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Observations 62<br />

ANOVA<br />

df SS MS F Significance F<br />

Regression 1 38228184,72 38228184,72 27,42079 0,00000221166<br />

Residual 60 83647889,32 1394131,489<br />

Total 61 121876074<br />

Coefficients<br />

Standard<br />

Error t Stat P-value<br />

~ 978 ~<br />

Lower<br />

95%<br />

Upper<br />

95%<br />

Lower<br />

95,0%<br />

Upper<br />

95,0%<br />

Intercept 512,2206 190,3024 2,6916 0,0092 131,5590 892,8821 131,5590 892,8821<br />

X Variable 1 8,9158 1,7026 5,2365 0,0000 5,5100 12,3216 5,5100 12,3216<br />

Since Multiple R has a positive value which is greater than 0.5, shows that between<br />

basic earning per share and price per share there is a direct correlation of medium<br />

intensity. R Square shows that 31.36% of the variation of the price per share is<br />

explained by the basic earning per share. Since Significance F has a very small, far<br />

below the threshold limit of 0.05, and F has a high value (27.42), we can accept the<br />

simple regression model presented in Formula 1, and validated by Figure 2.<br />

Figure 2. X Variable 1 Line Fit Plot<br />

4.3 The simple regression model to calculate the price per share in relation<br />

to comprehensive income per share<br />

Based on data from Annex 3 and on the results obtained by applying th Regression<br />

function in Table 2, we defined the following simple regression function to express<br />

the price per share in relation to basic earning per share:<br />

YCI = 729,47 + 5,37 XCI (Formula 2)

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