4.  (-1,9) (2,6) and (3,13)

5.  When given a table of values:  Press STAT button  Select option 1:EDIT  Plug X values into L1  Plug Y values into L2  Press STAT, go right one tab to CALC  Select option 5:QuadReg

6.  Find a quadratic model from the table given, using the number of years after 1990 as x  Estimate the number of stores in 2006, is that close to the actual number?  Estimate the number of stores in 2010. YearStoresYearStores 199211320002119 199316320012925 199426420023756 199543020034453 199666320045452 199797420056423 1998132120067715 1999165720079401

7.  To determine which model is better, linear or quadratic, take a look at the first and second differences of the y values in the table  If the first differences are more constant, than a linear model is better  If the second differences are more constant, than a quadratic model is better

8. Time x (seconds) Height (meters) 168.6 2117.6 3147 4156.8 5147 6117.6 768.6

9. Year1990199119921993199419951996 Recipient Families (thousands) 4218470849365050497946414166 Find the first and second differences for the data to justify that a quadratic model is better than a linear model Find the quadratic model for the data set What does the model give for a maximum number of families who were recipients of federal aid during the period 1990-1996.

10.  Same steps as finding quadratic model  Except instead of selecting QuadReg, select option A:PwrReg Edge LengthSurface Area of Cube 16 224 354 496 5150

11.  The noise level of a Vauxhall VX220 increases as the speed of the car increases. The table to the right gives the noise, in decibels (db), at different speeds  Fit a power function model to the data.  Use the result above to estimate the noise level at 80 mph. Speed (mph)Noise Level (db) 1050 3068 5075 7079 10084

12.  The following table gives the number of cohabiting (not married) households (in thousands) for selected years between 1960 and 2004.  Find a power function that models this data. yearCohabiting Households (thousands) yearCohabiting Households (thousands) 196043919933510 197052319943661 1980158919953668 1985198319963958 1990285619974130 1991303919984236 1992330820005476 20045841

13.  In the last example, we found a power function that is a good fit for the data given. However, a linear or quadratic model may also be a good fit as well.  A quadratic function may fit the data even if there is no obvious ‘turning point’ in the graph of the data points.  If the data points appear to rise (or fall) more rapidly than a line, than a quadratic or power model may fit the data well.  In some cases it may be necessary to find both models to determine which is a better fit for the data  The better fit is the line that the data points fall closer to.

14.  The table to the right shows the percentage of voting-age population who voted in presidential elections for the years 1960- 2004  Use x values where x in years after 1950  Find the quadratic model that fits the data  Find the power model that fits the data  Discuss the models to predict voting after 2004.  Which model would be better fit if a point were added giving the percent voting as 58.1 in 2008? Year% % 196063.1198453.1 196461.9198850.1 196860.8199255.1 197255.2199649.1 197653.6200051.3 198052.6200455.3

15.  Pages 228-236  3,5,8,9,12,13,17,19,21,24,27,31,33,34,36,39,40

16.  Pages 239-243  2-58 Even