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    Activity 10 Example 05.03 JayDomingoFinal

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    Jay Domingo
    The document presents quarterly smartphone sales data from 4 years in a table. It uses regression analysis with dummy variables and a time period variable to model seasonality and trend in the data. The regression results in a high R-squared value of 0.976, indicating the model fits the data well. The regression coefficients are then used to forecast future smartphone sales values.

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    © All Rights Reserved
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    Activity 10 Example 05.03 JayDomingoFinal

    Uploaded by

    Jay Domingo
    33 views10 pages
    The document presents quarterly smartphone sales data from 4 years in a table. It uses regression analysis with dummy variables and a time period variable to model seasonality and trend in the data. The regression results in a high R-squared value of 0.976, indicating the model fits the data well. The regression coefficients are then used to forecast future smartphone sales values.

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    Activity-10-Example-05.03-JayDomingoFinal

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    The document presents quarterly smartphone sales data from 4 years in a table. It uses regression analysis with dummy variables and a time period variable to model seasonality and trend in the data. The regression results in a high R-squared value of 0.976, indicating the model fits the data well. The regression coefficients are then used to forecast future smartphone sales values.

    Copyright:

    © All Rights Reserved
    Download as XLSX, PDF, TXT or read online from Scribd
    Download as xlsx, pdf, or txt
    33 views10 pages

    Activity 10 Example 05.03 JayDomingoFinal

    Uploaded by

    Jay Domingo
    The document presents quarterly smartphone sales data from 4 years in a table. It uses regression analysis with dummy variables and a time period variable to model seasonality and trend in the data. The regression results in a high R-squared value of 0.976, indicating the model fits the data well. The regression coefficients are then used to forecast future smartphone sales values.

    Copyright:

    © All Rights Reserved
    Download as XLSX, PDF, TXT or read online from Scribd
    Download as xlsx, pdf, or txt
    of 10

    Sales

    Year Quarter (1000s) Quarterly Smartphone Sales Time Series Plot (with Forec
    1 1 4.8
    2 4.1
    3 6.0
    4 6.5
    2 1 5.8
    2 5.2
    3 6.8
    4 7.4
    3 1 6.0
    2 5.6
    3 7.5
    4 7.8
    4 1 6.3
    2 5.9
    3 8.0
    4 8.4

    Method: Regression with Dummy Variables (and Period)

    Sales
    Year Quarter Period Qtr1 Qtr2 Qtr3 (1000s)
    1 1 1
    2 2
    3 3
    4 4
    2 1 5
    2 6
    3 7
    4 8
    3 1 9
    2 10
    3 11
    4 12
    4 1 13
    2 14
    3 15
    4 16
    5 1
    2
    3
    4
    s Time Series Plot (with Forecast)

    Chapter 5 Time Series Analysis and Forecasting


    Forecast 5.1 Time Series Patterns
    -Trend and Seasonal Pattern
    Table 5.6 Quarterly Smartphone Sales Time Series
    Figure 5.6 Quarterly Smartphone Sales Time Series Plot
    5.4 Using Regression Analysis for Forecasting
    -Seasonality With Trend
    Table 5.12 Smartphone Sales Time Series with Dummy Variables and Time Period
    me Period
    Sales
    Year Quarter (1000s) Quarterly Smartphone Sales Time Series Plot (with Forec
    1 1 4.8
    2 4.1 Sales with forecast
    3 6.0 10.0
    4 6.5
    9.0
    2 1 5.8
    8.0
    2 5.2
    3 6.8 7.0

    4 7.4 6.0
    3 1 6.0 5.0
    2 5.6 4.0
    3 7.5
    3.0
    4 7.8
    2.0
    4 1 6.3
    2 5.9 1.0

    3 8.0 0.0
    1 2 3 4 5 6 7 8 9 10 11 12 13 14
    4 8.4

    Method: Regression with Dummy Variables (and Period)

