Consider that in a retrospective cohort study involving job type and heart disease among railroad workers, the following data was obtained. heart disease job type yes no clerk 50 500 track worker 22 390 calculate the association between the two vari

Consider that in a retrospective cohort study involving job type and heart disease among railroad workers, the following data was obtained. heart disease job type yes no clerk 50 500 track worker 22 390 calculate the association between the two vari

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 researcher found that sales of televisions and lawn fertilizer have been increasing over the past 12 years. The sales had a strong positive linear correlation, but no evidence could be found to support a cause-and-effect relationship. Rather, this would have been purely coincidental. This is an example of a) an accidental cause-and-effect relationship b) a cause-and-effect relationship O c a reverse cause-and-effect relationship O a presumed cause-and-effect d) relationship
Suppose a study finds that the correlation coefficient relating job satisfaction to salary is r=1. Which of the following are the proper conclusions? I. High salary causes high job satisfaction. II. Low salary causes low job satisfaction. III. There is a very strong association between salary and job satisfaction. I only IIl only I and Il only I, Il and III O O
Refer to the Baseball 2018 data given below, which report information on the 30 Major League Baseball teams for the 2018 season. Let the number of games won be the dependent variable and the following variables be independent variables: team batting average, team earned run average (ERA), number of home runs and whether the team plays in the American or National league (American League is 1 and National League is 0). a. Develop a correlation matrix. (i) Which independent variables have strong or weak correlations with the dependent variable. (ii) Do you see any problems with multicollinearity? Explain your answer. b. Use Excel to determine the multiple regression equation. (i) Write out the regression equation and determine its practical application (i.e., interpret the equation). (ii) Report and interpret the R-square. c. Conduct a global test on the set of independent variables. Interpret. d. Conduct a test of hypothesis on each of the independent variables. Would…
The following data represents the grades of a sample of 12 students instatistics and economics as followsStatistics grade A C B B D B F F D C B CEconomic Grade B F C B C A C F B B A CCalculate the correlation between the student's grade in Statistics and Economics.What its type and strength.
The National Safety Council released the following data on the incidence rates for fata or lost-worktime injuries per 100 employees for eight industries in three recent years. Compute the covariance between each pair of years (2018 and 2019), (2018 and 2020) and (2019 and 2020). Compute the coefficient of correlation (r) for each pair of years and determine which years are most highly correlated.
For a data set of weights (pounds) and highway fuel consumption amounts (mpg) of seven types of automobile, the linear correlation coefficient is found and the P-value is 0.021. Write a statement that interprets the P value and includes a conclusion about linear corelation. The P-value indicates that the probability of a linear correlation coefficient that is at least as extreme is %, which is so there sufficient evidence to conclude that there is a linear correlation between weight and highway fuel consumption in automobiles. (Type an integer or a decimal. Do not round.)
Listed below are annual data for various years. The data are weights (metric tons) of imported lemons and car crash fatality rates per 100,000 population. Construct a scatterplot, find the value of the linear correlation coefficient r, and find the P-value using a = 0.05. Is there sufficient evidence to conclude that there is a linear correlation between lemon imports and crash fatality rates? Do the results suggest that imported lemons cause car fatalities? 484 534 Lemon Imports Crash Fatality Rate 229 15.8 266 15.7 359 15.5 15.3 14.8 What are the null and alternative hypotheses? OA. H₂: p=0 H₁: p0 OD. Ho: p*0 H₁: p=0 OC. Ay 17+ 16- ܕ 15 14+ HHHH ▬▬▬▬▬ % do 6 200 400 600 G O D. Aу 17- 16 :|||||||* 15- 14+ 0 H °. C M o do 200 400 600 Q C sufficient evidence to support the claim that there is a linear correlation between lemon imports and crash fatality rates for a significance level of a = 0.05.
For a data set of weights (pounds) and highway fuel consumption amounts (mpg) of twelve types of automobile, the linear correlation coefficient is found and the P-value is 0.022. Write a statement that interprets the P-value and includes a conclusion about linear correlation. The P-value indicates that the probability of a linear correlation coefficient that is at least as extreme is %, which is so there sufficient evidence to conclude that there is a linear correlation between weight and highway fuel consumption in automobiles. (Type an integer or a decimal. Do not round.)
Refer to the Baseball 2018 data given below, which report information on the 30 Major League Baseball teams for the 2018 season. Let the number of games won be the dependent variable and the following variables be independent variables: team batting average, team earned run average (ERA), number of home runs and whether the team plays in the American or National league (American League is 1 and National League is 0). a. Develop a correlation matrix. (i) Which independent variables have strong or weak correlations with the dependent variable. (ii) Do you see any problems with multicollinearity? Explain your answer. b. Use Excel to determine the multiple regression equation. (i) Write out the regression equation and determine its practical application (i.e., interpret the equation). (ii) Report and interpret the R-square. c. Conduct a global test on the set of independent variables. Interpret. d. Conduct a test of hypothesis on each of the independent variables. Would…
In a study on television violence and aggression, researchers measured the TV viewing habits of 3rd grade males (preference for violent TV shows) and their levels of aggression; and then revisited the men 10 years later and measured the same variables. They found these zero-order correlations: Aggression G3 TV G3 Aggression Y18 Aggression G3 1.0 .65 .45 TV G3 1.0 .5 Aggression Y18 1.0 a) Suggest an operational definition to measure TV show preference for violent TV shows at Grade 3 and at Year 18. b) What is going on if the partial correlation of TV G3 and AGG Y18 with AGG G3 held constant is .09? How do you know this? c) What is going on if the partial correlation of AGG G3 and AGG Y18 with TV G3 held constant is .06? How do you know this?
A study was done to look at the relationship between number of partners college students have had in their lifetimes and their GPAs. The results of the survey are shown below. Partners 7 8 6 1 6 6 4 GPA 1.4 1.5 1.4 3 2.3 1.5 2.9 Find the correlation coefficient: r=r= Round to 2 decimal places. The null and alternative hypotheses for correlation are:H0:H0: ? r ρ μ Incorrect == 0 Ha:Ha: ? r ρ μ Incorrect ≠≠ 0 Use a level of significance of α=0.05α=0.05 to state the conclusion of the hypothesis test in the context of the study. You may want to reference the 95% Critical Values of the Sample Correlation Coefficient. There is statistically insignificant evidence to conclude that there is a correlation between the number of partners students have had in their lifetimes and their GPA. Thus, the use of the regression line is not appropriate. There is statistically significant evidence to conclude that a student who has had more partners will have a lower GPA than a…