To plot the data points from the table onto the grid and then draw a linear model that fits, follow these steps:

Step 1: Plot the Data Points

Using the table values, plot the data on the grid as follows:

• (1, 40)
• (2, 50)
• (4, 60)
• (6, 75)
• (7, 80)
• (9, 90)

Each point corresponds to the number of study hours (x-axis) and the test score (y-axis).

Step 2: Draw a Linear Model

To create a linear model, we can approximate the line by using a best-fit line through the points. Let's calculate the linear regression line, which typically has the form: $y = mx + b$ where $m$ is the slope, and $b$ is the y-intercept.

1. Using a simple linear regression method or plotting tool, you could estimate the slope and intercept.
2. However, since this is a small dataset, you could also estimate the line by visually drawing a line that closely passes through the points.

Would you like the specific calculation for the regression line or additional details on how to determine the slope and intercept?

Related Questions:

1. How can we calculate the slope (m) and y-intercept (b) for the linear model?
2. What methods can we use to estimate a line of best fit without a calculator?
3. How does increasing the number of study hours affect the test score according to the linear model?
4. Can we use this model to predict scores for other values of study hours, such as 5 or 10?
5. What would a nonlinear model look like, and when would it be more appropriate?

Tip:

In data analysis, the line of best fit helps predict values within the range of data points (interpolation).