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6-1 Numerical Summaries
6-1 Numerical Summaries

Samples and Inferential Statistics
Samples and Inferential Statistics

Statistics: Test 3 Review
Statistics: Test 3 Review

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Samples and Inferential Statistics

SS 024a – Exam #2 - Department of Statistical and Actuarial Sciences
SS 024a – Exam #2 - Department of Statistical and Actuarial Sciences

... 4. An efficiency expert wishes to determine the average time that it takes to drill three holes in a certain metal clamp. How large a sample will he need to be 99% confident that his sample mean will be within 15 seconds of the true mean? Assume that it is known from previous drilling studies that t ...
answers - CSUN.edu
answers - CSUN.edu

... GRE with a standard deviation of 80. These students claim to be WAY more nerdy than a group of 50 California psychology students who had a mean score of 515 with a standard deviation of 50. Are the New York students truly more nerdy than the California students? t = 530-515 / 13.34 = 1.12 Tcrit = 2. ...
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Methods I Midterm Revision exercises A researcher examines the

SPSS PC Version 10: T-Test - Differences between Means1 It is
SPSS PC Version 10: T-Test - Differences between Means1 It is

... to convince us that there is a real, statistically significant difference in educational attainment between the population of Hispanic women and the population of non-Hispanic women? • The second box displays the results of the t-test that SPSS conducts to test whether or not the difference between ...
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Statistics 6 - Z

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Chapter 8 b

6/25/97 502as1
6/25/97 502as1

... significantly from 58.73? Why? 2. How would these results change if these individuals were a random sample of 15 taken from the 150 members of the tribe that are depressed? 3. Assume that the population standard deviation is 4.50 (and that the sample of 15 is taken from a very large population). Fin ...
Statistics 5 - Z
Statistics 5 - Z

... Z-Scores Objective: By the end of the lesson, you should be able to: - Determine the z-score of a given value in a normally distributed data set. - Explain the meaning of a z-score. Key Point: Z-scores allow us to compare data from different normal distributions. We have seen that different standard ...
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Organizing and Displaying Quantitative Data: the - E

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Data Analysis

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Diony George Stats 1040 TR 1-2:20 Math 1040 Skittles - E

... can be obtained, as well as testing a hypothesis or claim about population parameters. A confidence interval estimate gives us a better sense of how good a given estimate is. When different degrees of confidence are used like, 90%, 95% and 99%, the most common three, the process will result in confi ...
1. A variable that is related to either the response variable or the
1. A variable that is related to either the response variable or the

... increased after read the book at 0.05 level of significance. The value of the test statistics of the hypothesis test was found to be a. 0.0798 b. 1.725 c. 0.05 d. 0.0173 e. None of the above. ...
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Creating Plot of Group Means With Error Bars in Excel
Creating Plot of Group Means With Error Bars in Excel

... the standard errors, multiplied by the appropriate value for a confidence interval, and then point Excel to that column to read the half-width of the error bars – this is the “custom error amount.” I found a nice tutorial on all this at http://peltiertech.com/Excel/ChartsHowTo/ErrorBars.html . Retur ...
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CHAPTER 6 CONTINUOUS PROBABILITY DISTRIBUTIONS

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Note - Mathematics and Statistics

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Finding Margin of Error and Confidence Intervals

... error is based on the size of the sample and, in addition, the confidence interval desired. A 95% confidence interval means that the probability is 95% tha the true population mean is within a range of values called a confidence interval. It also means that when you select many different large sampl ...
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expected value

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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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