    Sales
    Year Quarter Period Qtr1 Qtr2 Qtr3 (1000s)
    1 1 1 1 0 0 4.8
    2 2 0 1 0 4.1
    3 3 0 0 1 6.0
    4 4 0 0 0 6.5
    2 1 5 1 0 0 5.8
    2 6 0 1 0 5.2
    3 7 0 0 1 6.8
    4 8 0 0 0 7.4
    3 1 9 1 0 0 6.0
    2 10 0 1 0 5.6
    3 11 0 0 1 7.5
    4 12 0 0 0 7.8
    4 1 13 1 0 0 6.3
    2 14 0 1 0 5.9
    3 15 0 0 1 8.0
    4 16 0 0 0 8.4
    5 1 17 1 0 0
    2 18 0 1 0
    3 19 0 0 1
    4 20 0 0 0

    SUMMARY OUTPUT

    Regression Statistics
    Multiple R 0.98806594
    R Square 0.976274301 98%
    Adjusted R Sq0.967646775
    Standard Erro 0.216663753
    Observations 16

    ANOVA
    df SS MS F Significance F
    Regression 4 21.248 5.312 113.1580731 7.37582E-09
    Residual 11 0.516375 0.046943182
    Total 15 21.764375

    CoefficientsStandard Error t Stat P-value Lower 95% Upper 95%


    Intercept 6.06875 0.162497815 37.34665609 6.12289E-13 5.711094721 6.426405279
    Period 0.145625 0.012111872 12.0233272 1.14029E-07 0.118966949 0.172283051
    Qtr1 -1.363125 0.157454336 -8.65727191 3.05975E-06 -1.70967966 -1.01657034
    Qtr2 -2.03375 0.155107642 -13.111862 4.65532E-08 -2.37513962 -1.69236038
    Qtr3 -0.304375 0.153682427 -1.98054525 0.073201043 -0.64262774 0.033877741
    s Time Series Plot (with Forecast)
    Sales w/o forecast
    Sales with forecast 9.0
    8.0
    7.0 f(x) = 0.179852941176471 x + 4.8525
    R² = 0.505321533604396
    6.0
    5.0
    4.0
    3.0
    2.0
    1.0
    0.0
    1 2 3 4 5 6 7 8 9 10 11 12 13 14

    Sales (1000s) Linear (Sales (1000s))

    7 8 9 10 11 12 13 14 15 16 17 18 19 20

    Forecast Chapter 5 Time Series Analysis and Forecasting


    5.1 Time Series Patterns
    (Y^) -Trend and Seasonal Pattern
    4.851 Table 5.6 Quarterly Smartphone Sales Time Series
    4.326 Figure 5.6 Quarterly Smartphone Sales Time Series Plot
    5.4 Using Regression Analysis for Forecasting
    6.201 -Seasonality With Trend
    6.651 Table 5.12 Smartphone Sales Time Series with Dummy Variables and
    5.434 0.583 0.146
    4.909 0.583
    6.784 0.582
    7.234 0.583 Forecast Y^=b0+(b1*Qtr1)+(b2*Qtr2)+(b3*Qtr3)+(b4*period)
    6.016 Coefficients
    5.491 Intercept 6.069 b0
    7.366 Period 0.146
    7.816 Qtr1 -1.363 b1
    6.599 Qtr2 -2.034 b2
    6.074 Qtr3 -0.304 b3
    7.949
    8.399
    7.181
    6.656
    8.531
    8.981

    Lower 99.0% Upper 99.0%


    5.5640632284 6.573436772
    0.108007869 0.183242131
    -1.8521477024 -0.8741023
    -2.5154843259 -1.55201567
    -0.7816828829 0.172932883
    /o forecast

    8525

    8 9 10 11 12 13 14 15 16

    Linear (Sales (1000s))

    orecasting

    ales Time Series


    ales Time Series Plot
    casting

    ries with Dummy Variables and Time Period

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