Inference Control in Statistical databases

Jan 18, 2013

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Statistical database:. Goal[DEN83]:Provide frequency, average, other statistics of personsChallenge:Preserving privacy[DEN83]Interaction between client and database server[JAG07]Client may not want server to know what information querying.Server would like to ensure that client does not learn

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Inference Control in Statistical databases

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Statistical Inference

Statistical Inference. L3, Bootstrap, L2, L1 progression Formal and informal Continuing implementation of new NCEA standards Team Solutions Steel • Hogan • Cathcart • McNaughton • Barks • Johnson. What may happen in this session. Data Questions and PPDAC Bootstrap idea

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Statistical Inference

Statistical Inference. Using our sample to infer about our population. What is going on in the sample is of little actual interest to us – we are only interested in what it can tell us about the population. That is, we want to generalize about the population using the data from our sample.

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Statistical Inference

Statistical Inference. Dr. Mona Hassan Ahmed Prof. of Biostatistics HIPH, Alexandria University. Lesson Objectives. Know what is Inference Know what is parameter estimation Understand hypothesis testing & the “types of errors” in decision making. Know what the a -level means.

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Inference Protection on Statistical Databases

CryptULLDecember 2005. Statistical Database. A statistical database is a database which provides statistics on subsets of records Statistics may be performed to compute SUM, MEAN, MEDIAN, COUNT, MAX AND MIN of records Two Types of Statistical Databases:Special Purpose (like Census)General Purp

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Statistical Inference

Statistical Inference. Mitchell Hoffman UC Berkeley. Introduction. Statistics: Making inferences about populations (infinitely large) using finitely large data. Crucial for Addressing Causal Questions, e.g. : - Does smoking cause cancer?

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Statistical Inference

Statistical Inference. Hypothesis Testing. What is a statistical hypothesis? What is so important about it? What is a rejection region? What does it mean to say that a finding is statistically significant ? Describe Type I and Type II errors. Hypothesis Testing.

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Statistical Inference

Statistical Inference. Guillaume Flandin Wellcome Trust Centre for Neuroimaging University College London. SPM Course Zurich, February 2012. Image time-series. Statistical Parametric Map. Design matrix. Spatial filter. Realignment. Smoothing. General Linear Model.

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Statistical Inference

Statistical Inference. Statistical Inference is the process of making judgments about a population based on properties of the sample intuition only goes so far towards making decisions of this nature experts can offer conflicting opinions using the same data . Methods of Statistical Inference.

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Statistical Inference

Statistical Inference. Most data comes in the form of numbers We have seen methods to describe and summarise patterns in data . Most data are samples (subsets) of the population of interest. Random variables and their probability distributions describe patterns in populations.

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Statistical Inference

Statistical Inference. Module 7. Inferential Statistics. We start with a question about a group or groups. The group(s) we are interested in is(are) called the population (s). Examples. What is the average number of car accidents for a person over 65 in the United States?.

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Statistical inference

Statistical inference. Take a random sample of n independent observations from a population. Calculate the mean of these n sample values. This is know as the sample mean . Repeat the procedure until you have taken all possible samples of size n , calculating the sample mean of each.

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Statistical Inference

Statistical Inference. What is Statistics?. It is a science that involves data summarization, experimental design, data collection, etc.

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Statistical Inference

Statistical Inference. Estimation Confidence Intervals Estimate the proportion of the electorate who support Candidate X Hypothesis Tests Make a decision Will Candidate X win the election?. Hypothesis Test: An example. Castanada v. Partida 430 U.S. 482 Background

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Statistical Inference

Statistical Inference. Chapter 12/13. Statistical Inference. Given a sample of observations from a population, the statistical inference consists in estimating characteristics of the population. A characteristic may be guessed to: Be a number (point estimation) Lay within an interval.

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Statistical Inference

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Statistical Inference

Statistical Inference. Estimation and Hypothesis Testing. Statistical Inference consists of two important components: Estimation Hypothesis testing. Estimation. Everyday Examples: There is a 60% chance that tomorrow’s rainfall will be 1.0 – 1.5 inches.

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Statistical inference

Statistical inference. Population - collection of all subjects or objects of interest (not necessarily people) Sample - subset of the population used to make inferences about the characteristics of the population

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Statistical Inference

Statistical Inference. Plan: Discuss statistical methods in simulations Define concepts and terminology Traditional approaches: Hypothesis testing Confidence intervals Batch means Analysis of Variance (ANOVA). Motivation.

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Statistical Inference

Statistical Inference. By Using Confidence Intervals. HYPOTHESIS TESTING( p value) & ESTIMATION (Confidence Interval). Sample is assumed to be representative to the population. In research: measurement are always done in the sample, the results will be applied to population. P. P. S.

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Statistical Inference

Statistical Inference. “Statistical thinking will one day be as necessary for efficient citizenship as the ability to read and write.” (H.G. Wells, 1946)

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Statistical Inference

Statistical Inference Statistical inference is concerned with the use of sample data to make inferences about unknown population parameters. For example, suppose we have a random variable X which has a normal distribution with unknown mean and variance.

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Statistical Inference

Statistical Inference. Note: Only worry about pages 295 through 299 of Chapter 12. The larger the sample size (n) the more confident you can be that your sample mean is a good representation of the population mean. In other words, the "n" justifies the means. ~ Ancient Kung Foole Proverb.

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