Answered: In using a double exponential smoothing…

In using a double exponential smoothing method, we calculated S3 to be 2,255.6 and G3 to be 12.2. D4 is 1,916 and alpha and beta values are 0.4, and 0.53, resp

College Algebra

1st Edition

ISBN:9781938168383

Author:Jay Abramson

Publisher:Jay Abramson

Chapter6: Exponential And Logarithmic Functions

Section6.7: Exponential And Logarithmic Models

Problem 19TI: Does a linear, exponential, or logarithmic model best fit the data in Table 2? Find the model.

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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?
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?
Does a linear, exponential, or logarithmic model best fit the data in Table 2? Find the model.
Find the mean hourly cost when the cell phone described above is used for 240 minutes.
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?
An individual has been exercising at a local gym for about 10 years. He always begins with a 10- to 15-minute session on the treadmill at a speed of 3.7 mph. During a 12-day period in 2010, he recorded his heart rate before using the treadmill and after 5 minutes of use. The data are shown in Table 8.37 (image attached). The subject also recorded his heart rate at baseline and 5 minutes after starting the treadmill exercise (at a speed of 2.5 mph) in 2000. The data are shown in Table 8.38 (image attached). Implement a test to compare the baseline heart rate initially (2000) and after 10 years of experience (2010), and report a two-sided p value.
A regression was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y).The results of the regression were:ŷ=a+bx a=38.98 b=-1.245 r2=0.802816 r=-0.896 Use this to predict the number of situps a person who watches 9 hours of TV can do (to one decimal place)
Consider the following log-wage regression results for women (W) and men (M) where wages are predicted by schooling (S) and age (A). wW = 2.23 + 0.077Sw + 0.017Aw and wM = 2.33 + 0.0745SM + 0.026AM. Sample means for the variables by gender are: women average a logged wage of 3.90, 12.7 years of schooling, and 40.8 years-old; men average a logged wage of 4.53, 14.2 years of schooling, and 43.9 years-old. Decompose the raw difference in average logged wages using the Oaxaca-Blinder decomposition. Specifically, decompose the raw difference into the portion due to differences in schooling, differences in age, and the portion left unexplained, possibly due to gender discrimination.
The manager of a concert venue wants to determine a model for the total number of tickets sold each day for an upcoming concert. She collects the data in the following table. Total Number of Tickets Month Sold 1 20 a) Sketch a scatter plot of the data using the DESMOS graphing calculator on the next page 338 3. 439 b) On the next page on DESMOS graphing calculator, generate an exponential model for this data using a regression equation 571 743 964 a) Sketch a scatter plot of the data using the DESMOS graphing calculator on the next page b) On the next page on DESMOS graphing calculator, generate an exponential model for this data using a regression equation. c) Write the equation of the exponential model using the DESMOS graph? d) Use the DESMOS graph to predict how many tickets will be sold after 12 days. e) Use DESMOS to predict how many days after tickets go on sale the company will sell 5000 tickets
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A highway employee performed a regression analysis of the relationship between the number of construction work-zone fatalities and the number of unemployed people in a state. The regression equation is Fatalities = 12.7 + 0.000114 (Unemp). Some additional output is: Predictor Coef SE Coef T P Constant 12.726 8.115 1.57 0.134 Unemp 0.00011386 0.00002896 3.93 0.001 Analysis of Variance Source DF SS MS F P Regression 1 10,354 10,354 15.46 0.001 Residual Error 18 12,054 670 Total 19 22,408 a. How many states were in the sample? b. Determine the standard error of estimate. (Round your answer to 2 decimal places.) c. Determine the coefficient of determination. (Round your answer to 2 decimal places.) d. Determine the correlation coefficient. (Round your answer to 2 decimal places.)
A regression was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y). The results of the regression were: b=35.976 7²-0.509796 T=-0.714 Use this to predict the number of situps a person who watches 7 hours of TV can do (to one decimal place)