...Ben (Yu Zhenghao) Case study 1 February 3, 2012 Set A 1. The average age of customers is 43.08. 2. The average age of the married customers is 44.5. 3. The average age of single customers is 35.625. 4. Female:93% Male:7% 5. The average sales of single customers are 75.35 and average sales of married customers are 78.02. Married customers consumed much than single customers. Set B 1.Method of Payment | Frequency distribution | Proprietary Card | 70 | MasterCard | 14 | Visa | 10 | Discover | 4 | American Express | 2 | 2. | Relative frequency distribution | Percent frequency distribution | Proprietary Card | 0.7 | 70% | MasterCard | 0.14 | 14% | Visa | 0.1 | 10% | Discover | 0.04 | 4% | American Express | 0.02 | 2% | 3. The graph shows the proportion of five payments. It seems that proprietary card covers the most area. The bar chart clearly shows the number of five means of payments. Besides, the frequency of the use of five methods of payment is also clearly notified. 4. Proprietary Card has the most use of customers. Set C 1. 2 | 0 | 0 | 2 | 2 | 4 | 8 | 8 | 8 | 8 | 8 | | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 4 | 4 | 4 | 6 | 6 | 6 | 6 | 6 | 6 | 8 | 8 | 8 | 8 | 8 | | 4 | 0 | 0 | 0 | 0 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 6 | 8 | 8 | 8 | 8 | 8 | 8 | | 5 | 0 | 0 | 0 | 2 | 2 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 6 |...
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...- Be able to use software-generated information to make decisions in an organisation. Criteria reference To achieve the criteria the evidence must show that the learner is able to: Task No Page numbers 1 a) create a plan for the collection of primary and secondary data for a given business problem. Interview, questionnaire. b) present the survey methodology and sampling frame used. c) design a questionnaire for a given business problem 2 a) create information for decision making by summarizing data using representative values. (table w/ info summarize) b) analyse the results to draw valid conclusions in a business context c) analyse data using measures of dispersion to inform a given business scenario (median, mode, standard deviation…) d) explain how quartiles, percentiles and the correlation coefficient are used to draw useful conclusions in a business context 3 a) produce graphs using spreadsheets and...
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...Assignment Last Date of Submission: 27th February (10 marks will be deducted if anyone cross this deadline) 1. The following data give the weekly amounts spent on groceries for a sample of households. 271 |363 |159 |76 |227 |337 |295 |319 |250 | |279 |205 |279 |266 |199 |177 |162 |232 |303 | |192 |181 |321 |309 |246 |278 |50 |41 |335 | |116 |100 |151 |240 |474 |297 |170 |188 |320 | |429 |294 |570 |342 |279 |235 |434 |123 |325 | | a) How many class do you Suggest? b) What class interval do you suggest? c) Organize data in to a frequency distribution. d) Calculate relative frequency, cumulative frequency e) Draw frequency histogram. f) Draw frequency polygon. g) Calculate mean, median and mode for random data h) Calculate mean and Median for grouped data according to the frequency distribution of question no (c) 2. A student is taking two courses, history and math. The probability that the student will pass the history course is 0.60 and the probability of passing math course is 0.70. The probability of passing both is 0.50. What is the probability of passing at least one? 3. The Law firm of Hagel and Hagel is located in downtown Cincinnati. There are ten partners in the firm; seven live in Ohio and three in northern Kentucky. Ms. Wendy Hagel, the managing partner; wants to appoint a committee of three partners to look into moving the firm to northern Kentucky. If the committee is selected at random...
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...After look over the car data I was able to determine that the information providers in the chart are both discrete and continuous. The discrete data is the size of the cars and the number of cylinders. Both of this data types are discrete data because they cannot be broken down any smaller or explained in a simpler form. Discrete data cannot be half, however continuous data can. The weight, length, and mileage are all continuous data. They are continuous data because they can be broken down to small units. The level of data that the car data is a ratio scale. The data is a ratio scale because each group of data can be broke down to find it mean, median, and mode. Below is a histogram graph of the car data provided. By using the data in the chart one can come up with many different measurement or center. Here is some example of measurement of center that is given in the graph and data average weight is 3316, average lengths 180, and the average number of cylinders is 5. There also is many measurement of variation that can be found in the data the following are some example of these measurement of variation that can be found. The variation of the weight is 1855, the variation of the length is 58, and the variation of the cylinder is 4. The information giving in my opinion is an example of probability distribution. It is probability distribution because the information is all links to each other. Each column information is link to the column before it and after it. The...
