on the left is positively skewed. The one on the right is negatively skewed. Measure of Skewness: 1. Karl Pearson coefficient of Skewness Sk = 3(mean - median) / Standard Deviation. = 3(X –Me) / S 2. The skewness of a random variable X is denoted or skew(X). It is defined as: where and are the mean and standard deviation of X. Interpretation: 1. If Sk = 0‚ then the frequency distribution is normal and symmetrical. 2. If Sk 0‚ then the frequency distribution is positively skewed. 3. If Sk 0‚
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Difference between Depreciation by Straight Line Method and Depreciation by Reducing Balance Method 6 2.0 The Difference 6 Question 3 - Standard Deviation and Quartile Deviation 7 Standard Deviation 7 Quartile Deviation 8 3.0 Purpose of Calculating Standard Deviation and Quartile Deviation 8 3.1 Calculation of Standard Deviation and Quartile Deviation 8 Reference 9 Question 1 – Difference between Simple Interest and Compound Interest To know the difference between simple and compound interest
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Statistics – Lab Week 2 Name:Michael Jacks Math221 Statistical Concepts: * Using MINITAB * Graphics * Shapes of Distributions * Descriptive Statistics * Empirical Rule Data in MINITAB * MINITAB is a powerful‚ yet user-friendly‚ data analysis software package. You can launch MINITAB by finding the icon and double clicking on it. After a moment you will see two windows‚ the Session Window in the top half of the screen and the Worksheet or Data Window in the bottom
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calculate: =NORMDIST(5.5‚1.88‚1.99‚True) X= 5.5 Mean= 1.88 Standard Deviation=1.19 b) 32 to 35 weeks = 43.83% The NORMDIST formula was used to calculate: =NORMDIST (5.5‚5.73‚1.48‚True) X= 5.5 Mean= 5.73 Standard Deviation=1.48 c) 37 to 39 weeks = 4.66% The NORMDIST formula was used to calculate: =NORMDIST(5.5‚7.33‚1.09‚ True) X= 5.5 Mean= 7.33 Standard Deviation=1.09 d) 42 weeks and over = 2.75% The NORMDIST formula was used to
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surplus/deficit. There is a huge standard deviation in the data given for GDP. In both 2009 and 2010 the standard deviation was over four and a half times larger than the average of GDP itself. This will make it hard to create general assumptions for all countries to assess whether different factors correlate with each other. Even other factors such as GDP growth have relatively large standard deviations. GDP growth has a very large standard deviation. An example of ambiguous data can be seen when
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Chi-squared Analysis Problem Sets • Exercises 19 and 20 (Ch. 17) Chapter 10 31. A new weight-watching company‚ Weight Reducers International‚ advertises that those who join will lose‚ on the average‚ 10 pounds the first two weeks with a standard deviation of 2.8 pounds. A random sample of 50 people who joined the new weight reduction program revealed the mean loss to be 9 pounds. At the .05 level of significance‚ can we conclude that those joining Weight Reducers on average will lose less than 10
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Introduction to Basic Statistics Pat Hammett‚ Ph.D. 2005 Instructor Comments: This document contains an overview of basic probability and statistics. It also includes a practice test at the end of the document. Note: answers to the practice test questions are included in an appendix. 1 Pat Hammett University of Michigan Table of Contents 1. VARIABLES- QUALITATIVE AND QUANTITATIVE......................3 1.1 Qualitative Data (Categorical Variables or Attributes) .............
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8 – 23 MERRILL FINCH INC. RISK AND RETURN a. (1) Why is T-bill’s return independent of the state of the economy? Do T-bill’s promise a completely risk-free return? Explain (2) Why are High Tech’s returns expected to move with the economy‚ whereas‚ Collections’ are expected to move counter to the economy? 1. The 5.5% T-bill return does not depend on the state of the economy because the Treasury must redeem the bills at par regardless of the state of the economy; therefore‚ T-bills are
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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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The average (mean) annual income was less than $50‚000‚ One-Sample Z: Income ($1000) Test of mu = 50 vs < 50 The assumed standard deviation = 14.64 95% Upper Variable N Mean StDev SE Mean Bound Z P Income ($1000) 50 43.74 14.64 2.07 47.15 -3.02 0.001 α 0.05= -1.645 H0 μ = 50‚000 Ha μ < 50‚000 The hypothesis test claims that the average annual income was less than $50‚000. H0 claims equal
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