Use the model to predict the percentage of female high school graduates in 2029. Round to thenearest tenth (1 decimal place). The percentage of female high school graduates whoenrolled in college is given in the table below.College EnrollmeYearfor Females197025.5198030.3199038.3200045.6201050.5Let xx represent time (in years) since 1970, and let yrepresent the corresponding percentage of femalehigh school graduates who enrolled in college.Find the cubic regression modely=F(x)=ax^3+bx^2+cx+dwhere aa is rounded to 6 decimal places, bb isrounded to 4 decimal places, cc is rounded to 3decimal places, and dd is rounded to 1 decimal place.

When we want to estimate an impact of many variables on the one variable at that time we…

Answered: Use the model to predict the percentage…

College Algebra

1st Edition

ISBN:9781938168383

Author:Jay Abramson

Publisher:Jay Abramson

Chapter6: Exponential And Logarithmic Functions

Section6.8: Fitting Exponential Models To Data

Problem 3TI: Table 6 shows the population, in thousands, of harbor seals in the Wadden Sea over the years 1997 to...

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What does the y -intercept on the graph of a logistic equation correspond to for a population modeled by that equation?
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?
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?
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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?
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It is believed that the electrical energy that a chemical plant consumes each month is related to the average environmental temperature, the number of days in the month, the average purity of the product, and the tons of product manufactured. Historical data is available for the previous year and is presented in the following table (seen in the image=. Perform simple linear regression analysis
A virus, due to human error, escaped a research facility in an isolated island. A day after the breach, 7 test subjects were found to be infected. On the sixth day, the count increased to 21 infected. The number increased to 63 infected on the 11th day, 189 on the 16th day, and 567 on the 21st day. If no intervention happened, create a regression model to quantify the infected test subjects after "t" days, graph the function, and, determine the number of infected after 60 days. Use exponential regression model.
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Use the model to predict the percentage of female high school graduates in 2029. Round to the nearest tenth (1 decimal place).