...Group Project: Coca Cola CocaProProposal Group Project: Coca Cola CocaProProposal Megan Bond (Team Lead) Taryn Keenan Allysa Kiedpool Krista Samples Nicole Smith July 12, 2014 BA 615 Dr. Mohammad Oskoorouchi Megan Bond (Team Lead) Taryn Keenan Allysa Kiedpool Krista Samples Nicole Smith July 12, 2014 BA 615 Dr. Mohammad Oskoorouchi Contents Executive Summary………………………………………………………………………………………………3 Analysis & Approach…………………………………………………………………………………………….4 Pie Charts……………………………………………………………………………………………………4 Line Charts……………………………………………………....……………………………………..…5 Descriptive Statistics and Variation…………………………………….………………………8 Histograms…………………………………..…………………………………….……………….……10 Confidence Intervals…………………………………..……………………………………….……13 Hypotheses and Hypothesis Test…………….……………………..………………………..15 Scatter Plots and Correlation……………….……………………………………………….....18 Conclusion……………………………………………………………………………………………….…………22 Recommendations…………….…………………………………………………………………….…………22 Executive Summary As a company, Coca Cola always strives to keep their customers happy. The corporate goal is to deliver all customer orders with 100% accuracy and within the customer’s time window. A metric the company has developed to measure this is On Time and In Full (OTIF), which illustrates the percentage of the orders sent out on a particular day that were within the customer’s time window and with 100% of the cases the customer...
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...TERM END EXAMINATIONS,MARCH-2013 BACHELOR OF COMMERCE, YEAR – III ELEMENTARY STASTISTICS Time: 3 hours M.Marks:60 SECTION A Note: - Attempt any 4 questions. All questions carry equal marks. (4 X 5) The answer should be limited upto 200 words. 1) What is statistics? Explain the nature and limitations of statistics? 2) What is frequency distribution? What are the different types of frequency distribution? 3) What is frequency curve? Explain cumulative frequency curve with example? 4) Suppose mean of a series of 5 item is30.four values are respectively, 10, 15, 30 and 35.estimate the missing 5th value of the series. ANSWER : Mean = (10+15+30+35+x)/5=30 Therefore, x=(30*50)-( 10+15+30+35) i.e x = 150-90, hence x=60 5) Calculate median of the following distribution of data. Class interval | 0-5 | 5-10 | 10-20 | 20-30 | 30-50 | 50-70 | 70-100 | frequency | 12 | 15 | 25 | 40 | 42 | 14 | 8 | n= 12+15+25+40+42+14+8=156 Hence median is at the average of n/2 & (n/2 +1) positon i.e 78th & 79th position Class interval | 0-5 | 5-10 | 10-20 | 20-30 | 30-50 | 50-70 | 70-100 | frequency | 12 | 15 | 25 | 40 | 42 | 14 | 8 | Position 12 27 52 92 134 148 156 6) Calculate the coefficient of correlation...
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...exclusive. That is, if one is true, the other must be false; and vice versa. Formulate an Analysis Plan The analysis plan describes how to use sample data to accept or reject the null hypothesis. It should specify the following elements. * Significance level. Often, researchers choose significance levels equal to 0.01, 0.05, or 0.10; but any value between 0 and 1 can be used. * Test method. Use the one-sample z-test to determine whether the hypothesized population proportion differs significantly from the observed sample proportion. Analyze Sample Data Using sample data, find the test statistic and its associated P-Value. * Standard deviation. Compute the standard deviation (σ) of the sampling distribution. σ = sqrt[ P * ( 1 - P ) / n ] where P is the hypothesized value of population proportion in the null hypothesis, and n is the sample size. * Test statistic. The test statistic is a z-score (z) defined by the following equation. z = (p - P) / σ where P is the hypothesized value of population...
