Find the equation of the least-squares regression line for predicting the number of wins from number of goals scored.
Y=−0.15+0.29x
Calculate a quadratic model for these data using carapace length as the explanatory variable
Y =−0.0106x^2 +6.6181x−1018.9553
Would you be willing to use the regression line to predict the long-jump distance for a student whose 40-yard sprint time is 3 seconds? Explain.
No, far outside data value
Use it to determine whether the regression model is appropriate.
yes
Calculate and interpret the residual for March, when the average temperature was 49.4°F and Joan used an average of 520 cubic feet per day.
443.422 cubicfeetperday, res=76.578
Interpret the value s = 15.99 for this model.
The actual percent of the grass area burned is typically about 15.99% away from its predicted percent of the grass area burned using the least-squares regressio
Interpret the slope of the regression line.
decreases by 19.87 cubic feet per day for each additional degree of Fahrenheit for the average monthly temperature
Use it to determine whether the regression model is appropriate.
Yes
Calculate and interpret the residual for the tortoise with carapace length 298 millimeters.
2.6
Use technology to calculate the equation of the least-squares regression line relating y = number of larvae to x = number of stumps.
y = −1.29 + 11.89x
Interpret r=.45
somewhat weak and positive
Make a scatterplot
2.2 #5
Estimate r
close to -1
Use technology to calculate the equation of the least-squares regression line relating y = price to x = screen size.
Y = −575.23 + 25.41x
Calculate the correlation
.84
Estimate r
Close to 1
Find the Residual.
2.07 minutes
Calculate Correlation
.89
Use it to determine whether the regression model is appropriate.
No
Predict the interval of time between eruptions if the previous eruption lasted 4 minutes
86.51 minutes
Predict the long-jump distance for a student who had a 40-yard-dash time of 6 seconds
140.8 inches
make a segmented bar chart
expl = age
Interpret the value r^2 = 0.646 for this model.
64.6% of the variability in the percent of the grass area burned is accounted for by the least-squares regression line with x = wildebeest abundance.