ieh of the following statements is true? a) About 95.3% of the variation in tread depth is explained by the regression on miles. b) According to the least-squares regression line, the groove depth of a new tire (driven 0 miles) is predicted to be 360.64 mils. Oc) According to the least-squares regression line, we would predict a decrease in groove depth of 11.39 mils for each 1000 miles driven on a tire. d) All of the above.

1. Develop a simple linear regression equation for starting salaries using an independent variable that has the closest relationship with the salaries. Explain how you chose this variable.

Answer the following: 2. Describe the form, direction, and strength of the relationship between these variables. Does there appear to be any outliers? 3. The equation of least-squares regression line is y = 6.871 + 0.061x. This line goes through the points (0, 6.9) and (700, 49.5). Use these points to add the regression line to your scatterplot above. 4. One animal is farthest from the regression line and is an outlier for these two variables. Find the name of this animal and write a sentence describing how its position differs from the overall relationship among the other animals. 5. The elephant has the longest gestation period of these animals. a. Considering only the gestation variable, would you say that the elephant is an outlier? Write a sentence or two explaining why or why not? b. Considering both variables and the relationship between average life span and average gestation period, would you say the elephant is an outlier now? Write a sentence or two explaining why or why…

For Data Set 9 in Appendix B, “Bear Measurements,” we get this regression equation: Weight = -274 + 0.426 Length + 12.1 Chest Size, with R2 = 0.928. Interpret the multiple coefficient of determination – what does this value tell us?

Find the​ least-squares regression line treating square footage as the explanatory variable.   y =  ​(Round the slope to three decimal places as needed. Round the intercept to one decimal place as​ needed.)

A local retail store compared their monthly sales of umbrellas with the amount of rainfall that occured during that month. They computed the following statistics: Rainfall (in) # of umbrellas mean = 4.64 mean = 34.2 SD = 1.17 SD = 13.2 r = 0.8 1. Find the equation for the regression line that predicts the monthly sales of umbrellas from monthly rainfall.

what ​% of the variation is ( height, or head circumference) explained by the​ least-squares regression model. ​(Round to one decimal place as​ needed.)

Is there a way to compute using Excel?