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252y0811 - On-line Web Courses
252y0811 - On-line Web Courses

... possibility.) For z make a diagram. Draw a Normal curve with a mean at 0. z .0005 is the value of z with 0.05% of the distribution above it. Since 100 – 0.05 = 99.95, it is also the .9995 fractile. Since 50% of the standardized Normal distribution is below zero, your diagram should show that the pro ...
Lectures 7-9: Measures of Central Tendency, Dispersion
Lectures 7-9: Measures of Central Tendency, Dispersion

Summarizing Data: Measures of Location
Summarizing Data: Measures of Location

http://circle.adventist.org/files/download/IntroStatistics.pdf
http://circle.adventist.org/files/download/IntroStatistics.pdf

... whose primary job was to write out a web-book on number theory. From there I ended up helping to write two statistics books which he has since made many editions. You might still find those volumes at www.andrews.edu/~calkins. He also provided valuable input on my own book, pointing out some trouble ...
File
File

... Example : A lumber company must estimate the mean diameter of trees to determine whether or not there is sufficient lumber to harvest an area of forest. They need to estimate this to within 1 inch at a confidence level of 99%. The tree diameters are normally distributed with a standard deviation of ...
Tests of Hypotheses Using Statistics
Tests of Hypotheses Using Statistics

... The method of hypothesis testing uses tests of significance to determine the likelihood that a statement (often related to the mean or variance of a given distribution) is true, and at what likelihood we would, as statisticians, accept the statement as true. While understanding the mathematical conc ...
Introduction To Statistics - MATHCFS-STUDENTS-PAGE
Introduction To Statistics - MATHCFS-STUDENTS-PAGE

Confidence Interval for a Proportion
Confidence Interval for a Proportion

Sampling Variability and Confidence Intervals Lecture Topics
Sampling Variability and Confidence Intervals Lecture Topics

... observed number of “yes’s” divided by total sample size  This is the key summary measure for binary data, analogous to a mean for continuous data  There is a formula for the standard deviation of a proportion, but the quantity lacks the “physical interpretability” that it has for continuous data ...
第七章信賴區間估計 - 國立交通大學開放式課程
第七章信賴區間估計 - 國立交通大學開放式課程

0.95
0.95

... 21.2 < µ < 45.6 The 90% confidence interval for the mean population is 21.2 < µ < 45.6 . Based on the data from the sample, the point estimate of the mean is 33.4 which is close to the actual population mean of 32. Notice that a mean of 32 is within the 90% confidence interval. ...
Power and Its Influences
Power and Its Influences

Here
Here

Calculating the Exact Pooled Variance
Calculating the Exact Pooled Variance

Data presentation and summary Consider the following table: State
Data presentation and summary Consider the following table: State

This interval
This interval

Standard errors: A review and evaluation of standard error
Standard errors: A review and evaluation of standard error

Excel Version
Excel Version

Document
Document

JOHN S. LOUCKS Chapter 9, Part A Hypothesis Tests Developing
JOHN S. LOUCKS Chapter 9, Part A Hypothesis Tests Developing

Confidence Intervals
Confidence Intervals

16 Chapter Generalizing a Sample’s Findings to Its
16 Chapter Generalizing a Sample’s Findings to Its

6/11/2013 1 7.1 Confidence Intervals for the Mean When σ Is Known
6/11/2013 1 7.1 Confidence Intervals for the Mean When σ Is Known

Probability Sampling and Sample Sizes
Probability Sampling and Sample Sizes

Getting to the essential
Getting to the essential

< 1 ... 35 36 37 38 39 40 41 42 43 ... 285 >

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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