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...introduce you to SPSS. You are encouraged to actively access the interactive help of SPSS and ask your tutor questions if you get lost. You are expected to spend time outside of class (i) familiarising yourself with SPSS and (ii) completing the homework exercises to complete the Assignment 2 (Research Report) for assessment (20% of your MKTG7510 Grade). SPSS is accessible in all computer labs in Building 39A and 35. Please check current times of access to the computer labs and plan your study time accordingly. 1. How to create a new project? (1) Click on the ‘Start’ menu. Click on ‘All Programs’. (2) You will find the folder ‘Data Analysis and Stats Programs’ - click it to open the folder. (3) Click on the ‘IBM SPSS statistics’ folder. Click on the ‘IBM SPSS Statistics’ icon. (4) Once SPSS is open, the screen (see Fig. 1) asks you what you would like to do: either type in the data manually, or find an existing data file (as circled below). Figure 1: Create a New Project MKTG7510 Market and Consumer Research S1-2014 P. 1/20 2. What are the main components of an SPSS ‘project’? When you open a new ‘project’ you will see the Data Editor window where the raw data are visible. However, there are multiple windows that allow you to interact with the data in different ways. The following is a list of the main components of a ‘project’. i. Data Editor: screen containing all the data and information for one study. You can have multiple worksheets open when working in SPSS...
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...2. Descriptive Statistics Once the Data collection process has been completed the researcher must then try to make sense of the information collected. The data at this stage will be in its raw form and will generally not be suitable for presentation or interpretation The purpose of descriptive statistics, as the name suggests, is to describe a set of data. They are used to provide manageable summaries of data sets. They are the simplest and most widely used set of statistics and in many data analysis projects they will provide all the information required. There are many techniques available for describing a set of data. In this course we will look at three groups of univariate statistics, namely i) Frequency Distributions (ii) Measures of Central Tendency (iii) Measures of Dispersion We will also examine three bivariate techniques i) Crosstabs (ii) Tables of Means (iii) Correlation 2.1 Frequency Distributions - The frequency of a variable value is the number of times that value occurs in a set of data - A frequency distribution is simply a table of frequencies for all possible values of the variable. - They are relatively simple to construct and interpret; yet they still provide a very powerful tool for examining data. Example 2.1 2.1 Frequency Distribution of the number of cars owned by 500 households |Number of Cars |Frequency | |0 |70 ...
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...country of measuring its GNP and interpreting these statistics. Most countries use Gross National Product (GNP) as an indication of the economic welfare of a country. However, there are numerous problems involved when calculating the GNP. This is especially true in developing countries. The first problem that a developing country might encounter is the existing use of barter in the economy. Barter is not included in the proper records of economic activity. Therefore governments in developing countries find it hard to gather the statistics for the calculation of the GNP. It is even possible for countries to not even have proper records of economic activity. Furthermore, a developing country might lack the resources or skills required in order to collect the data needed. They may not be able to afford allocating skilled manpower and other resources to collect data. The government themselves may be incompetent or inefficient which may lead to inaccurate and unreliable measurements of the GNP. In developing countries, non-marketed goods and illegal goods may distort the values of the GNP. Unpaid services such as housewives, self-consumed output and payment in kind lead to the inaccuracy in the GNP. Since many developing countries depend on their primary sector heavily, such as farming, include goods that are kept for subsistence purposes. Such goods are not recorded in the GNP statistics. Even if the GNP statistics were accurate, there is a problem faced with interpreting...
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...terms: • Central Limits Theorem: A statistical theory that states that given a sufficiently large sample size from a population with a finite level of variance, the mean of all samples from the same population will be approximately equal to the mean of the population. Furthermore, all of the samples will follow an approximate normal distribution pattern, with all variances being approximately equal to the variance of the population divided by each sample's size. This statistical theory is very useful when examining returns for a given stock or index because it simplifies many analysis procedures. An appropriate sample size depends on the data available, but generally speaking, having a sample size of at least 50 observations is sufficient. Due to the relative ease of generating financial data, it is often easy to produce much larger sample sizes. • Null Hypothesis: States the assumption (numerical) to be tested, for Example: The average number of TV sets in U.S. Homes is at least three (H0: μ ≥ 3). 1. Is always about a population parameter, not about a sample statistic. ✓ H0: μ ≥ 3 X H0: [pic] ≥ 3 Always begins with the assumption that the null hypothesis is true, similar to the notion of innocent until proven guilty. Refers to the status quo. Always contains “=”, “≤” or “≥” sign. May or may not be...
