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Chapter 6: Confidence Intervals
Chapter 6: Confidence Intervals

Statistics for one and two samples
Statistics for one and two samples

... in our setting we have only a rather small sample, so in fact we may have to speculate, that is, assume some specific X-distribution f . The most common type of assumption, or one could say model, for the population distribution is to assume it to be the normal distribution. This assumption makes th ...
Key_unit3
Key_unit3

S1P1 - Lyle School of Engineering
S1P1 - Lyle School of Engineering

Chapter 14-15 - University of Utah Math Department
Chapter 14-15 - University of Utah Math Department

Descriptive Statistics
Descriptive Statistics

NOTE: There is a lot of reading, but I have broken them into chunks
NOTE: There is a lot of reading, but I have broken them into chunks

... participants with similar characteristics into two or more groups, then apply different treatments to each of the groups; the results allow the researchers to draw conclusions about cause and effect. An experimental study might be to determine the effect of practicing multiplication facts using the ...
Confidence Intervals
Confidence Intervals

... second row of boldfaced column headings. Say we want 95% confidence; we choose the 5th column from the right, with the column headings 0.0250 and 0.9500. (The first column heading is the area in the right tail; the second column heading is the confidence level. The reason these don’t add up to 1 is ...
8.1 Confidence Intervals: The Basics
8.1 Confidence Intervals: The Basics

Lab 5 - Risk Analysis, Robustness, and Power
Lab 5 - Risk Analysis, Robustness, and Power

Review of Statistics 1
Review of Statistics 1

Unit 24 Hypothesis Tests about Means
Unit 24 Hypothesis Tests about Means

Document
Document

... judgement on the part of the chemist. Nevertheless, it is a task that cannot be neglected because an analysis of totally unknown reliability is worthless. Thus the task of the analytical chemist goes beyond that of correctly performing the manipulations and taking the readings required in a procedur ...
This work is licensed under a Crative Commons Attibution
This work is licensed under a Crative Commons Attibution

10 1.96 10 x ±
10 1.96 10 x ±

791-04-Collocations
791-04-Collocations

... (or Student's t-test) H0 states that: P(w1 , w2 )  P(w1 )P(w2 ) We calculate the probability p-value that a collocation would occur if H0 was true If p-value is too low, we reject H0 ...
Testing Statistical Assumptions
Testing Statistical Assumptions

... Parametric tests are significance tests which assume a certain distribution of the data (usually the normal distribution), assume an interval level of measurement, and assume homogeneity of variances when two or more samples are being compared. Most common significance tests (z tests, t-tests, and F ...
Lecture16
Lecture16

Unit 1: Univariate Data
Unit 1: Univariate Data

... Determine whether the following variables are categorical (C) or quantitative (Q) 1. Brand of vehicle purchased by a customer 2. Price of a CD 3. Type of M&Ms preferred by students (peanut, plain) 4. Phone number of each student 5. Height of a 1-year old child 6. Term paper status (turned in on time ...
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UNIT IV

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Final Examination Math 201-813-AB WINTER 2016 [Marks] Total
Final Examination Math 201-813-AB WINTER 2016 [Marks] Total

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FUNDAMENTALS OF BUSINESS STATISTICS

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Chapter 4 Exploratory Data Analysis

Short Answer Questions - Colorado Mesa University
Short Answer Questions - Colorado Mesa University

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Misuse of statistics

Statistics are supposed to make something easier to understand but when used in a misleading fashion can trick the casual observer into believing something other than what the data shows. That is, a misuse of statistics occurs when a statistical argument asserts a falsehood. In some cases, the misuse may be accidental. In others, it is purposeful and for the gain of the perpetrator. When the statistical reason involved is false or misapplied, this constitutes a statistical fallacy.The false statistics trap can be quite damaging to the quest for knowledge. For example, in medical science, correcting a falsehood may take decades and cost lives.Misuses can be easy to fall into. Professional scientists, even mathematicians and professional statisticians, can be fooled by even some simple methods, even if they are careful to check everything. Scientists have been known to fool themselves with statistics due to lack of knowledge of probability theory and lack of standardization of their tests.
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