Describe three approaches to modeling seasonality in a regression forecast.
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Describe three approaches to modeling seasonality in a regression forecast.
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- Write down the fitted regression equation. If in the family, the primary caregiver is the biological mother, whose income is $50,000 and the youth live with the family most of the time. What predicted youth absent days in school?Researchers are interested in predicting the height of a child based on the heights of their mother and father. Data were collected, which included height of the child (height ), height of the mother ( mothersheight), and height of the father (fathersheight ). The initial analysis used the heights of the parents to predict the height of the child (all units are inches). The results of the analysis, a multiple regression, are presented below. . regress height mothersheight fathersheight Source Model Residual Total height mothersheight fathersheight _cons SS df 208.008457 314.295372 37 2 104.004228 8.49446952 MS 522.303829 39 13.3924059 Coef. Std. Err. .6579529 .1474763 .2003584 .1382237 9.804327 12.39987 t P>|t| 4.46 0.000 C 0.156 0.79 0.434 Number of obs = F( 2, 37) = Prob > F R-squared Adj R-squared Root MSE = .3591375 -.0797093 -15.32021 = 40 12.24 0.0001 0.3983 0.3657 2.9145 [95% Conf. Interval] .9567683 .4804261 34.92886 What is the predicted height for a child born to a mother…A well-balanced stock market portfolio will often experience an exponential growth. A particular investor with a well-balanced stock market portfolio records the portfolio balance every month, in thousands of dollars, from the date of investment. The roughly exponential growth can be transformed to a linear model by plotting the natural log of the balances versus time, in months, where t = 0 represents the date the money was invested. The linear regression equation for the transformed data is Using this equation, what is the predicted balance of the portfolio after 2 years (24 months)? (A) $5,654 (B) $6,798 (C) $285,431 (D) $896,053 (E) $948,464
- Calculate two lines of regression and calculate a linear regression equation to model the data given below: years (1980,1985,1990,1995,2000) Enrolment ( 21,25,29,39,47)Use the Financial database from “Excel Databases.xls” on Blackboard. Use Total Revenues, Total Assets, Return on Equity, Earnings Per Share, Average Yield, and Dividends Per Share to predict the average P/E ratio for a company. Use Excel to develop the multiple linear regression model. Assume a 5% level of significance. Which independent variable is the strongest predictor of the average P/E ratio of a company? A. Total Revenues B. Average Yield C. Earnings Per Share D.Return on Equity E. Total Assets F.Dividends Per Share Company Type Total Revenues Total Assets Return on Equity Earnings per Share Average Yield Dividends per Share Average P/E Ratio AFLAC 6 7251 29454 17.1 2.08 0.9 0.22 11.5 Albertson's 4 14690 5219 21.4 2.08 1.6 0.63 19 Allstate 6 20106 80918 20.1 3.56 1 0.36 10.6 Amerada Hess 7 8340 7935 0.2 0.08 1.1 0.6 698.3 American General 6 3362 80620 7.1 2.19 3 1.4 21.2 American Stores 4 19139 8536 12.2 1.01 1.4 0.34 23.5 Amoco 7 36287…3. Describe the problem that outliers present for a regression analysis and outline what you could do to resolve this problem.
- Explain what a residual is and how this relates to the best-fit regression model.Develop a scatterplot and explore the correlation between customer age and net sales by each type of customer (regular/promotion). Use the horizontal axis for the customer age to graph. Find the linear regression line that models the data by each type of customer. Round the rate of changes (slopes) to two decimal places and interpret them in terms of the relation between the change in age and the change in net sales. What can you conclude? Hint: Rate of Change = Vertical Change / Horizontal Change = Change in y / Change in xA box office analyst seeks to predict opening weekend box office gross for movies. Toward this goal, the analyst plans to use online trailer views as a predictor. For each of the 66 movies, the number of online trailer views from the release of the trailer through the Saturday before a movie opens and the opening weekend box office gross (in millions of dollars) are collected and stored in the accompanying table. A linear regression was performed on these data, and the result is the linear regression equation Yi=−0.840+1.4108Xi. Determine the coefficient of determination,r2,and interpret its meaning. Determine the standard error of the estimate. How useful do you think this regression model is for predicting opening weekend box office gross? Can you think of other variables that might explain the variation in opening weekend box office gross?
- A box office analyst seeks to predict opening weekend box office gross for movies. Toward this goal, the analyst plans to use online trailer views as a predictor. For each of the 66 movies, the number of online trailer views from the release of the trailer through the Saturday before a movie opens and the opening weekend box office gross (in millions of dollars) are collected and stored in the accompanying table. A linear regression was performed on these data, and the result is the linear regression equation Yi=−1.254+1.3968Xi. Complete parts (a) through (d). a. Determine the coefficient of determination,r2,and interpret its meaning. b. Determine the standard error of the estimate. c. How useful do you think this regression model is for predicting opening weekend box office gross? d. Can you think of other variables that might explain the variation in opening weekend box office gross?When you are deciding which variables to include as predictors in a multiple regression equation, what are some conditions that you must consider first?Describe regression variation in terms of variation in Y.