Average Daily TV Viewing Time Per U.S. Household Total Min 4 35 275 51 291 1065

Question

Average Daily TV Viewing Time Per U.S. Household Total Min 4 35 275 51 291 1065 1970 1975 5 29329 356 367 10 53 17 35 1985 430 413 437 455 491 501 2010 Use Excel & MegaStat, to make a time-series line chart. Adjust the Y-axis scale if necessary to show more detail (because Excel usually starts the scale at zero). If a fitted trend is called for, use Excel's option to display the equation and R'statistic. (a) Plot the total minutes of TV viewing time per household (Excel & Megastat). (CTHREE POINTS) (b) Describe the trend (if any) and discuss possible causes. (ONE POINT) c) Fit a linear trend to the data. (TWO POINTS) (d) Would this model give reasonable forecasts? Would another trend model be better? Explain. (FIVE POINTS) (e) Make a forecast for 2015. Show the forecast calculations. (THREE POINTS) () Would these data ever approach an asymptote? (FOUR POINTS) Note Time is in 5-year increments, so use 1 - 14 for the 2015 forecast. Television a) Plot the total minutes of TV viewing time per household. Using Excel, write down steps: Sketch Excel chart and write steps to generate it: (ONE POINT) Plot the Data using Megastat. Write down the required Megastat commands: (TWO POINTS)

Explanation / Answer

a)Plotting viewing data in a scatter diagram against coded year 1950 as Base,

b)Indicates increasing trend year after year except 1985-1990 . and the trend looks

linear at 1st look as shown in following figure(fig-1).

Fig-1

c)Hence a linear trend is fit (as shown in figure below).

Equation is y = 18.973x + 255.42 i.e

Viewing Mins = 18.973*(coded year) + 255.42

Rsquare is also quite high 0.9841 indicating a very good correlation.

Fig-2

Though close look indicates a bit upward movement. Hence possible

Altenatives could be exponential or ploynomial.

Fig 3-exponential

Fig 4 ploynomial

Summary of fitted equations with Rsquare value is as follows

a> Linear : y = 18.973x + 255.42

R² = 0.9841

b> Exponential : y = 268.72e0.05x

R² = 0.9767

c> Polynomial(order 2): y = -0.1144x2 + 20.574x + 251.42

R² = 0.9845

Conclusion

Forecast linear exp polynomial

521(8 Hr 41 min)., 541(9 Hr 1 min)., 517(8 Hr 37 min)

Hence Polynomial model gives highest corelation,but its almost same as linear.

Forecast is highest in exponential fmodel

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