![]() ![]() ![]() By following the steps outlined in this guide and applying the concepts through practical examples, you can confidently navigate the world of Z scores and harness their power in various fields such as research, data analysis, and decision-making. Whether we are calculating probabilities, finding cut-off points, or determining proportions, the Z score table provides a valuable resource for statistical analysis. Understanding Z scores and utilizing the Z score table empowers us to analyze and interpret data within a standard normal distribution. You can also determine the percentage of the population that lies above or below any z-score using a z-score table. A negative z-score means its lower than average. A positive z-score means the data value is higher than average. By subtracting the two proportions, we can conclude that approximately 65.46% of the data falls between -0.75 and 1.25. Table 1: Table of the Standard Normal Cumulative Distribution Function (z) z. The z-score allows you to compare data from different samples because z-scores are in terms of standard deviations. Probability z TABLE A Standard normal probabilities (continued) z.00. Using the positive Z score table, we find that the proportion below 1.25 is approximately 89.43%. Tables T-3 Table entry for z is the area under the standard normal curve to the left of z. Using the negative Z score table, we find that the proportion below -0.75 is approximately 23.97%. Suppose we have Z scores of -0.75 and 1.25. Example 3 - Proportion Calculation: Let's consider a scenario where we want to find the proportion of data points falling between two Z scores. ![]()
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