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

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In summary, the conversation discusses the use of StatsCrunch to analyze data and determine the correct answer to a question. The speaker rejects options A, D, and E due to the limited data and the lack of a clear trend. They also reject option 5 due to the assumptions involved in least squares regression and the unequal variance in the dataset. The speaker believes options 2 and 3 to be the correct answers, but also questions the usability of the data in general.
  • #1
riseofphoenix
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Question4.png


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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  • #2
Explain clearly why you rejected the options that you did. I can see an obvious problem with the dataset that you're ignoring.
 
  • #3
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.
 
  • #4
Options 4, 5, and 6 are false.

Explain why.
 
  • #5
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.
 
  • #6
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?
 
  • #7
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)?
 
  • #8
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)?
 
  • #9
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.
 

1. Can we make a conclusion/inference from this graph?

Yes, conclusions and inferences can be made from a graph. However, the validity of these conclusions depends on the quality of the data and the accuracy of the graph.

2. How do we make a conclusion/inference from a graph?

To make a conclusion or inference from a graph, you need to analyze the data and look for patterns or trends. You can also use statistical techniques to determine the significance of the data.

3. What factors should be considered when making a conclusion/inference from a graph?

When making a conclusion or inference from a graph, it is important to consider the data source, sample size, data collection methods, and any potential biases that may affect the results.

4. Is it possible to make a conclusion/inference without any statistical analysis?

It is possible to make a conclusion or inference without statistical analysis, but it may not be as accurate or reliable. Statistical analysis helps to determine the significance of the data and reduce the impact of potential errors.

5. Can we make multiple conclusions/inferences from the same graph?

Yes, it is possible to make multiple conclusions or inferences from the same graph. However, it is important to carefully consider the data and any potential limitations before drawing multiple conclusions.

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