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    Book 1

    Uploaded by

    Komang Gayatri
    This document appears to be the output of a linear regression analysis with one independent variable. It performed a linear regression on a dataset with 30 observations and variables x, y, and xy. It found a multiple R-squared of 0.7673, indicating the model explains about 76% of the variability in the dependent variable. It shows the intercept and coefficient for the independent variable x are statistically significant predictors of xy.

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    Attribution Non-Commercial (BY-NC)
    Download as XLSX, PDF, TXT or read online from Scribd

    Book 1

    Uploaded by

    Komang Gayatri
    5 views2 pages
    This document appears to be the output of a linear regression analysis with one independent variable. It performed a linear regression on a dataset with 30 observations and variables x, y, and xy. It found a multiple R-squared of 0.7673, indicating the model explains about 76% of the variability in the dependent variable. It shows the intercept and coefficient for the independent variable x are statistically significant predictors of xy.

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    Book1

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    This document appears to be the output of a linear regression analysis with one independent variable. It performed a linear regression on a dataset with 30 observations and variables x, y, and xy. It found a multiple R-squared of 0.7673, indicating the model explains about 76% of the variability in the dependent variable. It shows the intercept and coefficient for the independent variable x are statistically significant predictors of xy.

    Copyright:

    Attribution Non-Commercial (BY-NC)
    Download as XLSX, PDF, TXT or read online from Scribd
    Download as xlsx, pdf, or txt
    5 views2 pages

    Book 1

    Uploaded by

    Komang Gayatri
    This document appears to be the output of a linear regression analysis with one independent variable. It performed a linear regression on a dataset with 30 observations and variables x, y, and xy. It found a multiple R-squared of 0.7673, indicating the model explains about 76% of the variability in the dependent variable. It shows the intercept and coefficient for the independent variable x are statistically significant predictors of xy.

    Copyright:

    Attribution Non-Commercial (BY-NC)
    Download as XLSX, PDF, TXT or read online from Scribd
    Download as xlsx, pdf, or txt
    of 2

    No

    y
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    SUMMARY OUTPUT
    Regression Statistics
    Multiple R
    0.875966237
    R Square
    0.767316849
    Adjusted R Square
    0.759006736
    Standard Error
    1.651393936
    Observations
    30
    ANOVA
    df
    Regression
    Residual
    Total

    Intercept
    X Variable 1

    1
    28
    29
    Coefficients
    -0.703467918
    1.124979058

    SS
    251.8078126
    76.35885408
    328.1666667

    MS
    F
    251.80781 92.335314
    2.7271019

    Standard Error
    t Stat
    P-value
    3.917987733 -0.1795483 0.8588003
    0.117074018 9.6091266 2.304E-10

    Significance F
    2.30364E-10

    Lower 95%
    Upper 95% Lower 95,0% Upper 95,0%
    -8.729101865 7.32216603 -8.729101865
    7.32216603
    0.885163807 1.36479431 0.885163807
    1.36479431

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