AI Math Solver
Resources
Questions
Pricing
Login
Register
Home
>
Questions
>
Calculate Residual Values Using a Line of Best Fit (8th Grade Math Problem)
Mathematics
Grade 8 (Junior High School)
Question Content
The predicted values were computed by using the line of best fit, $y = 3.2x + 2$. Complete the chart by finding the residual values $a$, $b$, $c$, $d$: \n| $x$ | Given | Predicted | Residual |\n|-----|-------|-----------|----------|\n| 1 | 6.1 | 5.2 | $a$ |\n| 2 | 7.4 | 8.4 | $b$ |\n| 3 | 11 | 11.6 | $c$ |\n| 4 | 15 | 14.8 | $d$ |
Correct Answer
$a=0.9$, $b=-1.0$, $c=-0.6$, $d=0.2$
Detailed Solution Steps
1
Step 1: Recall the definition of a residual: Residual = Observed (Given) Value - Predicted Value, which measures the difference between actual data and the value predicted by the line of best fit.
2
Step 2: Calculate residual $a$: Substitute the given and predicted values for $x=1$: $a = 6.1 - 5.2 = 0.9$.
3
Step 3: Calculate residual $b$: Substitute the values for $x=2$: $b = 7.4 - 8.4 = -1.0$.
4
Step 4: Calculate residual $c$: Substitute the values for $x=3$: $c = 11 - 11.6 = -0.6$.
5
Step 5: Calculate residual $d$: Substitute the values for $x=4$: $d = 15 - 14.8 = 0.2$.
Knowledge Points Involved
1
Residual Value Calculation
Residual is calculated as Observed Value - Predicted Value. It quantifies how far an actual data point is from the value predicted by a regression model (like a line of best fit). Positive residuals mean the observed value is higher than the predicted value; negative residuals mean the observed value is lower.
2
Line of Best Fit
A linear equation (such as $y=3.2x+2$) that approximates the linear relationship between an independent variable ($x$) and dependent variable ($y$) in a dataset. It is used to predict $y$-values for given $x$-values, and forms the basis for calculating residuals.
3
Interpreting Residuals
Residuals help evaluate the accuracy of a line of best fit: small residuals (close to 0) mean the model fits the data well, while large positive/negative residuals indicate the model does not predict that data point effectively.
Loading solution...