The table below gives the estimated world population (in billions) for various years.19901997200020052011Years 1980Population 440051005852608064507000What is the strength of the correlation between the two given variables in the table?no correlation at allmoderateweakstrong

Given data obtained as follows, x y x2 y2 x⋅y 1980 4400 3920400 19360000 8712000 1990 5100…

Answered: The table below gives the estimated…

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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Shown below is a scatterplot of Y versus X. -10 -20 -30 -40 -50 10 15 20 25 Which choice is most likely to be the correlation coefficient between X and Y? -0.99 0.99 0.10 -0 10
Fully explain the issues and problems with the below correlational data. Focus on the data in the highlighted box versus the rest of the data. Do not try to explain the data, but rather focus on the statistical issues and problems. IF YOU CAN'T SEE THE IMAGE HERE. PLEASE GO TO THIS LINK Linke 10 6. 8. 7. 5. 4. 10 20 30 40 50 60 70 80 90 100 Age Enjoyment of Hip-Hop
What is another possible explanation for this correlational relationship? Since this is just a correlation, we can’t be sure if age causes changes in your field goal percentage. So, what is one possible other explanation that you can come up with for the correlation that you calculated (hint: think about possible "third variable" explanations for this correlation---what other variables or factors might be causing changes in both x and y that could explain this correlation?).
The table below gives the estimated world population (in billions) for various years. 1980 1990 1997 2000 2005 2011 Years Population 4400 5100 5852 6080 6450 7000 Is the correlation significant? yes cannot be determined yes only to some extent O no
Part C ?
CPI represents a country’s Consumer Price Index and MS represents the country’s Money Supply (in £ billions) over a 10-year period. CPI MS 95 8 99 9 100 10 105 12 110 15 120 20 122 20 123 21 123 21 124 21 Calculate the Pearson correlation coefficient between the two variables. Comment on the strength of the relationship between CPI and MS.
What is another possible explanation for this correlational relationship? Since this is just a correlation, we can’t be sure if age causes changes in your field goal percentage. So, what is one possible other explanation that you can come up with for the correlation that you calculated (hint: think about possible "third variable" explanations for this correlation---what other variables or factors might be causing changes in both x and y that could explain this correlation?).
You conduct a Pearson Product-Moment correlation test to see if two variables are related to each other. Even though the test is significant, you suspect that there actually isn't a real relationship between X and Y, and that they only seem to be related because of their relationship to another variable Z. This is called: bi-directionality the third-variable problem reverse-causality a causal effect
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The table below gives the estimated world population (in billions) for various years. 1980 1990 1997 2000 2005 2011 Years Population 4400 5100 5852 6080 6450 7000 What is the value of the correlation coefficient (r)? O 0.558 Ⓒ -0.894 0.998 O- 0.489
Part b?

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The table below gives the estimated world population (in billions) for various years. 1980 1990 1997 2000 2005 2011 Years Population 4400 5100 5852 6080 6450 7000 What is the strength of the correlation between the two given variables in the table? O no correlation at all moderate O weak strong