Construct a scatterplot and identify the mathematical model that best fits the data. Assume that the model is to be used only for the scope of the given data and consider only linear, quadratic, logarithmic, exponential, and power models. Use a calculator or computer to obtain the regression equation of the model that best fits the data. You may need to fit several models and compare the values of R². The table below shows the weight of crops of oranges and the resulting gallons of juice. Using the weight as the independent variable, find the regression equation of the best model. x (pounds of oranges). 4321 5012 5239 5366 8978 25413 y (gallons of orange juice) 341.3 391.5 399.6 417.2 656.1 1927.3 Oy=-13.07 +0.079x Oy=264.43 el.0001x Oy=-7511.9+923.99 In x Oy=0.079x0.998

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Construct the equation for the line of best fit for the following data set. Draw the line of best fit and label the intercepts. Show all of your work in creating the equation using the intercepts.

The following is data for the first and second Quiz scores for 8 students in a class. First Quiz (Q1) Second Quiz (Q2) 15 15 16 13 17 15 22 21 24 24 26 21 29 27 30 27 Plot the points in the grid below, then sketch a line that best fits the data. Use the line above to predict the value of the second quiz score if a student had a score of 1212 on the first test.  (Please enter a whole number)

The owner of a radio station wants to determine how much airtime songs take up. The owner collects the song length in seconds of 10 popular songs. The data are reproduced in the table below. Calculate the mode(s) using a TI-83. TI-83 plus, or TI-84 graphing calculator. Length of Songs in Seconds 279 219 298 134 206 293 165 227 230 212

The table shows the total number of trade apprenticeships in Canada each year from 1991 to 1997 a) Consider the year 1991 as year #0. Create a scatter plot of the data on the next page using the DESMOS graphing calculator. apprenticeships 192,945 Year 1991 1992 180,965 168,985 163,750 163,370 1993 1994 1995 1996 165,325 171,180 1997 b) From the scatter plot, which model appears most appropriate: linear, quadratic, exponential. Give reason for your answer c) Generate a model for the data (line/curve of best fit) using regression. Write the equation of the model that best fits the data. d) Use the model to predict when apprenticeships will return to 1991 levels.

A real estate agent has developed a linear model for the price of a house, P, in dollars in terms of the area, A, in square feet for the homes in a certain neighborhood. The data set had areas ranging from 1,000 square feet to 4,500 square feet. Would predicting the prices of a home that is 4,900 square feet be interpolation or extrapolation? Explain.     Using the model to predict the price of a 4,900 square foot home is extrapolation because 4,900 square feet is outside the range of the areas in the data. Using the model to predict the price of a 4,900 square foot home is interpolation because 4,900 square feet is inside the range of the areas in the data. Using the model to predict the price of a 4,900 square foot home is extrapolation because 4,900 square feet is inside the range of the areas in the data. Using the model to predict the price of a 4,900 square foot home is interpolation because 4,900 square feet is outside the range of the areas in the data.

The data represent the heights in feet and the number of stories of the tallest buildings in a city. Height, x 793 388 825 404 439 550 931 669 532 Stories, y 57 26 58 31 33 36 60 49 34 Scatter Plot for the Heights (in Feet) and Number of Stories of the Buildings. 90- 50- 40- 30- 20- 10- 100 1200 400 300 700 Part: 0 /3 Part 1 of 3 Find the value for r. Round your answer to at least three decimal places.

ExQm Score Oata 76 69 64 75 70 80 90 86 77 79 82 96 51 64 95 6.2 76 65 68 74 Oc Mahe a box- and whiskar plot d alculus data Exam

4. Draw a line / curve of best fit for the following sets of data & identify if they are linear, quadratic or exponential: 2500 1500 1000 500 300 200 -100 25 50