Nothing Special   »   [go: up one dir, main page]
Scribd Logo
Upload

Welcome to Scribd!

  • 15 views

    Book 1

    Uploaded by

    Ionut Alexandru
    The regression model has a very strong linear relationship (R Square of 0.994688385) between the predictor variable x and the outcome variable y. The regression equation is y = 41.96428571 + 1.892857143*x. Both the intercept and slope are statistically significant based on the t-stats and p-values. Overall, the regression model is statistically significant in predicting y from x based on the F-statistic and p-value reported. Residuals are small, showing a close fit between predicted and observed y values.

    Copyright:

    © All Rights Reserved
    Download as XLSX, PDF, TXT or read online from Scribd

    Book 1

    Uploaded by

    Ionut Alexandru
    15 views5 pages
    The regression model has a very strong linear relationship (R Square of 0.994688385) between the predictor variable x and the outcome variable y. The regression equation is y = 41.96428571 + 1.892857143*x. Both the intercept and slope are statistically significant based on the t-stats and p-values. Overall, the regression model is statistically significant in predicting y from x based on the F-statistic and p-value reported. Residuals are small, showing a close fit between predicted and observed y values.

    Original Description:

    formule

    Original Title

    Book1

    Copyright

    © © All Rights Reserved

    Available Formats

    XLSX, PDF, TXT or read online from Scribd

    Did you find this document useful?

    Is this content inappropriate?

    The regression model has a very strong linear relationship (R Square of 0.994688385) between the predictor variable x and the outcome variable y. The regression equation is y = 41.96428571 + 1.892857143*x. Both the intercept and slope are statistically significant based on the t-stats and p-values. Overall, the regression model is statistically significant in predicting y from x based on the F-statistic and p-value reported. Residuals are small, showing a close fit between predicted and observed y values.

    Copyright:

    © All Rights Reserved
    Download as XLSX, PDF, TXT or read online from Scribd
    Download as xlsx, pdf, or txt
    15 views5 pages

    Book 1

    Uploaded by

    Ionut Alexandru
    The regression model has a very strong linear relationship (R Square of 0.994688385) between the predictor variable x and the outcome variable y. The regression equation is y = 41.96428571 + 1.892857143*x. Both the intercept and slope are statistically significant based on the t-stats and p-values. Overall, the regression model is statistically significant in predicting y from x based on the F-statistic and p-value reported. Residuals are small, showing a close fit between predicted and observed y values.

    Copyright:

    © All Rights Reserved
    Download as XLSX, PDF, TXT or read online from Scribd
    Download as xlsx, pdf, or txt
    of 5

    SUMMARY OUTPUT

    Regression Statistics
    Multiple R 0.997340657
    R Square 0.994688385 legatura puternica
    Adjusted R Square 0.993626062
    Standard Error 1.636634177
    Observations 7

    ANOVA
    df SS MS F Significance F
    Regression 1 2508.0357142857 2508.035714 936.3333333 0.00
    Residual 5 13.3928571429 2.678571429
    Total 6 2521.4285714286

    Coefficients Standard Error t Stat P-value Lower 95%


    Intercept 41.96428571 1.6656034023 25.19464457 0.00 37.6827158629
    x 1.892857143 0.0618589574 30.59956427 0.00 1.7338436306

    y = a+b*x
    a
    RESIDUAL OUTPUT b

    Observation Predicted y Residuals


    1 60.89285714 -0.8928571429
    2 70.35714286 -0.3571428571
    3 79.82142857 0.1785714286
    4 89.28571429 0.7142857143
    5 98.75 1.25
    6 108.2142857 1.7857142857
    7 117.6785714 -2.6785714286

    y = 41,96 +1,89*x

    consum venit
    modelul in amsamblu este semnificativ

    Upper 95% Lower 95,0% Upper 95,0%


    46.2458555657 37.682715863 46.245855566
    2.0518706552 1.7338436306 2.0518706552

    41.96 difera semnificativ de 0


    1.89 difera semnificativ de 0
    SUMMARY OUTPUT

    Regression Statistics
    Multiple R 0.993532
    R Square 0.987106
    Adjusted R 0.984957
    Standard E 2.681876
    Observatio 8

    ANOVA
    df SS MS F Significance F
    Regression 1 3303.72 3303.72 459.3310345 0.00 modelul in amsamblu este semnificativ
    Residual 6 43.15476 7.19246
    Total 7 3346.875

    Coefficients
    Standard Error t Stat P-value Lower 95% Upper 95%Lower 95,0%
    Upper 95,0%
    Intercept 44.34524 2.465632 17.98534 1.900353E-06 38.3120537853 50.37842 38.31205 50.37842
    x 1.77381 0.082764 21.43201 6.731764E-07 1.5712920916 1.976327 1.571292 1.976327

    RESIDUAL OUTPUT

    ObservationPredicted y Residuals
    1 62.08333 -2.08333
    2 70.95238 -0.95238
    3 79.82143 0.178571
    4 88.69048 1.309524
    5 97.55952 2.440476
    6 106.4286 3.571429
    7 115.2976 -0.29762
    8 124.1667 -4.16667
    lu este semnificativ

    Upper 95,0%
    x y
    10 60
    15 70
    20 80
    25 90
    30 100
    35 110
    40 115
    45 120

    You might also like