Test the following data table to see whether the data are quadratic. x 0 4 16 32 44 y 4 12 16 12 20 Calculate the first-order and second-order differences.

The height (sidewalk to roof) of notable tall buildings in America is compared to the number of stories of the building (beginning at street level). Stories (x) Height (y) 59 1050 29 428 25 362 40 529 60 790 22 401 38 380 110 1454 100 1127 46 700 Calculate the least squares line. Put the equation in the form of: y = a + bx. (Round your answers to three decimal places.) ŷ = + X

The scatter plot shows the number of hours worked and money spent on entertainment by each of 24 students. Also shown is the line of best fit for the data. Fill in the blanks below. 70 65 60 55. Amount of 50 × 45 money spent on entertainment 40+ (in dollars) 35. × × 30 25 20 15. × 10+ 5-x 0 6 8 10 12 14 16 18 20 22 Number of hours worked (a) For these 24 students, as the number of hours increases, the amount of money spent tends to (Choose one) (b) For these 24 students, there is (Choose one) money spent. correlation between the number of hours worked and the amount of (c) Using the line of best fit, we would predict that a student working 8 hours would spend approximately (Choose one) ▼ ك ?

Data in the table comes from the NASA Global Climate Change website. It gives the average globaltemperature, in Celsius, for the given year. Use both rows for your calculations. For this problem, youwill see how well year correlates with the average temperature. Let ? be the number of years since1984 and ? be the average global temperature in degrees Celsius.Year 1984 1986 1988 1990 1992 1994 1996 1998 2000Temp 0.16 0.18 0.38 0.45 0.22 0.31 0.33 0.61 0.40Year 2002 2004 2006 2008 2010 2012 2014 2016 2018Temp 0.63 0.54 0.64 0.54 0.72 0.64 0.75 1.02 0.85a. Make a scatterplot of the data. Being sure to clearly label each axis. Keep in mind howeach variable is defined. Based on this scatterplot, is a linear model reasonable for this data set?Briefly explain.b. Determine the correlation coefficient for this data. Interpret this value in context.c. Determine the coefficient of determination for this data. Interpret this value incontext.d.State the regression line for the data.e. What does the…