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...Projects 1–4 Grading Rubrics Each project is worth 36 points total. Project 1 Grading Rubric Criteria | Instructor’s Comments/Point Deductions | 1. Displayed correct group frequency distribution8 points | | 2. Correct midpoint, relative frequency, and cumulative frequency columns7 points | | 3. Correct frequency histogram or bar graph7 points | | 4. Correct frequency polygon 7 points | | 5. Discussion of any unrealistic data points4 points | | 5.Discussion of confidence in validity of the data3 points | | All items in the grading rubric will be graded for both correctness and clarity. Project 2 Grading Rubric Criteria | Instructor’s Comments/Point Deductions | Question 1. a)4 points | | Question 1. b)4 points | | Question 2. 7 points | | Question 3.7 points | | Question 4.4 points | | Question 5.4 points | | Two Replies to other students.6 points | | All items in the grading rubric will be graded for both correctness and clarity. Project 3 Grading Rubric Criteria | Instructor’s Comments/Point Deductions | Question 1 4 points | | Question 26 points | | Question 36 points | | Question 45 points | | Question 55 points | | Question 65 points | | Question 75 points | | All items in the grading rubric will be graded for both correctness and clarity. Project 4 Grading Rubric Criteria | Instructor’s Comments/Point Deductions | Item 1. 9 points | | Item 2. 9 points | | Item 3...
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...CHAPTER TWO DESCRIPTIVE STATISTICS: TABULAR AND GRAPHICAL METHODS CHAPTER OUTLINE AND REVIEW In Chapter 1, you were introduced to the concept of statistics and in exercise *6 of that chapter you were given a frequency distribution of the ages of 180 students at a local college, but you were not told how this frequency distribution was formulated. In Chapter 2 of your text, you were informed how such frequency distributions could be formulated and were introduced to several tabular and graphical procedures for summarizing data. Furthermore, you were shown how crosstabulations and scatter diagrams can be used to summarize data for two variables simultaneously. The terms that you should have learned from this chapter include: A. Qualitative Data: Data that are measured by either nominal or ordinal scales of measurement. Each value serves as a name or label for identifying an item. B. Quantitative Data: Data that are measured by interval or ratio scales of measurement. Quantitative data are numerical values on which mathematical operations can be performed. C. Bar Graph: A graphical method of presenting qualitative data that have been summarized in a frequency distribution or a relative frequency distribution. D. Pie Chart: A graphical device for presenting qualitative data by subdividing a circle into sectors that correspond to the relative frequency of each class. 23 24 Chapter Two E. Frequency Distribution: ...
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...BES Tutorial Sample Solutions, S1/13 WEEK 2 TUTORIAL EXERCISES (To be discussed in the week starting March 11) 1. What is meant by a variable in a statistical sense? Distinguish between qualitative and quantitative statistical variables, and between continuous and discrete variables. Give examples. A variable in a statistical sense is just some characteristic of an ‘object’. It may take different values. Data on a quantitative variable can be expressed numerically in a meaningful way (e.g. height of an individual, number of children in a family. Data on qualitative variables cannot be expressed numerically in a meaningful way; e.g. sex of an individual, hair colour). A discrete quantitative variable can assume only certain discrete numerical values on the number line (can be a finite or infinite number of these values). A continuous quantitative variable can assume any value in a specific range or interval; e.g. length of a pipe. 2. Distinguish between (a) a statistical population and a sample; (b) a parameter and a statistic. Give examples. A statistical population is the set of measurements or observations of a characteristic of interest for all elementary units in a frame; e.g the shoe sizes of all men in Australia. A statistical sample is a subset of a population; e.g. the shoe sizes of all the men in the class 1 is a sample of the population represented by the shoe sizes of all men in Australia. A parameter is a numerical description of a population. For example...
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...Variables1, in statistics, are any characteristic that varies with the members of the population. For example, Dr. Blackbeard’s Stat 101 class had a test and the results are in and not all had an equal score. Some score higher than others, and some scored lower. Thus, the test scores are considered a variable which in this case is a whole number between 0-25. Another variable is the amount of time the students took to study for the test. In this case, the variable varies between a second, minute, hour, or maybe even a tenth of a second. So, variables can differ depending on the situation or the information which is given. Now two types of numerical variables2 are discrete and continuous. Discrete3 is like IQ scores, SAT scores, a person’s shoes size, and so on. Continuous4 is more along the lines of height, weight, and age. Sometimes in the real world the distinction between continuous and discrete variables can be a blur. Height, weight, and age are considered continuous in theory, but they always seem to be rounded off to the nearest inch, ounce, or year, at which point they become discrete. On the other hand, money, which in theory is a discrete variable (because the difference between two values can’t be less than a penny), is mostly thought as a continuous variable because in most real life situation a penny can be thought of as an insignificantly small amount of money. Variables can also be used to describe gender, hair color, nationality, and so on. These variables are...
