test? (b) What values of , would lead you to reject the null hypothesis in the one-sided test? (c) What values of would lead you to reject the mill hypothesis in the one-sided test, but not the two-sided test? (d) What values of 3 would lead you to reject the null hypothesis in the two-sided test, but not the one-sided test?
There may be an association between a country's birthrate and the life expectancy of its inhabitants. A report this past year, coming from a random sample of 20 countries, contained the following information: the least-squares regression equation relating the two variables number of births per one thousand people (denoted by x) and female life expectancy (denoted by y and measured in years) is y = 82.28 – 0.51 x, and the standard error of the slope of this least-squares regression line is approximately 0.35. Based on this information, test for a significant linear relationship between these two variables by doing a hypothesis test regarding the population slope B,. (Assume that the variable y follows a normal distribution for each value of x and that the other regression assumptions are satisfied.) Use the 0.10 level of significance, and perform a two-tailed test. Then complete the parts below. (If necessary, consult a list of formulas.) (a) State the null hypothesis H, and the…
A pharmaceutical company is examining the relationship between dosage of a new pain-relief medication and the time it takes for patients to experience pain relief from a headache. Various dosages ranging from 200 to 1,200 milligrams (mg) in 100-milligram increments were given to 41 randomly selected patients, and the time until relief was measured in minutes. A check of the conditions necessary for inference for the slope of a regression line shows that they are met. A 95 percent confidence interval for the slope of the regression line relating the dosage, x, to the time until relief, y, is given by (−4.15,−2.27). Based on the confidence interval, which of the following claims is supported? A 100 mg increase in dosage results in a decrease in the time until relief by more than two minutes on average. A A 100 mg increase in dosage results in a decrease in the time until relief by more than four minutes on average. B There is not a linear relationship…
Suppose x1 ans x2 are predictor variables for a response variable y. a. The distribution of all possible values of the response variable corresponding to particular values of the two predictor variables is called a distribution of the response variable. b. State the four assumptions for multiple linear regression inferences.
4. A simple linear regression is fit to a dataset. Unfortunately, the corresponding ANOVA table is not complete because some quantities in the table are missing due to unknown digital errors. Calculate the missing values, denoted by "?", in the ANOVA table based on the other available values. Is this regression model significant (α = 0.05)? Source Regression Residual Total df ? ? 21 SS ? ? ? MS = SS/df ? 1.13 F-Ratio 430.65 p-value ?
8. In an investigation to empirically determine the value of 1, a student measures the circumference and diameter of several circles of varying size and uses Excel to make a linear plot of circumference versus diameter (both in units of meters). A linear regression fit yields the result of: y = 3.1527x - 0.0502, with R2= 0.9967 for the 5 data points plotted. How should this student report the final result? Does the empirical ratio of C/D agree with the accepted value of n?
A company studying the productivity of its employees on a new information system was interested in determingg if the age (X) of data entry opeertors influenced the number of completed entries made per hour (Y). The regression equation is y = 14.374 - 0.145x Suppose the acyual completed entries per hour for an operator who is 35 years old was 8. The residual is:
Suppose we carry out a linear regression analysis with the following model: Y = Bo + B1X1 + .. + BrXr + € where k = 8. n = 195. We find SSReg = 231.4, and SSTotal = 972.5. Before collecting the data, we knew that we'd like to test the null hypothesis that Bi = B2 = 63 = 0. If we run a regression model leaving the predictors associated with these slopes out of the model, we find SSReg = 74.1 and SSTotal = 972.5. What is the value of the appropriate Fstatistic to test this null hypothesis (that these 3 slopes are 0)?

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