Lost time hours can be predicted by the following linear formula Y a bX Lost

Lost time hours can be predicted by the following

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Lost-time-hours can be predicted by the following linear formula: Y = a + bX Lost-time-hours = 273.4 + (-0.143)
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21 Sun Coast Research Paper Evaluation Every new contract awarded to Sun Coast shall be trained in health and safety program. Data were collected from 223 contracts on training costs and lost-time hours. It would be useful to know how to predict lost hours of training, if training had been successful in reducing lost- time hours and, if so, how to predict lost-time hours from training expenditures Multiple Regressions The data were visually and statistically observed to determine if they fulfilled the necessary assumptions required for parameter testing. In addition, the graphic statistics below this confirm that skewness and kurtosis are within the acceptable ranges of -2 and +2. Mean, median, and mode as well as the normal distribution of data, closely aligned. These graphic statistics confirm that the information is suitable for data analysis using parameter statistical procedures. Regression Statistics Multiple R 0.601841822 R Square 0.362213579 Adjusted R Square 0.360083364 Standard Error 5.51856585 Observations 1503 ANOVA df SS MS F Significance F Regression 5 25891.88784 5178.377569 170.036146 7 2.1289E-143 Residual 1497 45590.48986 30.45456904 Total 1502 71482.3777 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 126.8224555 0.623820253 203.2996763 0 125.5988009 128.0461101 Frequency (Hz) -0.0011169 4.7551E-05 -23.48846042 4.0652E-104 - 0.001210174 -0.001023627 Angle in Degrees 0.047342353 0.037308069 1.268957462 0.20465350 1 - 0.025839288 0.120523993 Chord Length -5.495318335 2.927962181 -1.876840613 0.06073430 9 -11.23866234 0.248025671
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22 Sun Coast Research Paper Velocity (Meters per Second) 0.083239634 0.009300188 8.950317436 1.02398E-18 0.064996851 0.101482417 Displacement -240.5059086 16.51902666 -14.55932686 5.20583E-45 - 272.9088041 -208.103013 Hypothesis H0 3 There is no statistically significant relationship between frequency, angle in degrees, chord length, velocity, and displacement and decibel level. HA 3 There is a statistically significant relationship between frequency, angle in degrees, chord length, velocity, and displacement and decibel level. Frequency, velocity, and displacement coefficient p-values < .05 (alpha), therefore, the null hypothesis ( H0 3 ) is rejected and the alternative hypothesis ( HA 3 ) is accepted. Decibel level can be predicted by the following linear formula: Y = a + b1X1 + b2X2 +…+ bnXn dB = 126.8 + (-0.0011) (frequency) + (.083) (velocity) + (-240.5) (displacement) Independent Samples t Test: Hypothesis Testing H0 4 : (null hypothesis) There is no statistically significant difference in mean scores between previous training and revised training. HA 4 : (alternative hypothesis) There is a statistically significant difference in mean scores between previous training and revised training. t-Test: Two-Sample Assuming Unequal Variances Group A Group B Mean 69.79032258 84.77419355 Variance 122.004495 26.96456901 Observations 62 62 Hypothesized Mean Difference 0 df 87 t Stat -9.666557191 P(T<=t) one-tail 9.69914E-16 t Critical one-tail 1.662557349
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23 Sun Coast Research Paper P(T<=t) two-tail 1.93983E-15 t Critical two-tail 1.987608282 Using t-test for independent sample p-value < .05 or 5% (alpha), we can see on the table below it shows that p-value= 1.93983E-15 < .05 or 5%. Therefore, the null hypothesis (H04) is rejected and the alternative hypothesis (HA4) is accepted because the null hypothesis is failed it doesn’t mean that the alternative hypothesis is not true. It only means that there is not enough evidence to reject the null hypothesis. However, the mean score between prior and revised training differs statistically significant.
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