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...Page 21 to 27 1. Discuss the differences between statistics as numerical facts and statistics as a discipline or field of study. 10. The Wall Street Journal (WSJ) subscriber survey (October 13, 2003) asked 46 questions about subscriber characteristics and interests. State whether each of the following questions provided categorical or quantitative data and indicate the measurement scale appropriate for each. a. What is your age? Quantitative b. Are you male or female? Categorical c. When did you first start reading the WSJ? High school, college, early career, midcareer, late career, or retirement? Categorical d. How long have you been in your present job or position? Quantitative e. What type of vehicle are you considering for your next purchase? Nine response categories include sedan, sports car, SUV, minivan, and so on. Categorical 15. The Food and Drug Administration (FDA) reported the number of new drugs approved over an eight-year period (The Wall Street Journal, January 12, 2004). Figure 1.9 provides a bar chart summarizing the number of new drugs approved each year. a. Are the data categorical or quantitative? Quantitative b. Are the data time series or cross-sectional? Data time series c. How many new drugs were approved in 2003? About 20 d. In what year were the fewest new drugs approved? How many? 2002 e. Comment on the trend in the number of new drugs approved by the FDA over the Eight-year period. The FDA approved of more new drugs between the years...
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...Assignment II: Statistics Analysis in Business 1. What is the level of measurement for each of the following variables? a. Student IQ ratings b. Distance students travel to class c. Date of birth d. Number of hours students study per week 2. Place these variables in the following classification tables. Qualitative | Quantitative | | | e. Salary f. Gender g. Temperature h. Exam score i. Student rank in the class j. Number of mobile phone k. Soft drink preference 3. A total of 1,000 residents in Minnesota were asked which season they preferred. The results were 100 liked winter best, 300 liked spring, 400 liked summer, and 200 liked fall. If the data were summarized in a frequency table, how many classes would be used? What would be the relative frequencies for each class? Conduct a frequency table. 4. A set of data consisted of 38 observations. How many classes would you recommend for the frequency distribution? 5. A set of data consisted of 230 observations between $235 and $567. What class interval would you recommend? 6. Wachesaw Manufacturing, Inc., produced the following number of units in the last 16 days. 27 27 27 28 27 25 25 2826 28 26 28 31 30 26 26 | The information is to be organized into a frequency distribution. ...
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...CHAPTER 2—DESCRIPTIVE STATISTICS: TABULAR AND GRAPHICAL DISPLAYS MULTIPLE CHOICE 1. The minimum number of variables represented in a bar chart is a.|1| b.|2| c.|3| d.|4| ANS: A PTS: 1 2. The minimum number of variables represented in a histogram is a.|1| b.|2| c.|3| d.|4| ANS: A PTS: 1 3. Which of the following graphical methods is most appropriate for categorical data? a.|ogive| b.|pie chart| c.|histogram| d.|scatter diagram| ANS: B PTS: 1 4. In a stem-and-leaf display, a.|a single digit is used to define each stem, and a single digit is used to define each leaf| b.|a single digit is used to define each stem, and one or more digits are used to define each leaf| c.|one or more digits are used to define each stem, and a single digit is used to define each leaf| d.|one or more digits are used to define each stem, and one or more digits are used to define each leaf| ANS: C PTS: 1 5. A graphical method that can be used to show both the rank order and shape of a data set simultaneously is a a.|relative frequency distribution| b.|pie chart| c.|stem-and-leaf display| d.|pivot table| ANS: C PTS: 1 6. The proper way to construct a stem-and-leaf display for the data set {62, 67, 68, 73, 73, 79, 91, 94, 95, 97} is to a.|exclude a stem labeled ‘8’| b.|include a stem labeled ‘8’ and enter no leaves on the stem| c.|include a stem labeled ‘(8)’ and enter no leaves on the stem| d.|include a stem labeled ‘8’ and enter...
