The manager of the purchasing department of a large saving and loan organization would like to develop a model to predict the amount of time (measured in hours) it takes to record a loan application. Data are collected from a sample of 30 days, and the number of applications recorded and completion time in hours is recorded. Below is the regression output: Regression Statistics Multiple R R Square Adjusted R Square Standard 0.9447 0.8924 0.8886 0.3342 Error Observations 30 ANOVA MS Significance df SS 4.3946E-15 Regression 25.9438 25.9438 232.2200 Residual 28 3.1282 0.1117 Total 29 29.072 Coefficients Standard P-value Lower 95% t Stat Upper 95% Error 0.1492 Intercept Applications Recorded 0.4024 0.1236 3.2559 0.0030 0.6555 0.0143 0.0126 0.0008 15.2388 0.0000 0.0109

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Types of Bias

Statistics is the study of data collection, organization, analysis, interpretation, and presentation. Statistical bias is a characteristic of a statistical technique or its findings in which the expected value deviates from the actual root quantitative parameter being estimated. According to the actual definition of bias, it refers to the tendency of a statistic to overestimate or underestimate a parameter.