know about statistical ideas and techniques • The “must-know” formulas and calculations • Core topics in quick‚ focused lessons Deborah Rumsey‚ PhD Auxiliary Professor and Statistics Education Specialist‚ The Ohio State University Statistics Essentials FOR DUMmIES ‰ by Deborah Rumsey‚ PhD Statistics Essentials For Dummies® Published by Wiley Publishing‚ Inc. 111 River St. Hoboken‚ NJ 07030-5774 www.wiley.com Copyright © 2010 by Wiley Publishing‚ Inc.‚ Indianapolis‚ Indiana
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CLEAR SHAMPOO ENDORSEMENTS‚ GENDER‚ AND DE LA SALLE UNIVERSITY-MANILA STUDENTS’ BUYING BEHAVIOR In partial fulfillment of the requirements for CBESTA2 K36 Submitted to: Mrs. Maria Angeli T. Reyes Submitted by: Chua‚ Bettina Dela Cruz‚ Felicia Enriques‚ Robee Eusebio‚ Patrick Garcia‚ Andrea Garcia‚ Patrick September 1‚ 2010 Table of Contents Tables and Figures 1 Abbreviations and Acronyms 7 Executive Summary 7 Main Report Introduction (1-2pages) 8
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Descriptive Statistics Descriptive statistics is the analysis of data that summarize data in a way such that‚ meaningful patterns emerge from the data. Descriptive statistics do not allow us to reach to the conclusions beyond the data we have analyzed regarding any hypotheses we might have made. They simply describe our data. Measures of Central Tendency: these are ways of describing the central position of a frequency distribution for a group of data through: Mode: Sum of all observations divided
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Meisner September 16th‚ 2012 This week’s assignment is about the use of misleading statistics. Companies and individuals use statistics in a variety of way in order to provide information on certain things. The use of misleading statistics‚ while not ethical‚ could be viewed as valuable‚ if it increases profit margins or awareness of the stated topic of the statistic in a biased way. Question number 8 provides a statistic that Vitamin E is an antioxidant that may help fight heart disease and
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Statistics in Business Katherine Wolf QNT/275 April 3‚ 2015 Barry Adkins Statistics in Business Statistics is all about collecting numerical data‚ organizing it‚ and interpreting the data to form hypothesis. While interpreting the data we are able to draw all kinds of conclusions from weather forecasts to how much money a company is projected to make over the next year. There are different types of statistics that can be used for different outcomes. You have to first identify the
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n = 25 Use a one-sample t-test Conditions/assumptions for a t-test •Random sample- our survey was a random sample of 25 stations •Normal distribution –we are assuming gasoline prices are normally distributed Null hypothesis: The mean regular unleaded gas prices for your region is the same as that in the study µ 0 = 3.16 Alternate hypothesis: The mean regular unleaded gas prices for your region is greater than that in the study µ A > 3.16 Find the t-test statistic: t=(x(bar)
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| | Statistics for Public Administration: Practical Uses for Better Decision Making | | | Maureen BernerAs a public administrator‚ you need to be able to analyze and evaluate policies‚ and understand analysis and evaluations done by others. This means having a solid working knowledge of how to apply statistics to the types of data used in local government so that you make good decisions.Statistics for Public Administration: Practical Uses for Better Decision Making‚ a completely updated
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Statistics Investigation Introduction A new drug was developed that was claimed to lower the cholesterol level in humans. A leading heart specialist was interested to know if the claim made by the company selling the drug was accurate. They enlisted the help of 50 patients. They agreed to take part in an experiment in which 25 patients would be randomly allocated to a group that would take the new drug and the other 25 would take an identical looking pill that was a placebo (a sugar pill that
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(97-83)2 + (60 - 83)2 } / (8 -1) = 196.29 S0‚ standard deviation = √196.29 = 14.01 d. The coefficient of variation = 14.01/83 * 100% = 16.88% 93 65 80 97 85 87 97 60 4. The following data represent the daily supply (y in thousands of units) and the unit price (x in dollars) for a product. Daily Supply (y) Unit Price (x) 5 2 7 4 9 8 12 5 10 7 13 8 16 16 16 6 1 a. e an in er re = 11 X = 7 e a e c variance r ea ve a a. = ∑ - X - X / n-1 (y) 5 7 9 12 10 13 16 16
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Inferential Statistics Drawing Conclusions based on Samples Introduction This chapter introduces how you can use data from a sample to draw conclusions about the larger population from which the sample was taken. Data often arises from the results of a survey of individuals. For example‚ the management of a fast food chain might be interested in determining the total number of dollars that Baylor students spend each year eating in Waco fast food restaurants. The fast food chain would
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