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...Lecture 12: Mean Shift and Normalized Cuts CAP 5415 Fall 2006 Each Pixel Data Vector Example Once we have vectors… • Group the vectors into clusters • Algorithms that we talked about last time: – K-means – EM (Expectation Maximization) • Today: (From Comanciu and Meer) – Mean-Shift – Normalized Cuts Mean-Shift • Like EM, this algorithm is built on probabilistic intuitions. • To understand EM we had to understand mixture models • To understand mean-shift, we need to understand kernel density estimation (Take Pattern Recognition!) Basics of Kernel Density Estimation • Let’s say you have a bunch of points drawn from some distribution • What’s the distribution that generated these points? Using a Parametric Model • Could fit a parametric model (like a Gaussian) • Why: – Can express distribution with a few number of parameters (like mean and variance) Non-Parametric Methods • We’ll focus on kernel-density estimates • Basic Idea: Use the data to define the distribution • Intuition: – If I were to draw more samples from the same probability distribution, then those points would probably be close to the points that I have already drawn – Build distribution by putting a little mass of probability around each data-point • Why not: – Limited in flexibility Example Formally Kernel • Most Common Kernel: Gaussian or Normal Kernel • Another way to think about it: (From Tappen – Thesis) – Make an image, put 1(or more) wherever you have...
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...Qualitative vs. Quantitative Analysis When research is being conducted you must gather data. “Data are the facts and figures collected, analyzed, and summarized for presentation and interpretation” (Anderson, Sweeney & Williams, 2005, 5.) You must differentiate the type of data before you can analyze it. “There are basically two ways to go about an analysis, qualitative analysis and quantitative analysis” ("Difference between qualitative," 2011). “You can use both qualitative and quantitative reports to track the work performance of individuals, business units and your workforce as a whole” (Ciaran, John). Each type of data has its own advantages and many times analyzers use a combination of both types of data to make decisions. Qualitative and quantitative data are important to gather because they provide different outcomes. These are often used together when analyzing in order to get a full picture of a population. Qualitative data is either on the “nominal or ordinal scale of measurement and may be nonnumeric or numeric” (Anderson, Sweeney & Williams, 2005, 7.). This type of data focuses on interpreting raw data. This type of data is also known as “categorical” data. Qualitative data can be used to evaluate investments or other business opportunities. This type of data can also assist when it comes to decision making. Some believe qualitative analysis is “the foundation of a broad array of investment and financial decision-making methods” ("Qualitative analysis...
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...graphs and tables in questions 1–4 and to answer both parts of question 5. If you can’t figure out how to make the graphs and tables in Excel, you are welcome to draw them by hand and then submit them as a scanned document or photo. 1. Open a blank Excel file and create a grouped frequency distribution of the maximum daily temperatures for the 50 states for a 30 day period. Use 8 classes. (8 points) 2. Add midpoint, relative frequency, and cumulative frequency columns to your frequency distribution. (8 points) 3. Create a frequency histogram using Excel. You will probably need to load the Data Analysis Add in within Excel. If you do not know how to create a histogram in Excel, view the video located at: http://www.youtube.com/watch?v=_gQUcRwDiik. Or a simple bar graph will also work. If you cannot get the histogram or bar graph features to work, you may draw a histogram by hand and then scan or take a photo (your phone can probably do this) of your drawing and email it to your teacher. (8 points) 4. Create a frequency polygon in Excel (or by hand). For help, view http://www.youtube.com/watch?v=7Q-KdmDJirg (8 points) 5. A. Do any of the temperatures appear to be unrealistic or in error? If yes, which ones and why. (4 points) B. Explain how this affects your confidence in the validity of this data set. (4 points) Submit your work through the assignment link by 11:59 p.m. (ET) on Monday of Module/Week...
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... and the final sample is 1.98. The first quarter using the box plot is 2.00, and the mean is 3.00, while the 3rd quarter is at 4.00, the interquarter is 2.00, with the final category of the mode at 2.00 using the descriptive statistics to final the results for the box plot. This is also used with the frequency distribution- Quantitative and shows the frequency at 78 and the percentage at 100.0. Also using the histogram to find the results at a percentage of 25% are satisfied with the request for the desired shift was fulfilled, the highest point at 2 on the histogram chart. Question four is how many sick days in the past month? The boxplot shows this at a different point than question threes mark as it is lower and using the descriptive statistics shows the count at 78, the mean at 2.77 and the sample at 1.51 and the sample at 2.28. The highest point on the dotplot is at 2 and using the frequency distribution to find the employees have a 100% of how many sick days in the past month. Using other methods to determine that the employees are near 30% using the histogram, and the frequency polygon with how many sick days in the past...
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