When making predictions based on regression lines, which of the following is not listed as a consideration? Choose the correct answer below. O A. Use the regression equation for predictions only if the graph of the regression line on the scatterplot confirms that the regression line fits the points reasonably well. O B. Use the regression line for predictions only if the data go far beyond the scope of the available sample data. OC. If the regression equation does not appear to be useful for making predictions, the best predicted value of a variable is its point estimate. O D. Use the regression equation for predictions only if the linear correlation coefficient r indicates that there is a linear correlation between the two variables.

When making predictions based on regression lines, which of the following is not listed as a consideration? Choose the correct answer below. O A. Use the regression equation for predictions only if the graph of the regression line on the scatterplot confirms that the regression line fits the points reasonably well. O B. Use the regression line for predictions only if the data go far beyond the scope of the available sample data. OC. If the regression equation does not appear to be useful for making predictions, the best predicted value of a variable is its point estimate. O D. Use the regression equation for predictions only if the linear correlation coefficient r indicates that there is a linear correlation between the two variables.

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

See similar textbooks

Similar questions

Simple Linear Regression
Use the given data to find the equation of the regression line. Examine the scatterplot and identify a characteristic of the data that is ignored by the regression line. 10 9. 11 9. 11 13 4 11 8 У 7.67 7.07 12.74 6.81 7.75 8.44 6.15 5.63 7.75 6.13 5.84 ..... Create a scatterplot of the data. Choose the correct graph below. O A. О В. С. D. Ay 25- Ay 25- Ay 25- Ay 25- 20- 20- 20- 20- 155 15- 155 15- 10- 10- 10- 10- 5- 5- 5- 5- X X X 0+ 0 5 0- 0- 05 0- 05 10 15 20 25 5 10 15 20 25 10 15 20 25 10 15 20 25 Find the equation of the regression line. y = + (Round the y-intercept two decimal places as needed. Round the slope to three decimal places as needed.) Identify a characteristic of the data that is ignored by the regression line. O A. There is no trend in the data. B. The data has a pattern that is not a straight line. C. There is no characteristic of the data that is ignored by the regression line. D. There is an influential point that strongly affects the graph of the regression line.
Follow the 8 steps in regression analysis to answer the following problem.STEP 1: Identify the dependent and independent variableSTEP 2: Compute the correlation coefficient rSTEP 3: Test the significance of rSTEP 4: Decision and ConclusionSTEP 5: Computation of values of b and mSTEP 6: Form the Regression EquationSTEP 7: Prediction and ConclusionSTEP 8: Graph the scatter plot and regression equation
Simple regression analysis means that A. the data are presented in a simple and clear way B. we have only a few observations C. there are only two independent variables D. we have only one explanatory variable
. In the following multiple-choice questions, select the best answer. 1. The correlation coefficient is used to determine: a. A specific value of the y-variable given a specific value of the x-variable b. A specific value of the x-variable given a specific value of the y-variable c. The strength of the relationship between the x and y variables d. None of these 2. Regression modeling is a statistical framework for developing a mathematical equation that describes how a. One explanatory and one or more response variables are related b. Several explanatory and several response variables response are related c. One response and one or more explanatory variables are related d. All of these are correct. 3. The coefficient of correlation a. Is the square of the coefficient of determination b. Is the square root of the coefficient of determination c. Is the same as r-square d. Can never be negative
could tou write with pen and paper?
12. A consumer advocacy group recorded several variables on 140 models of cars. The resulting information was used to produce the following regression output that relates the city gas mileage (in mpg) and the engine displacement (in cubic inches). The regression equation is mpg:city= 33.4 0.0624 displacement Predictor Constant SE Coef T P 0.7762 43.00 0.000 displacement -0.0624 0.003810 0.000 S 3.13923 = R-Sq 66.0% R-Sq (adj) = 65.8% a. We have a car that has an engine with 150 cubic inches. Based on this output, what city gas mileage would you predict for this car? Coef 33.4 b. Based on this output what is the correlation between city gas mileage and displacement? C. I value: The test statistics for testing the slope is zero is missing. Calculate this The group o also recorded the power of the engine (in horsepower) for each car. The following regression output was produced. Predictor Constant horsepower The regression equation is mpg:city = 32.2 0.0572 horsepower Coef SE Coef 32.2 T…
. A professor at the University of Alabama was interested in evaluating the relationship between family support and delinquency. Using data collected on 4545 families, the researcher used regression to analyze the relationship. The results are presented below. Variables Entered/Removeda Model Variables Entered Variables Removed Method 1 Family supportb . Enter a. Dependent Variable: Delinquency b. All requested variables entered. Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .249a .062 .062 1.59168 a. Predictors: (Constant), Family support ANOVAa Model Sum of Squares df Mean Square F Sig. 1 Regression 759.204 1 759.204 299.671 <.001b Residual 11479.107 4531 2.533 Total 12238.311 4532 a. Dependent Variable: Delinquency b. Predictors: (Constant), Family support…
Which of the following statements is incorrect? Select one: a. The coefficient of determination is defined as r². b. If r²=0.70, it implies that 70% of the variation in Y is explained by the regression line. c. If r=0.64 then r²=0,4096. O d. r indicates the strength and the direction of the relationship between X and Y variables. O e. The coefficient of correlation r can never be negative.
Find the equation of the regression line for the given data. Then construct a scatter plot of the data and draw the regression line. (The pair of variables have a significant correlation.) Then use the regression equation to predict the value of y for each of the given x-values, if meaningful. The table below shows the heights (in feet) and the number of stories of six notable buildings in a city. Height, x Stories, y Un ne conteui yrapii veIUW. O A. Stories A 60- Height (feet) OO ( 800 (a) Predict the value of y for x = 503. Choose the correct answer below. A. 40 OB. 47 O C. 50 O D. not meaningful (b) Predict the value of y for x = 652. Choose the correct answer below. 774 51 OA. 54 O B. 47 OC. 40 O D. not meaningful (c) Predict the value of y for x = 810. Choose the correct answer below. OA. 54 625 47 OA. 54 OB. 50 OC. 47 O D. not meaningful (d) Predict the value of y for x = 725. Choose the correct answer below. B. 40 ΕΛ 521 45 O B. Stories 60+ 508 41 0 Height (feet) 800 497 38 477…
Use the given data to find the equation of the regression line. Examine the scatterplot and identify a characteristic of the data that is ignored by the regression line. 11 7 13 8 12 14 4 12 8 5 У 7.53 6.85 12.72 7.2 7.45 8.44 6.25 5.14 7.88 6.73 5.85 Create a scatterplot of the data. Choose the correct graph below. O A. B. C. D. Ay 25- Ay 25- Ay 25- Ay 25- 20 20- 20 20- 15 155 15 15 10 10- 105 105 5- 55 5- 55 0- 0- 0- 0- 10 15 20 25 10 15 20 25 5 10 15 20 25 5 10 15 20 25 Find the equation of the regression line. (Round the constant two decimal places as needed. Round the coefficient to three decimal places as needed.)
Suppose a doctor measures the height, x, and head circumference, y, of 8 children and obtains the data below. The correlation coefficient is 0.941 and the least squares regression line is y = 0.244x + 10.794. Complete parts a and b below. Height, x Head Circumference, y 27.00 25.75 26.25 17.3 25.75 27.50 17.5 26.25 17.1 26.00 27.00 17.4 17.4 17.1 17.1 17.1 (a) Compute the coefficient of determination, R?. R2 =% (Round to one decimal place as needed.) (b) Interpret the coefficient of determination. % of the variation in height is explained by the least-squares regression model. (Round to one decimal place as needed.)