The number of cases of merlot wine sold by a Barossa Valley Winery in a six-month period follows.a) Calculate the 3 period moving averages to predict the number of cases for the time periods (4-7) and fill in the table below.b) De-seasonalize the forecast for period 7 using a seasonal index 0.95b) Calculate the errors and fill in the table below Only write the number value (ignore + and -)c) Calculate the error² and fill in the table below.d) Calculate MADRound off your answers to one decimale)Calculate MSERound off your answers to one decimalf) The other purpose of the moving averages is tog) The prediction for which time period has the highest errorh) If the MAD for 4-moving averages is 35, which model is better 3 or 4? type only the number? 567Numbercases400460358508Month of1234.4213823 Moving averages ABS(Errors)N/AN/AN/AErrors2

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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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Google "covid 19 california" and choose statistics from menu on left. Select data for "all time" period. In the discussion editor, click on 3 dots, insert table using editor (3 columns and 10 rows) Label the table with headings Day (t), Cases (y), Rate of change () Fill column 1 with alternate Sunday dates starting from May 3 ending Aug 16 Fill in column 2 with number of daily cases from Google statistics page. For each row (except first), find rate of change in cases per fortnight (2wk). At = 14 For example day rate of change cases May 3 5707 (8658-5707)/14= ? cases per fortnight May 10 8658 May 17
4.- For the following data set 10, 7, 6, k, 4, 7, 1, 10. Find the value of k so that (a) X = 6.25 (b) X = 7.5 Villalobos 2020 22 (c) M = 7 (d) MR = 10 CHAPTER 3. DATA DESCRIPTION
Using a TI-84 Plus calculator, I need to know the step by step process to calculate these. I don’t need these just written out on paper, but I also need to be shown the every move made into the calculator. Like, ‘STAT’ then ‘TESTS’ then ‘2: T-Test’, and so on.
10) The top point in the scatter plot (6.5, 1459) is for Washington, DC. Find the predicted DC violent crime rate if x=6.5. Then find the residual for DC using the predicted violence crime rate and the actual violent crime rate (the actual crime rate is provided in the first sentence of this question). Prediction = Actual y-value = Residual =
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…
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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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