State whether the following statements are true or false. If the statement is false, write the correct answer: Qualitative Methods are more accurate, for they involve analyzing numeric data and taking decisions based on previous numbers and statistics. Answer: Quantitate data is used in decision making especially for companies and organization that are new in the market. Answer:
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A:
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A: Given, 1 16 320 2 12 265 3 18 375 4 14 300
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State whether the following statements are true or false. If the statement is false, write the correct answer:
- Qualitative Methods are more accurate, for they involve analyzing numeric data and taking decisions based on previous numbers and statistics.
Answer:
- Quantitate data is used in decision making especially for companies and organization that are new in the market.
Answer:
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Solved in 2 steps
- Stock market analysts are continually looking for reliable predictors of stock prices. Consider the problem of modeling the price per share of electric utility stocks (Y). Two variables thought to influence this stock price are return on average equity (X1) and annual dividend rate (X2). The stock price, returns on equity, and dividend rates on a randomly selected day for 16 electric utility stocks are provided in the file P13_15.xlsx. Estimate a multiple regression equation using the given data. Interpret each of the estimated regression coefficients. Also, interpret the standard error of estimate and the R-square value for these data.The file P14_01.xlsx contains data on 100 consumers who drink beer. Some of them prefer light beer, and others prefer regular beer. A major beer producer believes that the following variables might be useful in discriminating between these two groups: gender, marital status, annual income level, and age. a. Use logistic regression to classify the consumers on the basis of these explanatory variables. How successful is it? Which variables appear to be most important in the classification? b. Consider a new customer: Male, Married, Income 42,000, Age 47. Use the logistic regression equation to estimate the probability that this customer prefers Regular. How would you classify this person?Suppose that a regional express delivery service company wants to estimate the cost of shipping a package (Y) as a function of cargo type, where cargo type includes the following possibilities: fragile, semifragile, and durable. Costs for 15 randomly chosen packages of approximately the same weight and same distance shipped, but of different cargo types, are provided in the file P13_16.xlsx. a. Estimate a regression equation using the given sample data, and interpret the estimated regression coefficients. b. According to the estimated regression equation, which cargo type is the most costly to ship? Which cargo type is the least costly to ship? c. How well does the estimated equation fit the given sample data? How might the fit be improved? d. Given the estimated regression equation, predict the cost of shipping a package with semifragile cargo.
- The model in Example 9.3 has only two market outcomes, good and bad, and two corresponding predictions, good and bad. Modify the decision tree by allowing three outcomes and three predictions: good, fair, and bad. You can change the inputs to the model (monetary values and probabilities) in any reasonable way you like. Then you will also have to modify the Bayes rule calculations. You can decide whether it is easier to modify the existing tree or start from scratch with a new tree.The owner of a restaurant in Bloomington, Indiana, has recorded sales data for the past 19 years. He has also recorded data on potentially relevant variables. The data are listed in the file P13_17.xlsx. a. Estimate a simple regression equation involving annual sales (the dependent variable) and the size of the population residing within 10 miles of the restaurant (the explanatory variable). Interpret R-square for this regression. b. Add another explanatory variableannual advertising expendituresto the regression equation in part a. Estimate and interpret this expanded equation. How does the R-square value for this multiple regression equation compare to that of the simple regression equation estimated in part a? Explain any difference between the two R-square values. How can you use the adjusted R-squares for a comparison of the two equations? c. Add one more explanatory variable to the multiple regression equation estimated in part b. In particular, estimate and interpret the coefficients of a multiple regression equation that includes the previous years advertising expenditure. How does the inclusion of this third explanatory variable affect the R-square, compared to the corresponding values for the equation of part b? Explain any changes in this value. What does the adjusted R-square for the new equation tell you?The management of a technology company is trying to determine the variable that best explains the variation of employee salaries using a sample of 52 full-time employees; see the file P13_08.xlsx. Estimate simple linear regression equations to identify which of the following has the strongest linear relationship with annual salary: the employees gender, age, number of years of relevant work experience prior to employment at the company, number of years of employment at the company, or number of years of post secondary education. Provide support for your conclusion.
- An antique collector believes that the price received for a particular item increases with its age and with the number of bidders. The file P13_14.xlsx contains data on these three variables for 32 recently auctioned comparable items. Estimate a multiple regression equation using the given data. Interpret each of the estimated regression coefficients. Is the antique collector correct in believing that the price received for the item increases with its age and with the number of bidders? Interpret the standard error of estimate and the R-square value for these data.In the sampling, hypothesis testing and analysis of business research data, which of the following is true? Group of answer choices 1. We make inferences about the population parameters based on sample statistics 2. The sample should be representative of the population 3. A larger sample size is always better 4. Answers1 and 2 only 5. Answers 1, 2 and 3Which modeling techniques are suitable to build a predictive model ? (Multiple answer may be correct) Group of answer choices Multiple linear regression Classification tree Logistic regression Clustering
- Business analytics concepts a) Data used in business analytics need to be reliable and valid. Explain your opinion on the statement and provide practical examples to support your answer. b) What are the similarities and differences between correlation analysis and regression analysis? c) What types of measurements can be used to measure the central tendency of the data? Please compare the advantages and disadvantages between these measurements.What is the advantages of using predictive analyctics? Explain in 250 words. Say that it improves decision making, making competitive advantage, improves risk management, and others.Which best describes the null hypothesis associated with an Analysis of Variance (ANOVA)? Group of answer choices a. Ho: Variance 1 = Variance 2 = Variance 3 b. Ho: Standard Deviation 1 = Standard Deviation 2 = Standard Deviation 3 c. Ho: Proportion 1 = Proportion 2 = Proportion 3 d. Ho: Median 1 = Median 2 = Median 3 e. Ho: Mean 1 = Mean 2 = Mean 3