idginne ods R) ni gnisnidFit a straight line trend by the method of least squares to the following data and/predict the sales for 2001 :vilYear :199419951996199719981999Sales (in '000=)151720232124शाँvollol ari o basat onilaesi

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Answered: Fit a straight line trend by the method…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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The foreign automobile dealer sells English, French, German, Japanese and Korean automobiels. Th enumber of such automobiles sold in a month follow; Country English French German Japanese Korean Number 3. 12 20 10 In words describe what descriptive method would you use to display the data to an audience? Comment on your choice. For the toolbar, press ALT+F10 (PC) or ALT+FN+F10 (Mac).
58. Fit a straight line trend by least squares method and tabulate the trend Pvalues from the following figures of production of a cement factory : Year Production ('000 tonnes) : 2008 77 2009 88 2010 94 2011 2013 2012 91 2014 90 85 98
12. Fit a straight line trend by the method of least squares to the following data. Assuming that the same rate of change continues, what would be the predicted earming for the year 2015? Year 2001 2002 2003 2004 2005 2006 2007 2008 Eamings (Rs. lakh): Do not plot the trend values on the graph. 38 40 65 72 69 60 87 95
2. Table below records the quarterly refrigerator sales for the period 2012-2014. Number of 1 2 3 4 purchases 2012 46 41 60 63 2013 56 52 66 72 2014 58 56 75 76 a) Find the trend equation using the least squares method. b) Forecast the sales for the 1" quarter of 2015.
Describe in your own words what it means to perform a linear least squares analysis. Provide an example to substantiate your claims
Consider data on every game played by the Brooklyn Nets in 2014 (82 games) that includes the variables margin, - the Net's margin of victory (number of points the Nets scored minus the number of points their opponent scored) for game i, and • home; - a dummy variable equal to 1 when the Nets are the home team (game i was played in their home arena) and equal to 0 when they are the away team (game i was played in the opponent's arena). I use the least-squares method to estimate the following regression model margin = a + ßhome; + ei Below is the Stata output corresponding to the estimated regression line: regress margin home if team===== "Brooklyn Nets" . Source Model Residual Total margin home _cons SS 1459.95122 15252.0488 16712 df 1459.95122 1 80 190.65061 None of the above 81 206.320988 Coef. Std. Err. 8.439024 3.049595 -5.219512 2.156389 MS t Number of obs F(1, 80) Prob > F R-squared O The Nets lost more games than they won in 2014 P>|t| 2.77 0.007 -2.42 0.018 Adj R-squared = Root…
15. Fit a straight line trend by the method of least squares. Year: 1987 1988 1989 1990 1991 1992 1993 1994 1995 Death: 175 190 185 195 180 200 185 190 205 Estimate the number of death for year 2002. CS Scanned with CamScanner
7. Fit a straight line trend by the method of least squares to the following data. Year: 1989 1990 1991 1992 1993 1994 1995 1996 Earnings: 38 40 65 72 69 60 87 95 (Rs lakhs) Fore cast the earnings for the year 1997.
Consider data on every game played by the Brooklyn Nets in 2014 (82 games) that includes the variables margin; - the Net's margin of victory (number of points the Nets scored minus the number of points their opponent scored) for game i, and • home; - a dummy variable equal to 1 when the Nets are the home team (game i was played in their home arena) and equal to 0 when they are the away team (game i was played in the opponent's arena). I use the least-squares method to estimate the following regression model margin = a + ßhome; + ei Below is the Stata output corresponding to the estimated regression line: regress margin home if team==== "Brooklyn Nets" Source Model Residual Total margin home _cons SS 1459.95122 15252.0488 16712 df 1 80 Coef. Std. Err. MS 81 206.320988 8.439024 3.049595 -5.219512 2.156389 1459.95122 190.65061 t Number of obs F (1, 80) Prob > F R-squared. Adj R-squared = Root MSE P>|t| 2.77 0.007 -2.42 0.018 82 7.66 0.0070 0.0874 0.0760 13.808 [95% Conf. Interval]…

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Fit a straight line trend by the method of least squares to the following data and ´predict the sales for 2001 : Year: 1994 1995 1996 1998 1999 1997 Sales (in '000=) 15 17 20 23 21 24