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...any of these are missing, you will not get full points for DQs. Your follow up posts should be at least 100 words, and MUST be in your own words. Textbook: Grove, S. K. (2007). Statistics for health care research: A practical workbook. Edinburgh: Elsevier Saunders. Topic 1 DQ 1: How can graphics and/or statistics be used to misrepresent data? Where have you seen this done? Statistics are used everywhere, every day to represent a multitude of data, or study sample. Sample characteristics are the traits that depict the study sample and can be portrayed in either some type of table or in an article (Grove, 2007). “Descriptive statistics are used to generate sample characteristics, and the type of statistic used depends on the level of measurement or the demographic variables included in the study” (Grover, 2007, p.75). It is this information and data that is presented can be misrepresented, either unethically or simply because the data is misunderstood. According to Statistics (2013), data can be misrepresented three ways: 1) misunderstanding the data presented, 2) using incomparable definitions, and 3) by deliberately misinterpreting the data presented, especially if it is a biased representation. An example of misunderstanding the data presented could be someone that is researching crime statistics in an area where they are looking to relocate and raise a family; if they do not understand the manner in which the data is being presented, it could be more than easy for them to...
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...snaptutorial.com PSYCH 625 Week 1 Individual Assignment Basic Concepts in Statistics Worksheet PSYCH 625 Week 1 Individual Assignment Reliability and Validity Matrix PSYCH 625 Week 1 Individual Assignment Time to Practice – Week One PSYCH 625 Week 2 Individual Assignment Time to Practice – Week Two PSYCH 625 Week 2 Learning Team Assignment Statistics Project Import Data Into IBM ® SPSS ® Software PSYCH 625 Week 3 Individual Assignment Time to Practice – Week Three PSYCH 625 Week 3 Learning Team Assignment Hypothesis Testing Problem Worksheet PSYCH 625 Week 3 Learning Team Assignment Statistics Project Descriptive Statistics PSYCH 625 Week 4 Individual Assignment Time to Practice – Week Four PSYCH 625 Week 4 Learning Team Assignment Statistics Project Comparing Means PSYCH 625 Week 5 Individual Assignment Programmatic Assessment Time to Practice – Week Five PSYCH 625 Week 5 Learning Team Assignment Statistics Project Correlations PSYCH 625 Week 6 Individual Assignment Overview of Important Statistical Tests PSYCH 625 Week 6 Learning Team Assignment Statistics Project Presentation ----------------------------------------- PSYCH 625 Week 1 Individual Assignment Basic Concepts in Statistics Worksheet For more classes visit www.snaptutorial.com Complete the following questions. Be specific and provide examples when relevant. Cite any sources consistent with APA guidelines. What are statistics and how are they used in the behavioral sciences? Your answer should be...
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...analysis with statistics has got less to do with numbers, and more to do with rules for arranging them. It even lets you create some of those rules yourself, so instead of looking at it like a lot of memorization, it's best to see it as an extension of the research mentality, something researchers do anyway (i.e., play with or crunch numbers). Once you realize that YOU have complete and total power over how you want to arrange numbers, your fear of them will disappear. It helps, of course, if you know some basic algebra and arithmetic, at a level where you might be comfortable solving the following equation There are three (3) general areas that make up the field of statistics: descriptive statistics, relational statistics, and inferential statistics. 1. Descriptive statistics fall into one of two categories: measures of central tendency (mean, median, and mode) or measures of dispersion (standard deviation and variance). Their purpose is to explore hunches that may have come up during the course of the research process, but most people compute them to look at the normality of their numbers. Examples include descriptive analysis of sex, age, race, social class, and so forth. 2. Relationalstatistics fall into one of three categories: univariate, bivariate, and multivariate analysis. Univariate analysis is the study of one variable for a subpopulation, for example, age of murderers, and the analysis is often descriptive, but you'd be surprised how many advanced statistics can be computed...
