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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?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?
- 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.Consider a hypothetical regression predicting if someone will be married or not by the age of 40, MARRIED? (1 means this person is married by the age of 40 and 0 means this person is not married by the age of 40). The regression is as follows (all variables are statistically significant): MARRIED? = 0.2 + 0.03*EDUCATION - 0.01*BMI Where EDUCATION is the number of years of education someone's had and BMI is their body mass index. Suppose someone had 20 years of education and a BMI of 25. What is the predicted value of MARRIAGE? 0.35, which makes sense even though MARRIED? can only be a zero or one 0, because the calculated value is 0.35 so we round down. 0.55, which makes sense even though MARRIED? can only be a zero or one. Calculating a predicted value should not be done here because the dependent variable is a dummy variable. 1, because the calculated value is 0.55 so we round up.Consider a hypothetical regression predicting if someone will be married or not by the age of 40, MARRIED? (1 means this person is married by the age of 40 and 0 means this person is not married by the age of 40). The regression is as follows (all variables are statistically significant): MARRIED? = 0.2 + 0.03*EDUCATION - 0.01*BMI Where EDUCATION is the number of years of education someone's had and BMI is their body mass index. Suppose someone had 20 years of education and a BMI of 25. What is the predicted value of MARRIAGE? a 0, because the calculated value is 0.35 so we round down. b 0.55, which makes sense even though MARRIED? can only be a zero or one. c 0.35, which makes sense even though MARRIED? can only be a zero or one d Calculating a predicted value should not be done here because the dependent variable is a dummy variable. e 1, because the calculated value is 0.55 so we round up.
- Consider a hypothetical regression predicting if someone will be married or not by the age of 40, MARRIED? (1 means this person is married by the age of 40 and 0 means this person is not married by the age of 40). The regression is as follows (all variables are statistically significant): MARRIED? = 0.2 + 0.03*EDUCATION - 0.01*BMI Where EDUCATION is the number of years of education someone's had and BMI is their body mass index. Suppose someone had 20 years of education and a BMI of 25. Complete this sentence: For every additional year of education someone has:... a ...their chance of getting married by 40 increases by 0.03 percentage points. b ...their chance of getting married by 40 increases by 3 percentage points. c ...their chance of getting married by 40 increases by 0.03. d ...their chance of getting married by 40 increases by 3%. e This regression means nothing because the dependent variable is a dummy variable.Consider a hypothetical regression predicting if someone will be married or not by the age of 40, MARRIED? (1 means this person is married by the age of 40 and 0 means this person is not married by the age of 40). The regression is as follows (all variables are statistically significant): MARRIED? = 0.2 + 0.03*EDUCATION - 0.01*BMI Where EDUCATION is the number of years of education someone's had and BMI is their body mass index. Suppose someone had 20 years of education and a BMI of 25.When is a variable in a regression statistically significant? 1 When p is more than alpha. 2 When p is more than R2. 3 When p is less than alpha. 4 When p is less than R2. 5 When p is less than the coefficient.
- Consider the following sample regression equation yˆ = 150 − 20x, where y is the demand for Product A (in 1,000s) and x is the price of the product (in $). The slope coefficient indicates that if _____Suppose that R2= 1 for a data set. What can you say abota. SSE? b. SSR? c. the utility of the sample multiple linear regression equation for making predictions?The certain study seeks to investigate whether negative life events, family environment, family violence, media violence, and depression are predictors of youth aggression and bullying. Which of the following statistical tests is most appropriate to answer this objective? a. Linear Regression b. McNemar's Test c. Chi-square Test d. Multiple Logistic Regression
