Unit D: Graphing

Predicting the Shape of a Graph in Real-Life Situations


Trends of linear relations can show positive correlation or negative correlation. The line of best fit can be drawn three different ways to demonstrate linear trends.

Graph A: Positive Correlation Showing Direct Variation



As the x-value increases, the y-value increases. The point (0, 0) is included in the data.


Graph B: Positive Correlation Showing Partial Variation



As the x-value increases, the y-value increases. The point (0, 0) is not included in the data. The y-intercept shows that there is a fixed value when x = 0.
Graph C: Negative Correlation



As the x-value increases, the y-value decreases. The point (0, 0) is not included in the data.




Match each trend in the following scenarios with one of the graphs above.

  1. The brightness of the headlights of a car decreases as the distance away from the car increases.

  2. Juan earns $10 per hour. He plots a graph showing his wages if he works 0 to 8 hours. Match the trend with one of the graphs above.

  3. The cost of pizza with tomato sauce and cheese is $6.00. It costs $0.70 for each additional topping. The graph is created for the purchase of a pizza with up to 6 toppings. Match the trend with one of the graphs above.

  1. The dependent variable (y-value) is the brightness of the headlights, and the independent variable (x-value) is the distance. The y-value decreases when the x-value increases, so the graph would look similar to graph C.






  2. The dependent variable is the money earned, and the independent variable is the hours worked. The y-value increases when the x-value increases, and the point (0, 0) is included in the data. Therefore, the graph would look similar to graph A.






  3. The dependent variable is the cost of the pizza, and the independent variable is the number of toppings. The y-value increases when the x-value increases, and there is an initial value of $6.00 when x = 0. Therefore, the graph would look similar to graph B.