QUESTION 8Which of the following is the most appropriate line equation obtainedby linear regression for certain x values versus y measurement values?Xy18.54210.5634513.42 16.23 18.66621.3QUESTION 9in question 8 What is the correlation coefficient of the linearregression performed in the question?

Answered: Image /qna-images/answer/152b4ff9-b1b4-49f7-9be6-beae5c88b159.jpg

Answered: QUESTION 8 Which of the following is…

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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You have been asked to analyse a suspected relationship between tutorial attendance and final marks for a university course. The latest set of data includes final marks Y (between 0 and 100) and tutorial attendance X (between 0 and 12) for 65 students. You have done a regression analysis on the dataset in R and the output is given below, along with a scatter plot of X and Y. Answer the subsequent questions regarding this analysis. 8. 2 4 6 8 10 12 Tutorials Attended Figure 3: Q3) Scatterplot of marks against tutorial attendance. Estimate Std. Error t value Pr(> |t|) (Intercept) 44.1517 4.6207 9.555 7.17e-14 X 2.7914 0.5679 4.915 6.65e-06 Table 2: Q10) Regression summary. 000 00 00 00 O O0 O OO 00 O O o o 08 09 40 Final Mark
Data from the United Nations on individual countries Obesity rates and vegetable consumption by country were organized into the scatter plot below. Linear Regression Equation for this data: y = -0.8449x + 50.328 Correlation Coefficient: 0.5 --- The data on this table can be simplified into a single statement. Which of the following statements would be the most accurate based on the chart, equation, and correlation coefficient? Question 10 options: As vegetable consumption goes up, obesity rates would tend to decrease. As vegetable consumption goes down, obesity rates would tend to increase. As vegetable consumption goes up, obesity rates would tend to increase. As vegetable consumption goes down, obesity rates would tend to decrease.
The following is the simple regression results based on the data from Madrigal Corporation: Dependent variable – Machine maintenance costs Independent variable – Machine hours Computed values Intercept P 3,500 Coefficient on independent variable P 3.50 Coefficient of correlation 0.856 R2 0.733 What percentage of the variation in maintenance costs is explained by the independent variable? (use two decimal places, e.g. 82.20%, 55.31% or 0.82, 0.55, these will be counted as correct answers)
# 1 Given the data as shown in the table below X Y 3.8 | 9.8 2.5 8.8 4.1 9.7 0.9 7.5 2.4 9.6 The correct equation of the "Best-Fit"/Regression line associated with the given data above would be Oŷ = 0.6742z + 7.2326 Oy = 0.6742z + 7.2326 Oy = 0.67427 + 7.2326 Oŷ = 0.67427 + 7.2326 Oŷ = 7.23262 + 0.6742 Note: Round all coefficients to four decimal places.
Palease ans only 10 questions
Data from the United Nations on individual countries Obesity rates and vegetable consumption by country were organized into the scatter plot below. Linear Regression Equation for this data: y = -0.8449x + 50.328 Correlation Coefficient: 0.5 Question 8 options: Strongly Correlated Weakly Correlated Not correlated Moderately Correlated
Measles cases 43 140 71 63 212 Mumps cases 800 454 1991 2612 370 For the above question; a. Test the significance of the correlation coefficient at = 0.05, using Table I. b. Give a brief explanation of the type of relationship. c. Find the equation of the regression line. d. Identify what is the slope of the regression line and what is the y-intercept.
3. Regression analysis breaks scores on the DV into... (explain and give equations)
Item9 eBook Item 9 The following regression output was obtained from a study of architectural firms. The dependent variable is the total amount of fees in millions of dollars. Predictor Coefficient SE Coefficient t p-value Constant 9.601 3.153 3.045 0.010 x1 0.221 0.110 2.009 0.000 x2 − 1.168 0.577 − 2.024 0.028 x3 − 0.106 0.073 − 1.452 0.114 x4 0.631 0.361 1.748 0.001 x5 − 0.043 0.029 − 1.483 0.112 Analysis of Variance Source DF SS MS F p-value Regression 5 1,844.31 368.9 9.37 0.000 Residual Error 56 2,205.62 39.39 Total 61 4,049.93 x1 is the number of architects employed by the company. x2 is the number of engineers employed by the company. x3 is the number of years involved with health care projects. x4 is the number of states in which the firm operates. x5 is the percent of the firm’s work that is health…

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