Table 1.1 (Hint: This is a frequency table. Read the section in the textbook!) a. Find the sample mean x = 1.48 b. Find the sample standard deviation‚ s = 1.12 c. Complete the columns of the chart. = d. Find the first quartile. = 1 e. Find the median. = 1 f. Find the third quartile. = 2 g. What percent of the students saw fewer than three movies? = 80% h. Find the 40th percentile. = 1 i. Find the 90th percentile. = 3 Exercise 2 (5 points) I. A “random survey” was conducted of 3274 people
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Why do forecasts fail? How do you recommend improving the results? What tools would you use? These are very important questions that you should ask yourself when making‚ monitoring‚ and updating a forecast. The answers to these questions will help you make a more accurate forecast or help you update or fix a forecast that may already be in place. Forecasts in their own nature are expected to have some type of error but with the correct techniques it can be measured and monitored. Some factors
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LL Beans Please submit your analysis of this case. In addition‚ be prepared to discuss your analysis in class. 1. How significant (quantitatively) of a problem is the mismatch between supply and demand for LL Bean? As per the historical series and its associated statistical description (see graph below)‚ we can observe that there is a significant spread between the A/F ratios sine the standard deviation equals 1/3 of the mean. Besides in cases‚ there is mismatch beyond 50% between the forecast
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TAGUCHI LOSS FUNCTION EXAMPLE PROBLEMS 1. A blueprint specification for the thickness of a dishwasher part at Partspalace‚ Inc. is0.325 ± 0.025 centimeters (cm). It costs $10 to scrap a part that is outside thespecifications. Determine the Taguchi loss function for this situation. 2. A team was formed to study the dishwasher part described in Problem 1. Whilecontinuing to work to find the root cause of scrap‚ they found a way to reduce the scrapcost to $5 per part. a. Determine the Taguchi loss
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1.0 Introduction This report will cover the distribution of final exam results for BSB123 and what factors influence the results. Factors that will be considered are the gender of the student‚ whether the student is studying a double or single degree‚ the results from the weekly quiz’s and the grade achieved on the mid semester report. The presence of outliers will be determined to help analyse the accuracy of the data. There are an infinite number of internal and external factors that contribute
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| Statistics 101 Report | The Kentucky Milk Case Study | | | | Preliminary Analysis 2a) Figure 1: X as a Data Object X is a data frame as derived from the program R shown above in Figure 1. There are 274 observations of 11 variables. The number of observations is obtained from the number of rows while the number of variables is obtained from the number of columns. 2b) Figure 2: Creating a sub-data frame from X Figure 3:Sub-data frame from X Figure 2 shows a screenshot
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STA2300 Data Analysis Faculty of Sciences Study Book Ashley Plank with contributions from The LTS Team‚ Christine McDonald‚ Paul Fahey‚ Peter Dunn‚ Shahjahan Khan‚ Taryn Swan & Rachel King ii c University of Southern Queensland‚ 2012.1 Published by University of Southern Queensland Toowoomba Qld 4350 Australia http://www.usq.edu.au Copyrighted materials reproduced herein are used under the provisions of the Copyright Act 1968 as amended‚ or as a result of application to the copyright
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Cultural Relativism and Global Values The Median That Works Universal values and human rights are abstractions that are considered by many as little more than a romantic concept. Those who would like to believe in a set of universal values find that they either can not find enough evidence for‚ or that there is too much evidence against such values. Cultural relativism‚ a relatively new idea in political science that has its origins in anthropology‚ is the major evidence and argument against
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Solutions Manual to accompany STATISTICS FOR ENGINEERS AND SCIENTISTS by William Navidi Table of Contents Chapter 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Chapter 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 Chapter 3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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Common Appraisal Errors Error Contrast Effect Definition Tendency of a rater to evaluate people in comparison with other individuals rather than against the standards for the job Example Think of the most attractive person you know and rate this person on a scale of 1 to 10. Now think of your favorite glamorous movie star. Rerate your acquaintance. If you rated your friend lower the second time‚ contrast effect is at work. A new supervisor noticed an employee who was going through a divorce performing
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