Find the quartic function that is the best fit for the data in the table below. Report the model with three significant digits in the coefficients. x y -2 -1 -1 -1 0 0 1 -1 2 -1 3 9 4 44 y=?

Use linear regression on your calculator to find the equation of the linear function that best fits this data. 1 3 4 5 6 y 84 110 139 170 180 222 = mx + b. Round all numbers to 2 decimal Hint: Write your final answer as an equation in the form y places.

The data show the chest size and weight of several bears. Find the regression equation, letting chest size be the independent (x) variable. Then find the best predicted weight of a bear with a chest size of 40 inches. Is the result close to the actual weight of 352 pounds? Use a significance level of 0.05. Chest size (inches) *Weight (pounds) 44 54 328 528 41 55 39 51 418 580 296 503 Click the icon to view the critical values of the Pearson correlation coefficient r. - What is the regression equation? x (Round to one decimal place as needed.)

Use linear regression to find the equation for the linear function that best fits this data. Round both numbers to two decimal places. Write your final answer in a form of an equation y= mx +b X y 1 92 2 110 4 5 3 139 160 171 6 196

Let ŷ represent the profit (in thousands of dollars) for a certain company xx years after 1965. A statistician calculates a linear model ŷ = 0.38xx + 15Use the model to estimate the profit in 1973.  Your answer should be in dollars, not thousands of dollars.Profit =$

A table of data points is given below. a. Find the regression equation for the data points. Use the formulas Sxx=∑ xi−x2 and Sxy=∑ xi−x yi−y to obtain Sxx and Sxy. b. Graph the regression equation and data points.   x 1 2 3   y 6 5 10   a. The regression equation is y= + () x. ​(Simplify your answers. Type integers or decimals. Do not​ round.)

Use the given data set to answer parts (a) and (b). a. Find the regression equation for the data points. X 5 b. Graph the regression equation and the data points. y -1 10 -10 y B. 10 -10- C. a. Find the regression equation for the data points. ŷ-0 (Use integers or decimals for any numbers in the expression. Round to two decimal places as needed.) b. Graph the regression equation and the data points. O A. 10 T -10- 1 2 -1 3 327 D. -10 4 y 4 28 0

The following table shows retail sales in drug stores in billions of dollars in the U.S. for years since 1995. 220- 210 200 190 180 170 160 150 Year 0 130- 120- 110- 100- 90 801 6 12 15 Let S(t) be the retails sales in billions of dollars in t years since 1995. A linear model for the data is F(t) = 9.44t + 84.182. Retail Sales 85.851 108.426 141.781 169.256 202.297 222.266 3 6 9 12 15 Use the above scatter plot to decide whether the linear model fits the data well. The function is a good model for the data. The function is not a good model for the data Estimate the retails sales in the U. S. in 2015. billions of dollars.

Some pedometers have a default setting that assumes that the number of steps a person must take to walk 1 mile decreases by 32 steps per 1 inch increase in height. Suppose a person 72 inches tall makes 2,250 steps per mile. Find a linear model of the number of steps S per mile as a function of height h (in inches). S =          Use the model to predict the number of steps per mile a person who is 64 inches tall will take.   steps per mile

The table shows the number of adult softball teams since 1990, where x represents the year since 2000 and y represents the number of teams (in thousands). Make a scatter plot of the data and determine which type of model best fits the data.  Year, x 0 1 2 3 4 Number of teams (in thousands) 163 155 149 143 119       linear     quadratic     exponential     cubic