Xy1742a =23896807decimal place values.Use regression to find an exponentialequation that best fits the data above. Theequation has form y = ab where:b =decimal place values.Question Help: VideoCalculator41045Submit Question5114961201Round to twoRound to two

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Answered: X y 1 742 a = 2 807 3 896 decimal place…

Algebra & Trigonometry with Analytic Geometry

13th Edition

ISBN:9781133382119

Author:Swokowski

Publisher:Swokowski

Chapter5: Inverse, Exponential, And Logarithmic Functions

Section5.6: Exponential And Logarithmic Equations

Problem 70E

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Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s pole vault height has fallen below the value predicted by the regression line in Example 2. This might have occurred because when the pole vault was a new event there was much room for improvement in vaulters’ performances, whereas now even the best training can produce only incremental advances. Let’s see whether concentrating on more recent results gives a better predictor of future records. (a) Use the data in Table 2 (page 176) to complete the table of winning pole vault heights shown in the margin. (Note that we are using x=0 to correspond to the year 1972, where this restricted data set begins.) (b) Find the regression line for the data in part ‚(a). (c) Plot the data and the regression line on the same axes. Does the regression line seem to provide a good model for the data? (d) What does the regression line predict as the winning pole vault height for the 2012 Olympics? Compare this predicted value to the actual 2012 winning height of 5.97 m, as described on page 177. Has this new regression line provided a better prediction than the line in Example 2?
Table 6 shows the population, in thousands, of harbor seals in the Wadden Sea over the years 1997 to 2012. a. Let x represent time in years starting with x=0 for the year 1997. Let y represent the number of seals in thousands. Use logistic regression to fit a model to these data. b. Use the model to predict the seal population for the year 2020. c. To the nearest whole number, what is the limiting value of this model?
Table 2 shows a recent graduate’s credit card balance each month after graduation. a. Use exponential regression to fit a model to these data. b. If spending continues at this rate, what will the graduate’s credit card debt be one year after graduating?
If your graphing calculator is capable of computing a least-squares sinusoidal regression model, use it to find a second model for the data. Graph this new equation along with your first model. How do they compare?
What does the y -intercept on the graph of a logistic equation correspond to for a population modeled by that equation?
The following fictitious table shows kryptonite price, in dollar per gram, t years after 2006. t= Years since 2006 0 1 2 3 4 5 6 7 8 9 10 K= Price 56 51 50 55 58 52 45 43 44 48 51 Make a quartic model of these data. Round the regression parameters to two decimal places.
Can you tell how old a lion is by looking at its nose? A professor at the University of Wisconsin-Madison con-ducted a study of data taken from 32 lions and observed the relationship between age (in years) and proportionof blackness in the lion’s nose. The equation of the leastsquares regression line was yn = 0.8790 + 10.6471x where yn is the predicted age of the lion, measured inyears, and x is the proportion of the lion’s nose that isblack. A lion whose nose was 11% black was known to be1.9 years old. What is the residual for the age of this lion?a) -0.15 years b) 0.15 yearsc) 0.88 years d) 2.05 yearse) 10.65 years(Source: http://www.stat.wisc.edu/~st571-1/15-regression-4.pdf)
QQ2. Blood Pressure MeasurementsRight Arm 102 101 94 79 79Left Arm 175 169 182 146 144Find the best predicted systolic blood pressure in the left arm given that the systolic bloodpressure in the right arm is 100 mm Hg. d) Find the regression equation,e) Letting the first variable be the predictor (x) variable, find the indicated predicted value.
The table below shows the results of an experiment involving the growth of bacteria. Write a power regression equation for this set of data, rounding all values to three decimal places. Using this equation, predict the following… The bacteria’s growth, to the nearest integer, after 15 minutes. The amount of time, to the nearest minute, for there to be 4,000 bacteria present.
Run a simple linear regression in SPSS to know if previous experience (‘prevexp’: Previous Experience-months) significantly predicts current salary(‘salary’: Current Salary) in the work force . Use α =.05 Is the regression equation significant? That is, does Previous Experience explains (or predicts) a significant amount of variability in Current Salary? Report the F, df (of numerator, and df of the denominator) and p-value.
Run a simple linear regression in SPSS to know if previous experience (‘prevexp’: Previous Experience-months) significantly predicts current salary(‘salary’: Current Salary) in the work force . Use α =.05 Does Previous Experience significantly predict Current Salary? Report Beta(β), and the p-value (p).
Listed below are systolic blood pressure measurements (in mm Hg) obtained from the same woman. Find the regression equation, letting the right arm blood pressure be the predictor (x) variable. Find the best predicted systolic blood pressure in the left arm given that the systolic blood pressure in the right arm is 85 mm Hg. Use a significance level of 0.05. Right Arm Left Arm100 17499 16992 14876 14777 147

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X y 1 742 a = 2 807 3 896 decimal place values. Use regression to find an exponential equation that best fits the data above. The equation has form y = ab where: b = decimal place values. Question Help: Video Calculator Submit Question 4 1045 5 1149 6 1201 Round to two Round to two