Answered: 1. For Marijuana Stigmatization… | bartleby

MATLAB: An Introduction with Applications

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

Related questions

Question

Predictor
(Constant)
Agea
Age 20-22
Age 23-25
Femaleb
Race/Ethnicity
Black
Hispanic/Latino
Asian American
Other
Lifetime Drug Use
Marijuana
Powder Cocaine
Ecstasy
Opioids
Amphetamine
Exposure to Usersd
R²
TABLE 3
Linear Regression Model Results Comparing Predictors of Stigmatization toward Users of Each Drug
Marijuana
Stigmatization
b
3.13***
-0.05
-0.09
0.02
0.12
0.00
0.04
0.16†
-0.56***
-0.06
-0.07
-0.14†
-0.05
-0.09***
0.36
(SE)
(0.08)
(0.05)
(0.07)
(0.04)
(0.07)
(0.06)
(0.06)
(0.08)
(0.05)
(0.07)
(0.08)
(0.07)
(0.07)
(0.01)
Powder Cocaine
Stigmatization
b
3.23***
-0.12+
-0.21*
-0.01
0.05
-0.11
-0.01
0.14
-0.29***
-0.17
-0.11
-0.14
0.10
-0.06***
0.18
(SE)
(0.07)
(0.05)
(0.08)
(0.05)
(0.08)
(0.07)
(0.07)
(0.10)
(0.06)
(0.09)
(0.09)
(0.08)
(0.09)
(0.01)
Ecstasy
Stigmatization
b
3.14***
-0.11
-0.20*
-0.01
0.05
-0.04
0.01
0.16
-0.36***
-0.12
-0.19+
-0.11
0.05
-0.05***
0.16
(SE)
(0.07)
(0.05)
(0.08)
(0.05)
(0.08)
(0.07)
(0.07)
(0.10)
(0.06)
(0.09)
(0.10)
(0.09)
(0.09)
(0.01)
Note; Comparison groups: ªAge 18-19, Male, "White. "Exposure to Users corresponds to level of stigmatization of the same drug.
*p < .05 (not significant due to Bonferroni correction), *p < .01, **p < .001,
*p < .0001.
Opioid
Stigmatization
b
3.20***
-0.12+
-0.24*
-0.04
0.05
-0.10
-0.02
0.07
-0.32***
0.02
0.03
-0.28*
-0.06
-0.05***
0.14
(SE)
(0.07)
(0.05)
(0.08)
(0.05)
(0.08)
(0.07)
(0.07)
(0.10)
(0.06)
(0.09)
(0.09)
(0.09)
(0.09)
(0.01)
Amphetamine
Stigmatization
b
3.22***
-0.13+
-0.22*
-0.05
0.05
-0.12
-0.07
0.10
-0.32***
-0.04
0.03
-0.28*
-0.11
-0.05***
0.15
(SE)
(0.07)
(0.05)
(0.08)
(0.05)
(0.08)
(0.07)
(0.07)
(0.10)
(0.06)
(0.09)
(0.09)
(0.09)
(0.09)
(0.01)

1.
For Marijuana Stigmatization regression equation in Table 3,
a. Write the research question being analyzed |
b. Write out the final estimated regression equation

Expert Solution

Trending now

This is a popular solution!

