9.27.97.08.05.11.86.53.0Send data to calculator V5.47.06.35.46.18.25.77.4Sleu!)Based on the study's data and the regression line, complete the following.6-S7XXGAS scoreXX XX(a) For these data, sleep times that are less than the mean of the sleep times tend to be paired with GAS scores that are(Choose one) ▼ the mean of the GAS scores.(b) According to the regression equation, for an increase of one in GAS score, there is a corresponding decrease of howmany hours in sleep time?X The well-known psychologist Dr. Elbod has established what he calls his Generalized Anxiety Scale (GAS). The GAS, which is a scale from 0 to 10, measures the"general anxiety" of an individual, with higher GAS scores corresponding to more anxiety. (Dr. Elbod's assessment of anxiety is based on a variety ofmeasurements, both physiological and psychological.)The bivariate data below give the GAS score (denoted by x) and the number of hours of sleep fast night (denoted by y) for each of the fifteen adults in a study.The least-squares regression line for these data has equation y=8.50-0.29x. This line, along with a scatter plot of the sample data, is shown below.GAS score, x1.14.03.51.59.16.03.79.27.97.08.05.11.8Sleep time, y(in hours)7.46.97.68.76.68.28.45.47.06.35.46.18.2Sleep time(in hours)10+965-0XXXXXXXXXxGAS scoreXX X10x

a) The independent variable is gas score (x) and the dependent variable is sleep time (y).

