Statistics Case Study-1 Age Weeks Employed 55 21 30 18 23 11 52 36 41 19 25 12 42 7 45 25 25 6 40 21 25 13 25 11 59 34 49 27 33 18 35 20 a. Age Weeks Employed Mean 37.75 Mean 18.6875 Standard Error 2.974195 Standard Error 2.188452 Median 37.5 Median 18.5 Mode 25 Mode 21 Standard Deviation 11.89678 Standard Deviation 8.753809 Sample Variance 141.5333 Sample Variance 76.62917 Kurtosis -1.17143 Kurtosis -0.21626 Skewness 0.337402 Skewness 0.522601 Range 36 Range 30
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Department Stores. By identifying our mean‚ median and mode regarding each variable (marital status‚ type of customer‚ gender and age)‚ we can further understand the relationships between our variables. Descriptive Statistics on Net Sales: Net Sales (All Customers) Mean Median Mode Range Standard Deviation Coefficient of Variation $77.60 $59.71 $31.60 274.36 55.66 71.73 Descriptive Statistics on Net Sales by Various Classifications of Customers: Mean Median Mode Range Standard
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encompassed ten University students and ten teachers and examined the effect of an Attentive Audience condition compared to Mere Presence condition on the performance of a complex word task. No significant difference was found between both conditions in the mean number of words obtained. This study supports Zajonc ’s (1965) Drive Theory as the most reliable indicator in determining individual ’s behaviour in the presence of others. A major area of social psychology is the study of an individual ’s behaviour
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linear or non-linear models fitted to time-series data‚ usually by regression methods 4. Box-Jenkins methods: autocorrelation methods used to identify underlying time series and to fit the "best" model COMPONENTS OF TIME SERIES DEMAND 1. Average: the mean of
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Group 7 MOTION PICTURES INDUSTRY- CASE STUDY The data collected for a sample of 100 motion pictures produced in 2005 is given below. A survey is carried out to analyze how different variables of the Motion Picture Industry contribute to the success of its motion pictures. The study focuses on four major variables‚ Opening Gross Sales‚ Total Gross Sales‚ Number of Theatres and weeks in top 60. Motion Picture Opening Gross Sales( $millions) Total Gross Sales ($ millions) Number of Theaters
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` Milkfish Chanos chanos Scale as Bioplastic Project Proponent: Rj G. Erestingcol Project Adviser: Mrs. Revilla Garcia Acknowledgement The researcher wishes to extend her heartfelt gratitude to the following persons and entities for their invaluable services in coming up with this science endeavor. To Almighty GOD‚ for his provision and guidance. To the researcher’s parents‚ for their undying support‚ financial and emotional‚ for being an inspiration. To Mr. Arturo S
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CHAPTER 1: Productivity = Units producedInput used (1-1) Multifactor productivity (total factor) = ductivity) qqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqOutputLabor+Material+Energy+Capital+Miscellaneous (1-2) CHAPTER 7: Value-added time = Operation time/total time (H-B) SUPPLEMENT 7: Utilization = Actual OutputDesign Capacity (S7-1) Efficiency = Actual OutputEffective Capacity (S7-2) Actual (Expected) output = (Effective
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development: physical maturity and emotional maturity. The first type of maturity‚ the physical‚ is easiest for us to fully understand. It is a scientific kind of maturity that is visible and quantifiable. For example‚ I am seventeen. While this literally means that since I was born the earth revolved around the sun roughly seventeen times‚ it more importantly is an approximated measure of physical maturity. By telling you I’m seventeen‚ you’ve most like already assumed that I don’t have wrinkles on my face
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Chapter 2: Descriptive Statistics CHAPTER 2: Descriptive Statistics 2.3 [LO 1] 28 2007 #1 28 71‚273.93 58‚069‚987.70 7‚620.37 59490 87970 28480 Distribution is skewed right. Descriptive statistics count mean sample variance sample standard deviation minimum maximum range Stem and Leaf plot for stem unit = leaf unit = Frequency 2 9 13 4 28 #1 10000 1000 Stem 5 6 7 8 Leaf 99 123446677 0000112444447 1377 Distribution is more normally shaped in 2007. 2.5 [LO 2] a. We have 2
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mailman delivers the mail follows a normal distribution with a mean of 2:00PM and a standard deviation of 15 minutes (20 pts) a) What is the probability that the mail will arrive before 1:50PM? b) What is the probability that the mail will arrive after 2:30PM? c) What is the probability that the mail will arrive between 1:40PM and 2:20PM? d) Between what two times (equally before the mean and equally after the mean) accounts for the mail being delivered 95% of the time?
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