10:17Class Size and Test ScoresAnswered Due on May 18, 11:59 PMNo Correct AnswerThe following graph summarizes results from astudy that recorded class sizes and averagestudent test scores. The figure suggests thatstudents in smaller class sizes get higher testscores. Does this mean that if we lowered classsizes we would get higher test scores? Briefly listone or two reasons why the relationship might be'spurious' rather than causal.Type your responseܕ ܬ ܕ ܘ ܘ ܬ ܪ ܪ ܪ ܪAverage Subject ScroeNational Average-15-20-10.0Figure 4: Class Size and Student Test ScoresClass Size & Student Test Scores-5.00.05.0Student to Teacher Ratio vs. National AverageTest Score = -0.9662"(Extra student per teacher)Correlation (r) -45%10.0Resubmit

The correlation shows the relationship b/w 2 variables.

Answered: The following graph summarizes results…

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

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
Your younger brother, a fourth form practical geography student wishes to test whether there could be some relationship between annual visits to a local market by all residents surrounding that market. After a well-designed field study he obtains the following figures: Number of visits per year 21 13 19 18 17 17 14 20 10 11 Residence distance from market (KM) 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 From the information above, what is the dependent variable and what is the explanatory variable? Using (i) above, obtain the regression equation of Yi on Xi. Hence, estimate the residence distance from the market (in KM) if the number of visits are 16 visits Sketch your results in (ii) above. What conclusion can you draw from your sketch?
The ages of the runners in a race and their finishing positions are given in the following table. Assuming x is the finish position and y is the person's age, is there a significant correlation between age and finishing position? Answer Choices Not significant Not one of these Significant at both 5% and 1% Significant at 5% but not 1% Significant at 1%
If the linear association between two numerical variables is studied and the correlation coefficient is 0.32, it can be concluded that:a. The relationship between the variables is inverse and weak. b. The percentage of variability observed in the data that is explained by the error is 89.76%. c. The percentage of observed variability in the data that is explained by the model is 32%. d. None is correct Please explain clearly, thank you
8.
Need help on questions 1 & 2. Thank you.
If when measuring two quantitative variables on an individual, we find that increases in the first variable x correspond to decreases in the second variable y, we say that A. there is a positive association between the two variables. b. there is a negative association between the two variables. c. there is no correlation between the two variables. d. there is a cause-and-effect relationship between the two variables.
The following data refers to the amount of caffeine (in mg) consumed by ten university students and their time to complete a practice test (in minutes) for a Economics Exam. Use attached Graph and Table to answer question below pls Describe the overall pattern of the relationship between caffeine amount and time to finish the practice exam in graph diagram provided. Does it follow a straight-line pattern? Is there a positive or negative association, or neither? How strong is the relationship?
Mr Alsup asked the studentS in his third-and sixth-period English classes whether they had chosen a topic for thier writing assignment. he recorded the data in a two-way table, as shown below English class Yes No Total Third period 24 6 30 Sixth period 22 3 25 Total 46 9 55 if jannette in in mr alsup sixth-period english class what is the probablity that she has not chosen a topic for her writing assignment? a- 3% b- 5% c- 12% d-14%
There appears to be a problem with one of your contact methods. Please check your SettingS Enter the following table into your calculator or the Desmos Grapher and create the scatterplot. X 2 60 33 2,100 36 2,860 49 14,440 65 10,560 79 567,860 79 570,560 97 2,238,200 95 3,674,760 98 5282120 1. Is there a correlation for the data? If so, is it strong or weak? 2. What shape of function does the scatterplot show? 3. Use your calculator or the Desmos Grapher to find the equation for the line/curve of best fit. Now, use the equation editor to give the equation for the line/curve of best fit. 4. Determine if this equation is a good predictor of the data, using the appropriate number as proof/reasoning.
Dr. Erie, a researcher at the Moore County Botany Institute, reported the following correlations. The correlation between pH level of soil and tomato weight is r₁ = -0.4. The correlation between hours of sunlight and tomato weight is r2 = 0.1. According to this information, which of the following statements are true? Select all that apply. Larger tomatoes result from more daily hours of sunlight. Larger tomatoes result from higher soil pH level. Tomato size is related more strongly to hours of sunlight than to soil pH level. None of these are true.
. Differentiate between correlation vs. causation. When two variables are correlated (such as strength and running speed), they are associated with one another. Explain how, even if there is a correlation between the two, one might not cause the other. Provide an example of an association between variables where a causal relationship makes sense conceptually. Why can't correlations be used as a tool to prove a causal relationship between variables rather than just an association?

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