Step by step

Solved in 4 steps with 13 images

SEE SOLUTION Check out a sample Q&A here

Blurred answer

Similar questions

a) Give a practical interpretation of the y-intercept of the regression line. b) What is the best-predicted value for the median hourly wage gain for the fifteenth year of schooling? c) The actual wage gain for the fifteenth year of schooling was 14%. How close was the predicted wage gain present to the actual value, i.e. what is the residual?
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…
On the first day of class, an economics professor administers a test to gauge the math preparedness of her students. She believes that the performance on this math test and the number of hours studied per week on the course are the primary factors that predict a student's score on the final exam. Using data from her class of 60 students, she estimates A portion of the regression results is shown in the following table. a. What is the slope coefficient of Hours? b. What is the sample regression equation? c. What is the predicted final exam score for a student who has a math score of 70 and studies 4 hours per week?
c. What are the 2 main pieces of information you get from the linear regression model? Should I go back to the data set and recalculate slope and y intercept if I already have determined the equation of the trend line?
A set of n = 15 pairs of scores (X and Y values produces a regression equation of Y = 2X + 6. What is the predicted Y value for the following X scores. a. 0 b. 3 c. -4
A. Write the equation of the regression line. B. Interpret each one of the slopes in this context. C. Calculate the estimated number of days absent and the residual for the first observation in the data set: a student who is aboriginal, male, a slow learner, and missed 2 days of school.
You do a survey where you ask people both their age and their income. You want to see if there is a relationship between these two variables, and you want to create an equation you can use to predict someone’s age from his/her income. What kind of hypothesis test or analysis should you do?a. One-Factor, Independent-Measures ANOVAb. Two-Factor ANOVAc. Correlation/Regressiond. Chi-Square Goodness of Fit
Answer the questions using complete sentences. a. What is an influential point? How should influential points be treated when doing a regression analysis? b. What is the coefficient of determination and what does it measure? c. What is extrapolation? Should extrapolation ever be used?
A large school district is reevaluating its teachers' salaries. They have decided to use regression analysis to predict mean teacher salaries at each elementary school. The researcher uses years of experience to predict salary. The resulting equation was: Where Y=salary and X=years of experience. The raw data is given in the table below, Salary $54,000.00 $42,140.00 $36,195.00 $45,000.00 $58,950.00 $56,890.00 $53,250.00 $49,800.00 $35,820.00 $74,390,00 $28,900.00 $38,690.00 $78,070.00 $64,205.00 $20,000.00 Years of experience 13 9 7 9 14 13 14 11 8 22 5 8 23 18 2 a) Use Excel to estimate the regression equation (copy and paste your output). What is the coefficient on experience? b) What is the correlation coefficient? c) What is the coefficient of determination, and how do you interpret it? d) Discuss the overall significance of the model
Connie Harris, who is in charge of office supplies at First Capital Mortgage Corp., would like to predict the quantity of paper used in the office photocopying machines per month. She believes that the number of loans originated in a month influence the volume of photocopying performed. She has compiled the following recent monthly data: Develop an estimated regression equation for this data set. Place output in cell G1 At .05 level of significance, is the equation significant? Why? Does the equation seem to have a good “fit”? Why? Forecast the amount of paper used in a month when 42 loan originations are expected. Loans Originated in Month Paper Used (000's) 45 22 25 13 50 24 60 25 40 21 25 16 35 18 40 25
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. a. Find the equation of the regression line. b. Estimate the amount of carbon dioxide emissions of a country with a GDP of 2.5 trillion dollars. c. Compute and interpret the correlation coefficient. 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)
The datasetBody.xlsgives the percent of weight made up of body fat for 100 men as well as other variables such as Age, Weight (lb), Height (in), and circumference (cm) measurements for the Neck, Chest, Abdomen, Ankle, Biceps, and Wrist. We are interested in predicting body fat based on abdomen circumference. Find the equation of the regression line relating to body fat and abdomen circumference. Make a scatter-plot with a regression line. What body fat percent does the line predict for a person with an abdomen circumference of 110 cm? One of the men in the study had an abdomen circumference of 92.4 cm and a body fat of 22.5 percent. Find the residual that corresponds to this observation. Bodyfat Abdomen 32.3 115.6 22.5 92.4 22 86 12.3 85.2 20.5 95.6 22.6 100 28.7 103.1 21.3 89.6 29.9 110.3 21.3 100.5 29.9 100.5 20.4 98.9 16.9 90.3 14.7 83.3 10.8 73.7 26.7 94.9 11.3 86.7 18.1 87.5 8.8 82.8 11.8 83.3 11 83.6 14.9 87 31.9 108.5 17.3…

SEE MORE QUESTIONS

Recommended textbooks for you

MATLAB: An Introduction with Applications

MATLAB: An Introduction with Applications

Statistics

ISBN:

9781119256830

Author:

Amos Gilat

Publisher:

John Wiley & Sons Inc

Probability and Statistics for Engineering and th…

Probability and Statistics for Engineering and th…

Statistics

ISBN:

9781305251809

Author:

Jay L. Devore

Publisher:

Cengage Learning

Statistics for The Behavioral Sciences (MindTap C…

Statistics for The Behavioral Sciences (MindTap C…

Statistics

ISBN:

9781305504912

Author:

Frederick J Gravetter, Larry B. Wallnau

Publisher:

Cengage Learning

MATLAB: An Introduction with Applications

MATLAB: An Introduction with Applications

Statistics

ISBN:

9781119256830

Author:

Amos Gilat

Publisher:

John Wiley & Sons Inc

Probability and Statistics for Engineering and th…

Probability and Statistics for Engineering and th…

Statistics

ISBN:

9781305251809

Author:

Jay L. Devore

Publisher:

Cengage Learning

Statistics for The Behavioral Sciences (MindTap C…

Statistics for The Behavioral Sciences (MindTap C…

Statistics

ISBN:

9781305504912

Author:

Frederick J Gravetter, Larry B. Wallnau

Publisher:

Cengage Learning

Elementary Statistics: Picturing the World (7th E…

Elementary Statistics: Picturing the World (7th E…

Statistics

ISBN:

9780134683416

Author:

Ron Larson, Betsy Farber

Publisher:

PEARSON

The Basic Practice of Statistics

The Basic Practice of Statistics

Statistics

ISBN:

9781319042578

Author:

David S. Moore, William I. Notz, Michael A. Fligner

Publisher:

W. H. Freeman

Introduction to the Practice of Statistics

Introduction to the Practice of Statistics

Statistics

ISBN:

9781319013387

Author:

David S. Moore, George P. McCabe, Bruce A. Craig

Publisher:

W. H. Freeman

SEE MORE TEXTBOOKS