Asked by BlUbArRy!2#
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On a graph titled Time Spent and Test Grades, a line and ten points are plotted in quadrant 1 of a coordinate plane. The x-axis is labeled Hours Worked, and the y-axis is labeled Grade.
Which of the following is an appropriate regression model given the bivariate dataset?
(1 point)
Responses
f(x)∼9x+100
f left parenthesis x right parenthesis is similar to 9 x plus 10
f(x)∼−9x +58
f left parenthesis x right parenthesis is similar to negative 9 x
f(x)∼−9x+100
f left parenthesis x right parenthesis is similar to negative 9 x plus 10
f(x)∼9x +58
On a graph titled Time Spent and Test Grades, a line and ten points are plotted in quadrant 1 of a coordinate plane. The x-axis is labeled Hours Worked, and the y-axis is labeled Grade.
Which of the following is an appropriate regression model given the bivariate dataset?
(1 point)
Responses
f(x)∼9x+100
f left parenthesis x right parenthesis is similar to 9 x plus 10
f(x)∼−9x +58
f left parenthesis x right parenthesis is similar to negative 9 x
f(x)∼−9x+100
f left parenthesis x right parenthesis is similar to negative 9 x plus 10
f(x)∼9x +58
Answers
There are no human answers yet.
Answered by
GPT-4o mini
To determine the appropriate regression model based on the description of the graph, we need to consider the typical relationship between time spent working (x-axis) and test grades (y-axis).
1. **Positive Relationship**: If the graph shows a rising trend, it suggests that as the number of hours worked increases, the test grades also increase. In this case, a positive slope (e.g., \(f(x) \sim 9x + b\)) would be expected.
2. **Negative Relationship**: Conversely, if the graph indicates that test grades decrease as hours worked increase (which is less common), a negative slope (e.g., \(f(x) \sim -9x + b\)) would be likely.
Since the question doesn't provide a direct visual of the graph, and based on the likely expectation that more study time correlates with better grades, a positive model would generally be more plausible.
Among the options given:
- **f(x)∼ 9x + 58** suggests a positive correlation,
- The others indicate negative correlations (which would be less common).
Therefore, if the context implies a positive relationship, the most appropriate regression model among the choices provided would be:
**f(x)∼9x + 58**.
1. **Positive Relationship**: If the graph shows a rising trend, it suggests that as the number of hours worked increases, the test grades also increase. In this case, a positive slope (e.g., \(f(x) \sim 9x + b\)) would be expected.
2. **Negative Relationship**: Conversely, if the graph indicates that test grades decrease as hours worked increase (which is less common), a negative slope (e.g., \(f(x) \sim -9x + b\)) would be likely.
Since the question doesn't provide a direct visual of the graph, and based on the likely expectation that more study time correlates with better grades, a positive model would generally be more plausible.
Among the options given:
- **f(x)∼ 9x + 58** suggests a positive correlation,
- The others indicate negative correlations (which would be less common).
Therefore, if the context implies a positive relationship, the most appropriate regression model among the choices provided would be:
**f(x)∼9x + 58**.
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