Real quick question Can we make a conclusion/inference from this graph?

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Discussion Overview

The discussion revolves around the interpretation of a graph related to housing data, specifically focusing on the implications of a scatter plot and the validity of various answer options based on statistical reasoning. Participants explore the limitations of the dataset and the appropriateness of predictions made from it.

Discussion Character

  • Debate/contested
  • Mathematical reasoning

Main Points Raised

  • One participant suggests that options B and C are correct, while rejecting A, D, and E based on the data limitations and trends observed.
  • Another participant challenges the reasoning behind rejecting certain options, pointing out an overlooked problem with the dataset.
  • It is noted that the scatter plot appears randomized, which raises questions about the linearity of the data.
  • Some participants argue that predicting future values based on limited data is problematic, particularly for homes built 15 years from 2000.
  • There is a discussion about the assumptions of least squares regression and whether the dataset exhibits equal variance across observations.
  • Concerns are raised about the usability of the data, with one participant expressing a desire to see an R² value to assess correlation.

Areas of Agreement / Disagreement

Participants express differing views on the validity of the answer options, with no clear consensus reached on which options are correct. Multiple competing interpretations of the data and its implications remain present throughout the discussion.

Contextual Notes

Limitations include potential issues with the dataset's variance and the lack of clarity regarding the type of statistical analysis to be applied. The discussion highlights the uncertainty surrounding predictions based on the available data.

riseofphoenix
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This is what I did on StatsCrunch using the data they gave me:

YearBuiltvsSquareFt.png
Would the answer be B and C?
Because A can't be right...our data goes up to 2000. And while there still is a trend going on (newer homes --> more square ft), we can't really give a "predicted value of 1964 and 2250". So that would eliminate D and E (the fourth option and the sixth option).
Am I right? I only get one submission and I have to choose AT LEAST ONE of these.
 
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Explain clearly why you rejected the options that you did. I can see an obvious problem with the dataset that you're ignoring.
 
Number Nine said:
Explain clearly why you rejected the options that you did. I can see an obvious problem with the dataset that you're ignoring.

Option 1 is false because the the scatter plot isn't necessarily linear - it's pretty randomized
Option 2 could be true because our parameter is 1920-2000
Option 3 is true.
Options 4, 5, and 6 are false.
 
Options 4, 5, and 6 are false.

Explain why.
 
Number Nine said:
Explain why.

Well you can't predict exactly how many square ft. a home built 15 years from the year 2000 will have. Especially considering the limited data that was given.
Option 5 just doesn't make sense to me. So it has to be Options 2 and 3.
 
riseofphoenix said:
Well you can't predict exactly how many square ft. a home built 15 years from the year 2000 will have. Especially considering the limited data that was given.
Option 5 just doesn't make sense to me. So it has to be Options 2 and 3.

It "doesn't make sense"? Do you understand the assumptions involved in least squares regression? Does the dataset look like it has equal variance across observations?
 
Number Nine said:
It "doesn't make sense"? Do you understand the assumptions involved in least squares regression? Does the dataset look like it has equal variance across observations?

Oh well not /completely. But to answer your second question, no it doesn't.

So Option 5 would also be the correct answer (along with Options 2 and 3)?
 
Number Nine said:
It "doesn't make sense"? Do you understand the assumptions involved in least squares regression? Does the dataset look like it has equal variance across observations?
That's a fair point, but the question does not specify the kind of analysis to be used. Is it not possible to make allowance for variable variance (heteroscedasticity)?
 
I'd really want to see an R^2 value for this data because it looks absolutely unusable to say the least given the scatterplot.

If the variables are high un-correlated, then any attempt to create dependencies between the variables is going to be useless.
 

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