Stats
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Business Intelligence AnalysisStats/analysisAnalytics 2020. 1. 15. 11:19
1. Overview Business intelligence analysis requires data skills, business knowledge, and intuition. It explains past performance like below: What happened? What did it happen? How many units did we sell? In which region did we sell the most goods? 2. Description 2.1 Observation Observe sales volume, new customers, and etc. 2.2 Quantification The process of representing observations as numbers 2...
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Population, Sample, and SamplingStats 2020. 1. 11. 14:59
1. Overview How to select good random samples from the population How to calculate the estimate of a population mean using samples How can I assure that the sample will provide adequate information about the population parameters 2. Description 2.1 Simple Random Sampling Method 2.1.1 finite population A simple random sample of size n from a finite population of size N is a sample selected such t..
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Analysis of variance(ANOVA)Stats/analysisAnalytics 2019. 10. 5. 22:43
1. Overview Analysis of variance (ANOVA) is a collection of statistical models and their associated estimation procedures (such as the "variation" among and between groups) used to analyze the differences among group means in a sample. The ANOVA is based on the law of total variance, where the observed variance in a particular variable is partitioned into components attributable to different sou..
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Relationship between MLE and MAPStats 2019. 10. 4. 20:33
1. Overview Both Maximum Likelihood Estimation (MLE) and Maximum A Posterior (MAP) are used to estimate parameters for a distribution. MLE is also widely used to estimate the parameters for a Machine Learning model, including Naïve Bayes and Logistic regression. It is so common and popular that sometimes people use MLE even without knowing much of it. For example, when fitting a Normal distribut..
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p-valueStats 2019. 9. 27. 15:58
1. Overview Probability of obtaining a sample "more extreme" than the ones observed in your data, assuming $H_{0}$ is true The p-value is one of the key outputs of analyzing data. The p-value is the probability that, if the null hypothesis were true, sampling variation would produce an estimate that is further away from the hypothesized value than our data estimate. Shortly, The p-value tells us..