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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?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?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.
- What situations are best modeled by a logistic equation? Give an example, and state a case for why the example is a good fit.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?In logistic regression, the outcome variable is: Question 10 options: Independent There is no outcome variable Categorical Continuous
- Can gender, educational level, and age predict the odds that someone votes for a particular candidate in the election? Over 1500 voters were selected, and data were col-lected on the highest year of school completed, their age, and their gender. We wish to t a logistic regression model: log(1 p p ) = 0 + 1Age + 2Education + 3Gender, where p is the binomial probability that a person voted for candidate Johnson, and gender is coded as the indicator for female. The R output is given below. parameter df estimate s.e z p-value (Intercept) 1 .1119 .3481 .321 .748 Age 1 .0020 .0032 .613 .54 Education 1 -.0100 .0184 .547 .585 Gender 1 .4282 .1040 4.117 .000 Null deviance: 255.95 on 1499 degrees of freedom Residual deviance: 220.80 on 1496 degrees of freedom Write down the tted logistic regression and give a short summary about the data analysis . Calculate the probability of voting for Johnson for a…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 classQuestion 6 Having studied Fixed Income Securities, you are now working as an analyst for a well known bond fund. Your manager asks you to replicate the JP Morgan T-Bond Index using a tracking error minimization approach. You are to replicate this index as closely as possible using a medium duration Treasury bond (M-BOND) and a long duration Treasury bond (L-BOND). These expire in approximately 7.15 years’ and 29.25 years’ time respectively. The following variance-covariance matrix, based on daily returns over the preceding six months, is given to you to use in your replication: Note: As usual, variances are given on the diagonal, e.g. the variance of M-BOND is 0.0042%. As usual, covariances appear in the non-diagonal elements, e.g. the covariance of M-BOND and L-BOND is 0.0057%. (a) Suppose the optimal weights for M-BOND and L-BOND are 0.7 and 0.3. Calculate the expected tracking error of the portfolio and explain how you interpret this number. (b) Calculate the correlation matrix…
- Scenario: You conduct research to assess the extent to which regulatory reports are late (in days). As potential contributors to late reports you want to simultaneously investigate company site (MN, CA, NY, TX), reporting agency (Agencies 1, 2, and 3), and report format (Long form, Short form) . What research analysis would be most appropriate? Group of answer choices a. ANOVA b. Binary Logistic Regression c. Chi-square Contingency Table d. Non-parametric e. RegressionConsider a simple logistic model in which the response variable is attendance at a conference (yes = 1; no = 0), and the sole independent variable is a college degree (degree = 1; no degree = 0). The estimated odds ratio for the college degree variable is 1.25. What is its proper interpretation?Question 3. Answer “YES “ or “No” to the following questions a. In ANOVA completely randomized design if you have more than two missing values, Analysis Toolpak in EXCEL will calculate F.YESNO b. In ANOVA you cannot use log transformed values if there are “zeros” in the data YESNo c. In ANOVA correction term can be larger than ƩY2 YESNO d. In ANOVA , my calculated F value was – 23.4. This is perfectly OK.YESNO e. In ANOVA, my raw data was multiplicative and so I log-transformed them and it became additive. Is this procedure correct? YESNO