3. Which of the following is not correct?a) a high R2 does not mean that the regression is correctb) a high R2 does not mean that there is no omitted variable biasc) one should always choose a regression with a higher R2.d) a high R2 does not necessarily mean that you have the most appropriate set ofregressors

The correct option is: (c) One should always choose a regression with a higher R2. The R2 value…

Answered: 3. Which of the following is not…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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A) A multiple regression model was used in production the speed of a car based on several factors known to affect the speed. A graph of the residuals for the predicted values is presented below. i) Discuss the relevance of the graph shown below in relation to the normality of predicted values. 6.00000- 4.00000- 225 2.00000 00000- -2.00000- 424 164 226 227 O163 -4.00000- Standardized Residual (b) The diameter of iron rods issued in a high rising building pillars are under investigation. The diameter for Eleven rods were measured and the following results are obtained: Days 14.5 16.0 15.4 16.3 15.4 15.9 15.5 14.9 15.7 16.0 15.9 i. Determine The Interquartile range of the data ii. Determine a measure to describe the asymmetry of the data set.
3. A Ross MAP team is trying to estimate the revenues of major-league baseball teams during the regular season using a regression model. Currently, the independent variables include stadium capacity, the number of weekend games, the number of night games, and the number of Wins (out of 162 regular season games). One of your team members suggests that the model also should include the number of losses as it provides additional explanatory power. Assume that ties are not possible; so every game results in exactly one team winning and the other team losing. Which of the following statements is the most likely conclusion of the new regression model? (a) R2 will increase, adjusted R2 will decrease, and Serror will decrease. (b) R2 and adjusted R2 will increase, and serror will decrease. (c) R, adjusted R2, and Serror will increase. (d) We cannot trust the regression output as some variables are highly correlated, resulting in multicollinearity. Answer to Question 3:
2.1. Give one word for the following statements or scenarios: 2.1.1. The data collected by the researcher from the Department of Education were the 2020 matric results for all South African high schools in disadvantaged areas.. 2.1.2. This analysis can be performed using either the method of moving averages, or by fitting a straight line using the method of least squares from regression analysis.
5) What is true about a regression using data from a randomized controlled trial? a) The independent variable is exogenous because it was determined randomly. b) The line of best fit does not minimize the sum of squared residuals. c) It eliminates all error due to unmeasured variables. d) The estimated slope is significant no matter how many observations are in the sample. e) The slope of the line of best fit must be equal to the true relationship.
2. Consider the foot length and foot width of six female grade 4 students in Morning Star Montessori School in 2006: Length of foot (cm) Y 20.9 Width of foot (cm) X 8.5 24 9 19.6 7.9 22.6 8.8 21 8.8 21.6 9.3 c) What is the least squares regression line for Y as a function of X?
17) Suppose that Y is normal and we have three explanatory unknowns which are also normal, and we have an independent random sample of 41 members of the population, where for each member, the value of Y as well as the values of the three explanatory unknowns were observed. The data is entered into a computer using linear regression software and the output summary tells us that R-square is 0.9, the linear model coefficient of the first explanatory unknown is 7 with standard error estimate 2.5, the coefficient for the second explanatory unknown is 11 with standard error 2, and the coefficient for the third explanatory unknown is 15 with standard error 4. The regression intercept is reported as 28. The sum of squares in regression (SSR) is reported as 90000 and the sum of squared errors (SSE) is 10000. From this information, what is the number of degrees of freedom for the t-distribution used to compute critical values for hypothesis tests and confidence intervals for the individual…
Suppose a doctor measures the height, x, and head circumference, y, of 8 children and obtains the data below. Thecorrelation coefficient is 0.944 and the least squares regression line is y = 0.199x + 11.982. Complete parts (a) and (b)below.Height, x27.5 25.5 26.25 25.25 27.5 26.25 26 27.25 27.25 27 27.25 ФHead Circumference, # 17.5 17.0 17.2 17.0 17.5 17.3 17.2 17.4 17.3 17.3 17.4(a) Compute the coefficient of determination, R?R?.% (Round to one decimal place as needed.)(b) Interpret the coefficient of determination and comment on the adequacy of the linear model.Approximately % of the variation inis explained by the least-squares regression model.According to the residual plot, the linear model appears to be (Round to one decimal place as needed.)
Data on 11 randomly selected athletes was obtained concerning their cardiovascular fitness (measured by time to exhaustion running on a treadmill) and performance in a 20-km ski race. Both variables were measured in minutes and a regression analysis was performed. Ski 88 2.5 . Treadmill Coefficients Estimate Std. Error 0.41 (Intercept) Treadmill 1.11 88 -2.5 Is there sufficient evidence to conclude that there is a linear relationship between cardiovascular fitness and ski race performance? (a). The test statistic is (b). The p-value is (use four decimal places) (use four decimal places) (c). At the 2% significance level, we Select an answer the null hypothesis and conclude that there is Select an answer evidence to conclude that there is a linear relationship between cardiovascular fitness and ski race performance.
x 5.7 4.1 6.2 4.4 6.5 5.8 4.9 y 1.9 4.8 0.8 3.9 1.2 1.7 3.0 (a) Computethecoefficientofdetermination. (b) Howmuchofthevariationintheoutcomevariableisexplainedbytheleast-squares regression line?
I have a doubt when it comes to this reasoning : Imagine I have a variable that is correlated to Y and to X1 in a linear regression model. If I ommit it it will result in Omitted Variable Bias but if I include it, would it result in perfect multicolinearity and therefore for example a solution is to include control variables ? Is this right ? Thanks.
a) Find a correlation coefficient r, round it to 4 decimal places. Do not round partial results. b) Interpret previous result clearly in terms of direction and strength of association. c) Find the equation of regression line. Equation of regression line: …………………………………………
In reading the results of a multiple regression analysis that contained 4 predictor variables, the researcher noticed a column labeled Beta. Two of the Beta’s were positive and two were negative. He concluded that a.) Beta’s that were positive were statistically significant b.) Beta’s that were positive had more of an effect c.) Beta’s that were positive were associated with increases in the criterion variable d.) Beta’s that were positive did not affect the criterion because they were “controlled for…”

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