Answered: 9.2 7.9 7.0 8.0 5.1 1.8 6.5 3.0 Send…

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

• where not met in 200 iterations Listed below are paired data consisting of amounts spent on advertising (in millions of dollars) and the profits (in millions of dollars). Determine if there is a significant linear correlation between advertising cost and profit. Use a significance level of 0.01 and round all values to 4 decimal places. Advertising Cost Profit 26 23 20 30 30 27 29 3 4 5 6 7 r= 8 9 10 11 Ho: p = 0 Ha: p = 0 Find the Linear Correlation Coefficient Find the p-value p-value = 29 31 The p-value is Greater than a O Less than (or equal to) a The p-value leads to a decision to Accept Ho O Do Not Reject Ho O Reject Ho The conclusion is O There is a significant negative linear correlation between advertising expense and profit. There is a significant linear correlation between advertising expense and profit. There is a significant positive linear correlation between advertising expense and profit. There is insufficient evidence to make a conclusion about the linear correlation…
Fifty matched pairs of magnitude/depth measurements randomly selected from 10,594 earthquakes recorded in one year from a location in southern California. Find the best predicted depth of an earthquake with a magnitude of 2.2. Use a significance level of 0.05. : Click the icon to view the earthquake measurements. Click here to view the critical values of the Pearson correlation coefficient, r. The regression equation is y = ? + ?x. 回-ト i Critical Values of the Pearson Correlation Coefficient r (Round to one decimal place as needed.) Critical Values of the Pearson Correlation Coefficient r α= 0.05 0.950 0.878 0.811 0.754 0.707 0.666 0.632 0.602 0.576 a = 0.01 0.990 0.959 0.917 0.875 0.834 0.798 0.765 0.735 0.708 NOTE: To test Ho: p=0 Jagainst H,: p#0, reject H, if the absolute value of r is greater than the critical value in the table. 5 10 11 12
Listed below are paired data consisting of amounts spent on advertising (in millions of dollars) and the profits (in millions of dollars). Determine if there is significant linear correlation between advertising cost and profit . Use a significance level of 0.10 and round all values to 4 decimal places. Advertising Cost Profit 3 23 4 23 5 22 6 26 7 25 8 25 9 25 10 30 11 31 12 31 Ho: ρ = 0Ha: ρ ≠ 0 Find the Linear Correlation Coefficient r = Find the p-value p-value =
Suppose a doctor measures the height, x, and head circumference, y, of 11 children and obtains the data below. The correlation coefficient is 0.899 and the least squares regression line is y=0.185x+12.276. Complete parts (a) and (b) below. Height, x 27.75 25.75 26.75 25.75 28 26.5 25.75 26.75 27 27.25 27.25 Head Circumference, y 17.4 17.1 17.2 16.9 17.4 17.1 17.1 17.3 17.3 17.3 17.4 (a) Compute the coefficient of determination, R2. R2=nothing% (Round to one decimal place as needed.) (b) Interpret the coefficient of determination. Approximately nothing% of the variation in ▼ height head circumference is explained by the least-squares regression model. (Round to one decimal place as needed.)
The following correlations were computed as part of a multiple regression analysis that used education, job, and age to predict income. Income Education Job Age Income 1.000 Education 0.677 1.000 Job 0.173 -0.181 1.000 Age 0.369 0.073 0.689 1.000 What is this table called? Multiple Choice Correlation matrix Net regression coefficients Coefficients of nondetermination Analysis of variance
10.4.9 Question Help The accompanying table shows results from regressions performed on data from a random sample of 21 cars. The response (y) variable is CITY (fuel consumption in mi/gal). The predictor (x) variables are WT (weight in pounds), DISP (engine displacement in liters), and HWY (highway fuel consumption in mi/gal). If only one predictor (x) variable is used to predict the city fuel consumption, which single variable is best? Why? E Click the icon to view the table of regression equations. The best variable is V because it has the best combination of V P-value, , and adjusted R2, (Type integers or decimals. Do not round.)
Suppose a doctor measures the height, x, and head circumference, y, of 11 children and obtains the data below. The correlation coefficient is 0.883 and the least squares regression line is y = 0.134x + 13.693. Complete parts (a) and (b) below. Height, x Head Circumference, y 17.4 27.75 25.25 26 25.25 27.25 26.75 26 27.25 27 27.25 26.75 17.1 17.3 17.0 17.4 17.2 17.2 17.3 17.3 17.4 17.3 ..... (a) Compute the coefficient of determination, R?. R? % (Round to one decimal place as needed.) (b) Interpret the coefficient of determination. Approximately % of the variation in is explained by the least-squares gression model. (Round to one decimal place as needed.)
Answer the questions based on the following data table. What is the value of the correlation coefficient (r) between the variables? (round to 2 decimals) How much of the variation between the variables is explained by the regression model (R-sqr)? (round to 2 decimals) How far off (std error) will the regression model typically be in predicting y? At what level is the regression statistically significant (i.e.,Sig.F)? decimals) What is the slope (coefficient of x) of the linear regression equation? 2 decimals) What is the y-intercept of the linear regression equation? (round to 2 If given an x value of 3.6, what value of y does the regression equation predict? (round to 2 decimals) (round to (round to 2 decimals)
Listed below are paired data consisting of amounts spent on advertising (in millions of dollars) and the profits (in millions of dollars). Determine if there is a significant negative linear correlation between advertising cost and profit . Use a significance level of 0.01 and round all values to 4 decimal places. Advertising Cost Profit 17 4 15 20 6. 28 7 17 8 29 Ho: p = 0 На: р < 0 Find the Linear Correlation Coefficient r = Find the p-value p-value = The p-value is Less than (or equal to) a O Greater than a The p-value leads to a decision to Ассept Ho Reject Ho O Do Not Reject Ho The conclusion is O There is a significant negative linear correlation between advertising expense and profit. There is a significant positive linear correlation between advertising expense and profit. O There is a significant linear correlation between advertising expense and profit. O There is insufficient evidence to make a conclusion about the linear correlation between advertising expense and profit.
Listed below are paired data consisting of amounts spent on advertising (in millions of dollars) and the profits (in millions of dollars). Determine if there is a significant linear correlation between advertising cost and profit . Use a significance level of 0.05 and round all values to 4 decimal places. Advertising Cost Profit 3 19 4 16 24 6 29 7 25 27 10 30 Ho: p = 0 На: р * 0 Find the Linear Correlation Coefficient r = Find the p-value p-value = The p-value is O Less than (or equal to) a O Greater than a The p-value leads to a decision to O Do Not Reject Ho O Accept Ho O Reject Ho The conclusion is O There is a significant negative linear correlation between advertising expense and profit. O There is a significant linear correlation between advertising expense and profit. O There is a significant positive linear correlation between advertising expense and profit. O There is insufficient evidence to make a conclusion about the linear correlation between advertising expense and…
Q9 The explanatory variable in linear correlation/regression model is recorded on which of thesemeasurement scales: A. categorical B. ordinal C. quantitative D. none of the above
Carefully read the description of the following two studies (A and B). For each study answer the following questions: Should a correlation analysis or multiple regression be used to analyze the results. Describe the basis for your answer – why did you select this test. Study A Dr. Banner want to determine whether his patients’ age is a factor in compliance with dialysis. He hypothesizes that older patients will be less compliant with dialysis. In other words, he believes the older the patient the more dialysis appointment they miss. Study B Dr. Banner’s intern does not agree. She believes there are other factors, along with age, that influence compliance such as race anxiety and mistrust of health care, side effects of treatment and issues related to culture. She wants to know who much each of these factors contribute to non-compliance.

SEE MORE QUESTIONS

Recommended textbooks for you

MATLAB: An Introduction with Applications

Statistics

ISBN:

9781119256830

Author:

Amos Gilat

Publisher:

John Wiley & Sons Inc

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

ISBN:

9781305504912

Author:

Frederick J Gravetter, Larry B. Wallnau

Publisher:

Cengage Learning

MATLAB: An Introduction with Applications

Statistics

ISBN:

9781119256830

Author:

Amos Gilat

Publisher:

John Wiley & Sons Inc

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

ISBN:

9781305504912

Author:

Frederick J Gravetter, Larry B. Wallnau

Publisher:

Cengage Learning

Elementary Statistics: Picturing the World (7th E…

Statistics

ISBN:

9780134683416

Author:

Ron Larson, Betsy Farber

Publisher:

PEARSON

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

Statistics

ISBN:

9781319013387

Author:

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

Publisher:

W. H. Freeman

SEE MORE TEXTBOOKS