Warm Up. Exponential Regressions and Test Review.

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Warm Up classes? cancel college the will days many how After ill. are students the of more or 40% when classes cancel will college The b) days? 5 after infected are students many How a) days. x after infected of number total the is y where 4999e 1 5000 y by modeled is virus the of spread The virus. flu contagious a with vacation from returns student one students, 5000 of campus college a On ) 4 0 6 e 5 e : x for Solve ) 3 2 log 4 8 log : Evaluate ) 2 ) 4 x ( 2log y : function the of inverse the Write ) 1 0.8x - x 2x 8 8 3 1 3

Transcript of Warm Up. Exponential Regressions and Test Review.

Page 1: Warm Up. Exponential Regressions and Test Review.

Warm Up

classes? cancel college

the will days many how After ill. are students the of

more or 40% when classes cancel will college The b)

days? 5 after infected are students many How a)

days. x after infected of number total the is ywhere 4999e15000

yby modeled is virus the of spread The

virus. flu contagious a with vacation from returns

student one students, 5000 of campus college a On )4

06e5e :x for Solve )3

2log48log :Evaluate )2

)4x(2log y:function the of inverse the Write)1

0.8x-

x2x

8831

3

Page 2: Warm Up. Exponential Regressions and Test Review.

Exponential Regressionsand

Test Review

Page 3: Warm Up. Exponential Regressions and Test Review.

Exponential Models for data…• In a research experiment, a population of fruit flies was

introduced into an environment and recorded below.

Elapsed time

(in days)

Population of fruit flies

0 252 286 33

10 3915 4919 5924 7430 96

1) Use your calculator to write an exponential model to best fit the data and PASTE it into Y=

2) Use your model to predict the number of fruit flies after 40 days.

3) Use your model to determine the elapsed time when then population of fruit flies was 65.

Page 4: Warm Up. Exponential Regressions and Test Review.

Time (L1)

Temp (L2)

1 184.35 164.07 155.1

10 143.714 130.518 119.920 115.223 109.2628 101

Time (L1)

Temp (L2)

32 95.72838 89.5645 84.3650 81.6260 77.8370 75.5380 74.1488 73.43

100 72.78

• A temperature probe was placed in a cup of hot coffee then removed and held in a room that was 70°. The temperature of the probe was recorded below.

Page 5: Warm Up. Exponential Regressions and Test Review.

TRASHKETBALL

Page 6: Warm Up. Exponential Regressions and Test Review.

Test Review

1) Determine a function for the

logistic function whose graph

is shown in the figure.

2) What is the total value after 6 years

of an initial investment of $2250 that

earns 7% APR compounded quarterly?

Page 7: Warm Up. Exponential Regressions and Test Review.

CALCULATOR ACTIVE

The population of a town is 350,000 and is increasing at the rate of 4.65% every year.

1. Write a function that determines the population of the town in terms of the number of years.

2. After how many years will the population be one million? (Round to 3 decimal places)

3. What will be the population in 30 years?

Page 8: Warm Up. Exponential Regressions and Test Review.

1. The half life of a certain substance is 60 days. How much of 24 grams will be left after 100 days?

2. How long will it take an account that compounds continuously at 6% APR to double in value?

3. A certain account has accumulated $10,000 in 5 years. The account has earned 4%APR compounded monthly. What was the initial deposit?

CALCULATOR ACTIVE

Page 9: Warm Up. Exponential Regressions and Test Review.

Calculator ActiveThe table shows the population of

Aurora, CO for selected years between 1950 and 2003.

1. Use logistic regression to find a logistic curve to model the data. Write your model rounding to 3 decimal places.

2. Based on the regression equation, what is the carrying capacity of Aurora, CO.? (3 decimal places)

3. Based on the regression equation, after how many years will the population exceed 300,000? (3 decimal places)

4. Based on the regression equation, what was the (approximate) population in 1965?

(nearest whole number).

Years after 1950

Population

0 11,421

20 74,974

30 158,588

40 222,103

50 275,923

53 290,418

Page 10: Warm Up. Exponential Regressions and Test Review.

NO Calculator

Solve for x:

1. log4256 = x

2.

3.

1log 2

4x

1

125

4log

3x

Page 11: Warm Up. Exponential Regressions and Test Review.

NO Calculator

5 5 5

11) log 3log 4 log 64

3 x

62 log2) 6 9x

12 12 12

13) 2log log 9 3log 3

2 x

Page 12: Warm Up. Exponential Regressions and Test Review.

NO Calculator1) logb1 = x

2

12) log

b xb

3) Simplify completely2 3ln e x

Page 13: Warm Up. Exponential Regressions and Test Review.

NO Calculator

Solve for x:

1. log2x2 – log2(x+3) = 2

2. log4x + log4(x – 3) = log77

Page 14: Warm Up. Exponential Regressions and Test Review.

1. Write as a single logarithm:

2. Write as simplified logarithms

1log log( 1) log(2 )2

x x x

2

2log

1

x

x

Page 15: Warm Up. Exponential Regressions and Test Review.

Solve for x. NO CALCULATOR

1) 2 + 3e-x = 8 2) 3e2x + 8ex = 3

Page 16: Warm Up. Exponential Regressions and Test Review.

Solve for x. NO CALCULATOR

1) 8x+1 = 10 2)

xe 5.41

3010

xe 5.41

3010

Page 17: Warm Up. Exponential Regressions and Test Review.

Calculator ActiveThe winning times in the women’s 100 meter freestyle event at the Summer Olympic Games since 1952 are shown. Let x = 0 be 1900…

1) Assuming the data approaches a horizontal asymptote of y = 52, write an exponential model that best fits the data.

2) According to this model, what would be the winning time for the women’s freestyle at the 2008 Olympics? Round to 3 decimal places.

YearsSince 1900

52 56 60 68 72 76 80 84 88 92 96 100

Time 66.8 62 61.2 60 58.6 55.7 54.8 55.9 54.9 54.6 54.5 53.8