1/20/2016 Integrated Math 10 - Santowski 1 Lesson 13 Introducing Exponential Equations Integrated Math 10 - Santowski.

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Presentation transcript:

1/20/2016 Integrated Math 10 - Santowski 1 Lesson 13 Introducing Exponential Equations Integrated Math 10 - Santowski

Comparing Linear & Exponential Models Data set #1 – Can this data be modeled with a linear relation? Why/why not? How do you know? Data set #2 - Can this data be modeled with a linear relation? Why/why not? How do you know? 1/20/2016 Integrated Math 10 - Santowski 2 x y x y

HINT Any obvious number patterns in the data?? 1/20/2016 Integrated Math 10 - Santowski 3

Comparing Linear & Exponential Models Data set #3 – Can this data be modeled with a linear relation? Why/why not? How do you know? Data set #4 - Can this data be modeled with a linear relation? Why/why not? How do you know? 1/20/2016 Integrated Math 10 - Santowski 4 x y x y

Comparing Linear & Exponential Models Data set #5 – Can this financial data be modeled with a linear relation? Why/why not? How do you know? What equation summarizes the data relationship and what do the #’s in the eqn represent? Data set #6 - Can this financial data be modeled with a linear relation? Why/why not? How do you know? What equation summarizes the data relationship and what do the #’s in the eqn represent? 1/20/2016 Integrated Math 10 - Santowski 5 x y x y

Summary What NEW pattern/relationships have you seen in the data sets? How can we write equations for our data that reflect our new patterns/relationships? 1/20/2016 Integrated Math 10 - Santowski 6

Summary How can we write equations for our data that reflect our new patterns/relationships? If you have correctly answered this question, go to slides # How can we write equations for our data that reflect our new patterns/relationships? If you have NOT correctly answered this question, go to slides #9 - #11 1/20/2016 Integrated Math 10 - Santowski 7

1/20/2016 Integrated Math 10 - Santowski 8 Additional data sets (if needed)

Exploring Exponential Equations Data set #A – This data can be modeled with a exponential relation. How do you know? What “equation” summarizes the data relationship? Data set #B - This data can be modeled with a exponential relation. How do you know? What “equation” summarizes the data relationship? 1/20/2016 Integrated Math 10 - Santowski 9 x y x y

Exploring Exponential Equations Data set #C – This data can be modeled with a exponential relation. How do you know? What “equation” summarizes the data relationship? Data set #D - This data can be modeled with a exponential relation. How do you know? What “equation” summarizes the data relationship? 1/20/2016 Integrated Math 10 - Santowski 10 x y x y

Exploring Exponential Equations Data set #E – This data can be modeled with a exponential relation. How do you know? What “equation” summarizes the data relationship? Data set #F - This data can be modeled with a exponential relation. How do you know? What “equation” summarizes the data relationship? 1/20/2016 Integrated Math 10 - Santowski 11 x y x y

1/20/2016 Integrated Math 10 - Santowski 12 Application of Exponential Models

Modeling Example #1 Investment data – Mr. S has invested some money for Andrew’s post- secondary education (not too hopeful for an athletic scholarship for my son!!!!) (a) How can you analyze the numeric data (no graphs) to conclude that the data is exponential  i.e how do you know the data is exponential rather than linear? (b) Graph the data on a scatter plot (c) Write an equation to model the data. Define your variables carefully. 1/20/2016 Integrated Math 10 - Santowski 13 Time (years) Value of investment (000’s $)

Modeling Example #2 The following data table shows the relationship between the time (in hours after a rain storm in Manila) and the number of bacteria (#/mL of water) in water samples from the Pasig River: (a) How can you analyze the numeric data (no graphs) to conclude that the data is exponential  i.e how do you know the data is exponential rather than linear? (b) Graph the data on a scatter plot (c) Write an equation to model the data. Define your variables carefully. 1/20/2016 Integrated Math 10 - Santowski 14 Time (hrs) # of Bacteria

Modeling Example #3 The value of Mr. S’s car is depreciating over time. I bought the car new in 2002 and the value of my car (in thousands) over the years has been tabulated below: (a) How can you analyze the numeric data (no graphs) to conclude that the data is exponential  i.e how do you know the data is exponential rather than linear? (b) Graph the data on a scatter plot (c) Write an equation to model the data. Define your variables carefully. 1/20/2016 Integrated Math 10 - Santowski 15 Year Value

Modeling Example #4 The following data table shows the historic world population since 1950: (a) How can you analyze the numeric data (no graphs) to conclude that the data is exponential  i.e how do you know the data is exponential rather than linear? (b) Graph the data on a scatter plot (c) Write an equation to model the data. Define your variables carefully. 1/20/2016 Integrated Math 10 - Santowski 16 Year Pop (in millions)

SUMMARY Write a SINGLE equation that summarizes the data relationships you have investigated this lesson. 1/20/2016 Integrated Math 10 - Santowski 17

1/20/2016 Integrated Math 10 - Santowski 18 SUMMARY  Exponential Growth Equations In general, the algebraic model for exponential growth is y = c(a) x where a is referred to as the growth rate (provided that a > 1) and c is the initial amount present and x is the number of increases given the growth rate conditions. All equations in this section are also written in the form y = c(1 + r) x where c is a constant, r is a positive rate of change and 1 + r > 1, and x is the number of increases given the growth rate conditions.

1/20/2016 Integrated Math 10 - Santowski 19 SUMMARY  Exponential Decay Equations In general, the algebraic model for exponential decay is y = c(a) x where a is referred to as the decay rate (and a is < 1) and c is the initial amount present. All equations in this section are in the form y = c(1 + r) x or y = ca x, where c is a constant, r is a rate of change (this time negative as we have a decrease so 1 + r < 1), and x is the number of increases given the rate conditions