Calculate the MAPE for each of the 3 forecast models used in the table. Which should be used for forecasting efforts, and why?
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Calculate the MAPE for each of the 3 forecast models used in the table.
Which should be used for forecasting efforts, and why?
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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?Distinguish between: a univariance function and multivariate function An exponential growth process and exponential decay processStrands of human hair absorb elements from the bloodstream and provide a historical record of both health and the use or nonuse of chemical substances. Hair grows at the rate of about one – half inch per month, and a person with long hair might be accused or absolved on the basis of a segment of hair that sprouted many months or years ago. By separately analyzing sections of the hair strand, scientists can even approximate the periods of time during which drug use was heavy, moderate, light or altogether absent. If a transit employee provides a strand of hair for drug testing, state the null and alternative hypothesis in verbal terms, then identify what would constitute a Type I error and Type II error in this situation.
- DiabetesA group of 10-year-old boys were first ascertained in acamp for diabetic boys. They had their weight measuredat baseline and again when they returned to camp 1 yearlater. Each time, a serum sample was obtained from whicha determination of hemoglobin A1c (HgbA1c) was made.HgbA1c (also called glycosylated hemoglobin) is routinelyused to monitor compliance with taking insulin injections.Usually, the poorer the compliance, the higher the HgbA1clevel will be. The hypothesis is that the level HgbA1c isrelated to weight. The data in Table 11.28 were obtained. **5 Compute a rank correlation between change inweight and change in HgbA1c, each over 1 year. Use this measure to directly test the hypothesis that change in weight over 1 year is related to change in HgbA1c. Report a two-tailed p-value, and provide a 95% confidence interval for the underlying rank correlationDiabetesA group of 10-year-old boys were first ascertained in acamp for diabetic boys. They had their weight measuredat baseline and again when they returned to camp 1 yearlater. Each time, a serum sample was obtained from whicha determination of hemoglobin A1c (HgbA1c) was made.HgbA1c (also called glycosylated hemoglobin) is routinelyused to monitor compliance with taking insulin injections.Usually, the poorer the compliance, the higher the HgbA1clevel will be. The hypothesis is that the level HgbA1c isrelated to weight. The data in Table 11.28 were obtained.11.92 What test can be performed to assess the relationship between weight and HgbA1c at the initial visit?11.93 Please perform the test in Problem 11.92, and reporta two-tailed p-value.11.94 Do the results in Problem 11.93 imply a relationshipbetween change in HgbA1c and change in weight for anindividual boy? Why or why not?11.95 Compute a rank correlation between change inweight and change in HgbA1c, each over 1 year. Use this…TRUE or False 1)Logistic regression cannot be employed when the dependent variable is binary. 2)In the average linkage clustering, the distance between two clusters is defined as the average of distances between all pairs of objects, where each pair is made up of one object from each group. 3)As Monte Carlo simulation is essentially statistical sampling, the larger the number of trials used, the more precise is the result. 4)Monte Carlo simulation is an inappropriate tool to analyze cash budgets because of the inherent uncertainty of the sales forecasts on which most cash budgets are based 5)Any solution that satisfies all constraints of a problem is called a feasible solution..
- What is the MA3 forecast for Year 4, Q1? What is the weighted MA3 forecast for Year 4, Q1 with weights of 0.5, 0.33, and 0.17? What is the Exponential Smoothing forecast for Year 4, Q1 using alpha = 0.3? What is the Adjusted Exponential Smoothing forecast for Year 4, Q1 using alpha = 0.3 and beta = 0.5?To determine characteristics associated with Y = whether a cancer patient achieved remission (1 = yes), a study used logistic regression. The most important explanatory variable was a labeling index (LI) that measures proliferative activity of cells after a patient receives an injection of tritiated thymidine. It represents the percentage of cells that are “labeled.” Table 4.8 shows the grouped data. Software reports Table 4.9 for a logistic regression model using LI to predict π = P (Y = 1). A. Show how software obtained π ̂ = 0.068 when LI = 8. B. Show that π ̂ = 0.50 when LI = 26.0 D. The lower quartile and upper quartile for LI are 14 and 28. Show that π ̂ increases by 0.42, from 0.15 to 0.57, between those values. E. When LI increases by 1, show the estimated odds of remission multiply by 1.16Say you have a regression where you are regressing wages on several variables, one being Years of Education (X1). In this regression, you estimate βˆx1 = 1.5 and it is statistically significant. Suppose you then find there is information of individuals SAT scores (an approximate measure of underlying ability) and add it to your regression as X2. You now get that βˆx1 = 0.5 and is still statistically significant, and you get βˆx2 = 0.1 and it is also statistically significant. What type of relationship to you suspect there is between Years of Education, SAT scored, and wages? Touch on the correlation of each variable to every other variable and explain the likely causal pathways. If cor(x1, y) ≡ 0.5, and cor(x2, y) ≡ 0.5, explain when regressing y on x1 and x2 gives an R2 of 0.5
- A study used logistic regression to determine characteristics associated with Y = whether a cancer patient achieved remission (1 = yes). The most important explanatory variable was a labeling index (LI) that measures proliferative activity of cells after a patient receives an injection of tritiated thymidine. It represents the percentage of cells that are “labeled.” Table 1 shows the grouped data. Software reports Table 2 for a logistic regression model using LI to predict π = P(Y = 1). Using information from Table 2, conduct a Wald test for the LI effect and Interpret. Using information from Table 2, construct a Wald confidence interval for the odds ratio corresponding to a 1-unit increase in LI and interpret. Using information from Table 2, conduct a likelihood-ratio test for the LI effect and interpret. Using information from Table 2, construct the likelihood-ratio confidence interval for the odds ratio and interpret.A study used logistic regression to determine characteristics associated with Y = whether a cancer patient achieved remission (1 = yes). The most important explanatory variable was a labeling index (LI) that measures proliferative activity of cells after a patient receives an injection of tritiated thymidine. It represents the percentage of cells that are “labeled.” Table 1 shows the grouped data. Software reports Table 2 for a logistic regression model using LI to predict π = P(Y = 1). Show how software obtained = 0.068 when LI = 8. Show that = 0.50 when LI = 26.0. Show that the rate of change in is 0.009 when LI = 8 and is 0.036 when LI = 26. The lower quartile and upper quartile for LI are 14 and 28. Show that increases by 0.42, from 0.15 to 0.57, between those values. When LI increases by 1, show the estimated odds of remission multiply by 1.16. Using information from Table 2, conduct a Wald test for the LI effect. Interpret. Using information from Table 2, construct a Wald…For the snoring and heart disease data of Table 3.1 (Section 3.2.3) with snoring-levelscores (0, 2, 4, 5), the logistic regression ML fit is logit [Pˆ(Y = 1)] = −3.866 +0.397x. Interpret the effect of snoring on the odds of heart disease.
