and data must be gathered to make sure that all details match and that all requirements are met. It would be safe to assume that our theory that the larger the house and the more rooms a house has‚ the more expensive the price of the house will be. The three major variables in our data summary are: number of bedrooms‚ size of the house‚ and number of baths. According to our information compiled (see attached excel file for mega stat)‚ the measure of tendency that best represents the data set
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Summarizing and Presenting Data Team B: Timothy Sosa‚ Gregory Moreno‚ Janice Cruz QNT/351 March 23‚ 2015 Steve Roussas Summarizing the Data The data collected in the BIMS case study had two major errors. The first error was when the office support staff member made the decision to use “0” if the employee did not answer the question. Two out of the ten questions received a response from each employee who completed the survey. About 17 employees provided a no response in eight of the questions and
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Managed Services (BIMS) their main office is in New York City that provides housekeeping and food service to different corporations and institutions throughout the country. BIMS main focus is their core competencies with contracts along with large organizations. They also provide lease support to outside vendors. BIMS clientele consist of 22 fortune 100 companies‚ 100 average firms‚ 16 high ranking universities‚ 14 medical centers‚ and three large airports. With a large company such as BIMS handling large
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Techniques for Summarizing Quantitative Data frequency histogram A sample of 40 female statistics students were asked how many times they cried in the previous month.Their replies were as follows: Stem-Leaf Plot A natural way to organize (group) quantitative data is with the order property of the real numbers‚ i.e.‚ arrange the data from least to greatest. For example‚ the 30 weights: 185‚ 160‚ 235‚ 165‚ 125‚ 175‚ 185‚ 132‚ 168‚ 112‚ 170‚ 155‚ 105‚ 158‚ 120‚ 190‚ 140
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Examine the data before settling on a specific analytical method. The nature of the data affect‚ and may even limit‚ the type of analysis you can conduct. Consider both the dependent variable (also known as the outcome variable) and the independent variable(s). Are these data categorical‚ ordinal or interval? Categorical data‚ in which the values of the data represent categories (such as gender‚ ethnicity‚ political affiliation)‚ are the simplest. Attitude scales such as the Likert scale are examples
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credit customers is selected with data collected on the following five variables: 1. LOCATION (Rural‚ Urban‚ Suburban) 2. INCOME (in $1‚000 ’s – be careful with this) 3. SIZE (Household Size‚ meaning number of people living in the household) 4. YEARS (the number of years that the customer has lived in the current location) 5. CREDIT BALANCE (the customers current credit card balance on the store ’s credit card‚ in $). |PROJECT PART A: Exploratory Data Analysis
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Analyzing and Interpreting Data – BIMS‚ Inc. QNT/351 Analyzing and Interpreting Data – BIMS‚ Inc. Consulting Group – Team D has performed a series of analysis on behalf of the top management of Ballard Integrated Managed Services‚ Inc (BIMS). These tasks were the result of an emerging trend of attrition and employee dissatisfaction within their organization. The initial actions taken involved data collection that were presented in the form of an internal employee survey. The data collection analysis
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Alberto Sikaffy CMGT 263 Professor Paul 11/17/14 Building Information Modeling Building Information Modeling (BIM) is an intelligent model-based process that provides insight to help you plan‚ design‚ construct‚ and manage buildings and infrastructure. BIMs are files that can be exchanged or networked to support decision-making about a place. Businesses and Individuals use BIM software to construct and maintain physical infrastructures from water‚ wastewater‚ electricity‚ bridges‚ ports‚ apartment
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Chapter 3 Summarizing Data 0.1 0.2 Introduction...........................................................................Error! Bookmark not defined. A Section Title .......................................................................Error! Bookmark not defined. Demonstration: ............................... Error! Bookmark not defined. Exercises ................................................................................... Error! Bookmark not defined. 0.3 0.4 Chapter Summary .
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Unit 1 Problem Set 1: Using Statistical Thinking and Summarizing Data 1. (Page 11 #26) – a. Yes b. Yes‚ because there is about a 1 chance in 1000 of getting the types of success rates generated through the study. c. Yes‚ because a 92% success rate is a much better result than a 72% success rate. d. Yes‚ after the study has been conducted numerous times to have validity. 2. (Page 17 #30) – a. The sample are the 1012 randomly surveyed adults. b. The population is that of all adults.
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