3) In a large introductory sociology course, a professor gives two exams. The professor wants to determine whether the scores studentsreceive on the second exam are correlated with their scores on the first exam. To make the calculations easier, a sample of eight students isselected. Their scores are shown in the table below.StudentExam 1Exam 2160607510070804.726854738397808586590a) Can you claim that a linear relationship exists between scores on the two exams?b) How well does the relationship account for the scores on exam 2?

The independent variable is Exam 1. The dependent variable is Exam 2. This is simple linear…

Answered: 3) In a large introductory sociology…

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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C. Give an example for each of the following: a. Two variables in which you have enough reasons to explain their cause-and-effect relationship. State the independent (x) and dependent variable (y). Provide also description of the cause-and-effect relationship. b. Two variables from which a considerable possible nature of linear correlation may exist. Explain briefly the possible nature of the relationship. State both variables and give a short description of their relationship.
The data provides information on life expectancy and the number of televisions per thousand people in a sample of 22 countries, as reported by the world almanac and book of facts 2006 A. Describe the Direction and strength of the correlation between the two variables B. Because of the association between the variables, someone might mistakenly conclude that simply sending televisions to the countries with the lower life expectancies would cause their inhabitants to live longer. Comment on this argument and the reason televisions are likely associated with longer life expectancies C. In general, if two variables are strongly associated, does it follow there must be a cause and effect relationship between them? Explain
A student wants to compute the correlation coefficient with two data pairs. What value or values of r should he expect? A. -1 B. 1 C. 0 D.|1| Given a set of paired data (X,Y), if Y is dependent on X, then what value of a correlation coefficient wouldyou expect? A. -1 B. 1 C. 0 D.|1|
A university would like to examine the linear relationship between a faculty member's performance rating (measured on a scale of 1-20) and his or her annual salary increase. The table to the right shows these data for eight randomly selected faculty members. Complete parts a and b.
Men's and women's winning national championship bowling scores are shown for a random sample of years. Is there a linear relationship between the men's and women's winning scores? Men 823 858 812 832 833 826 Women 752 754 771 736 792 763 A. There is NOT a linear relationship because the linear correlation coefficient is close to 1 or -1. B. There is NOT a linear relationship because the linear correlation coefficient is close to 0. C. There is a linear relationship because the linear correlation coefficient is close to 0. D. There is a linear relationship because the linear correlation coefficient is close to 1 or -1. Click to select your answer. ?
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An economist wants to determine whether there is a linear relationship between a country's gross domestic product (GDP) and carbon dioxide emissions. The data are shown in the table below. c. Compute and interpret the correlation coefficient. d. Compute and interpret the coefficient of determination. e. Test for the significance of the linear relationship. Use a 0.05 level of significance. State your conclusion. Hint: Your conclusion is either of the following. • There is a significant linear relationship between a country's gross domestic product (GDP) and carbon dioxide emissions. • There is no significant linear relationship between a country's gross domestic product (GDP) and carbon dioxide emissions. GDP 1.6 3.6 4.9 1.1 0.9 2.9 2.7 2.3 1.6 1.5 (trillion dollars) Carbon Dioxide Emissions 428.2 828.8 1214.2 444.6 264 415.3 571.8 454.9 358.7 573.5 (millions of metric tons)
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