When conducting a logistic regression analysis, what is the problem of class imbalance in a dataset? Question 2 options: When the number of observations in one class is too similar to the number of observations in the other class When the number of observations in one class exactly equals the number of observations in the other class. When the number of observations in one class is substantially smaller than the number of observations in the other class When the number of observations in one class does not exactly equal the number of observations in the other class

When conducting a logistic regression analysis, what is the problem of class imbalance in a dataset? Question 2 options: When the number of observations in one class is too similar to the number of observations in the other class When the number of observations in one class exactly equals the number of observations in the other class. When the number of observations in one class is substantially smaller than the number of observations in the other class When the number of observations in one class does not exactly equal the number of observations in the other class

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 might a scatterplot of data points look like if it were best described by a logarithmic 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?
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?
Cable TV The following table shows the number C. in millions, of basic subscribers to cable TV in the indicated year These data are from the Statistical Abstract of the United States. Year 1975 1980 1985 1990 1995 2000 C 9.8 17.5 35.4 50.5 60.6 60.6 a. Use regression to find a logistic model for these data. b. By what annual percentage would you expect the number of cable subscribers to grow in the absence of limiting factors? c. The estimated number of subscribers in 2005 was 65.3million. What light does this shed on the model you found in part a?
What situations are best modeled by a logistic equation? Give an example, and state a case for why the example is a good fit.
World Population The following table shows world population N, in billions, in the given year. Year 1950 1960 1970 1980 1990 2000 2010 N 2.56 3.04 3.71 4.45 5.29 6.09 6.85 a. Use regression to find a logistic model for world population. b. What r value do these data yield for humans on planet Earth? c. According to the logistic model using these data, what is the carrying capacity of planet Earth for humans? d. According to this model, when will world population reach 90 of carrying capacity? Round to the nearest year. Note: This represents a rather naive analysis of world population.
Does a linear, exponential, or logarithmic model best fit the data in Table 2? Find the model.
In logistic regression, the predictors are dichotomous, and the outcome is a continuous variable.
In logistic regression, the outcome variable is: Question 10 options: Independent There is no outcome variable Categorical Continuous
For which of the following business purposes would you use logistic regression? Question 15 options: To predict the number of products that will be sold To split customers into groups for specific marketing communications To predict the probability of a credit card transaction being fraudulent To forecast revenue for the next month
The use of multiple logistic regression is warranted when there are two or more independent quantitative or nominal variables and one dichotomous dependent variable. a. True b. False

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	When the number of observations in one class is too similar to the number of observations in the other class
	When the number of observations in one class exactly equals the number of observations in the other class.
	When the number of observations in one class is substantially smaller than the number of observations in the other class
	When the number of observations in one class does not exactly equal the number of observations in the other class