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IBM stock prices. Refer to Example 14.1 (p. 14-5) and the 2015 monthly IBM stock prices.
a. Use the exponentially smoothed series (with w = .5 from January to September 2015 to forecast the monthly values of the IBM stock price from October to December 2015. Calculate the forecast errors.
b. Use a simple linear regression model fit to the IBM stock prices from January to September 2015. Let time t
c. With what approximate precision do you expect to be able to predict the IBM stock price using the regression model?
d. Give the simple linear regression forecasts and the 95% forecast intervals for the October-December 2015 prices. How does the precision of these forecasts agree with the approximation obtained in part c?
e. Compare the exponential smoothing forecasts, part a, to the regression forecasts, part d, using MAD, MAPE, and RMSE.
f. What assumptions does the random error component of the regression model have to satisfy in order to make the model inferences (such as the forecast intervals in part c) valid?
g. Test to determine whether there is evidence of first-order positive autocorrelation in the random error component of the regression model. Use α = .05. What can you infer about the validity of the model inferences?
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Chapter 14 Solutions
Statistics for Business and Economics (13th Edition)
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- 20 km, because GISS Worksheet 10 Jesse runs a small business selling and delivering mealie meal to the spaza shops. He charges a fixed rate of R80, 00 for delivery and then R15, 50 for each packet of mealle meal he delivers. The table below helps him to calculate what to charge his customers. 10 20 30 40 50 Packets of mealie meal (m) Total costs in Rands 80 235 390 545 700 855 (c) 10.1. Define the following terms: 10.1.1. Independent Variables 10.1.2. Dependent Variables 10.2. 10.3. 10.4. 10.5. Determine the independent and dependent variables. Are the variables in this scenario discrete or continuous values? Explain What shape do you expect the graph to be? Why? Draw a graph on the graph provided to represent the information in the table above. TOTAL COST OF PACKETS OF MEALIE MEAL 900 800 700 600 COST (R) 500 400 300 200 100 0 10 20 30 40 60 NUMBER OF PACKETS OF MEALIE MEALarrow_forwardLet X be a random variable with support SX = {−3, 0.5, 3, −2.5, 3.5}. Part ofits probability mass function (PMF) is given bypX(−3) = 0.15, pX(−2.5) = 0.3, pX(3) = 0.2, pX(3.5) = 0.15.(a) Find pX(0.5).(b) Find the cumulative distribution function (CDF), FX(x), of X.1(c) Sketch the graph of FX(x).arrow_forwardA well-known company predominantly makes flat pack furniture for students. Variability with the automated machinery means the wood components are cut with a standard deviation in length of 0.45 mm. After they are cut the components are measured. If their length is more than 1.2 mm from the required length, the components are rejected. a) Calculate the percentage of components that get rejected. b) In a manufacturing run of 1000 units, how many are expected to be rejected? c) The company wishes to install more accurate equipment in order to reduce the rejection rate by one-half, using the same ±1.2mm rejection criterion. Calculate the maximum acceptable standard deviation of the new process.arrow_forward
- 5. Let X and Y be independent random variables and let the superscripts denote symmetrization (recall Sect. 3.6). Show that (X + Y) X+ys.arrow_forward8. Suppose that the moments of the random variable X are constant, that is, suppose that EX" =c for all n ≥ 1, for some constant c. Find the distribution of X.arrow_forward9. The concentration function of a random variable X is defined as Qx(h) = sup P(x ≤ X ≤x+h), h>0. Show that, if X and Y are independent random variables, then Qx+y (h) min{Qx(h). Qr (h)).arrow_forward
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