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...Exercise: 11 1. What demographic variables were measured at least at the interval level of measurements? Number of hours working per week and Length of labor 2. What statistics were used to describe the length of labor in this study? Were these appropriate? Descriptive Yes, Frequency (30) and mean (14.63) are used to describe the data. 3. What other statistic could have been used to describe the length of labor? Provide a rationale for your answer. Length of labor was described for both the experimental and control groups using means (14.63) and standard deviations (7.78). The exact length of labor was obtained, providing ratio level data that are descriptively analyzed with means and standard deviations. 4. Were the distributions of scores similar for the experimental and control groups for the length of labor? Provide a rationale for your answer. No, the distributions of scores were not similar for the two groups. Experimental group has slightly higher dispersion (n=30 and SD= 7.78) than control group (N=33 and SD=7.2). Standard deviation decreases with larger sample sizes. 5. Were the experimental and control groups similar in their type of feeding? Provide a rationale for your answer. Yes. Bottle-feeding was the mode for the experimental (53.1%) and the control (50%) groups since it was the most frequent type of feeding used by both groups 6. What was the marital status mode for the subjects in the experimental and control groups? Provide both the frequency...
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...Descriptive Statistics Paper Research and Evaluation I RES/341 July 20, 2011 Patricia Towne University of Phoenix Descriptive Statistics Paper Last week, team C paper included a hypothesis that explained why gas prices were on the rise and the factors that play a part it in. This week, after further research and evaluation team C conducted intense research to support and confirm the articles that explained the different situations that involved crude oil and the reason why it plays a factor on the price of gasoline. Team C thoroughly conducted research with the help of the UOP library and many online sources to help aid in their investigation. There are numerous factors that play a part in the calculation of this data and this paper will focus on the information gathered with the use of calculation of descriptive statistics, frequency distribution, and histogram. A part of descriptive statistics involves calculating the measures of central tendency and dispersion. Central tendency involves estimates of the mean, mode, and median. The mean can be used in describing central tendency. The mode is the most frequently occurring value in the set of scores (Lind, Marchal, Wathem, 2005). And the median is the score that is found at the exact middle of the set of values. By reviewing and analyzing the central tendencies of the data the below graph will help further explain the central tendency, dispersion, and skew for team C’s data. Looking over the histogram regarding gas...
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...variances with the outcomes. Hypothesis Test #1 Looking at Intrinsic Satisfaction by Gender H0:Males= μ Females H1:Males= μ Females ∝ = .05 Significance level: 0.05 T statistic: 2.05 Critical T values: -1.98 and 1.98 P value: 0.04 Since the test statistics of 2.05 is greater than critical value of 1.98 we reject to reject the Ho. The decision made for the intrinsic job satisfaction survey, which intrinsic is when one does it for one’s satisfaction. The data says that it does not support the intrinsic Ho. The p value is less than the alpha. Managers for this business could use this information specifically to help drive the motivation of their employees. This information could show value in the growth of the company with having employees that enjoy their job and complete for their own satisfaction not for reasons such as outside rewards could say a lot about working for this company. If employees first enjoy their job this could mean greatness for the company, employees will strive to better the company and their self-worth to the company. Hypothesis Test #2 Looking at Extrinsic Satisfaction by Position H0:Hourly= μ Salary H1:Hourly= μ Salary ∝ = .05 Significance level: 0.05 T statistic: -0.84 Critical T values: -1.98 and 1.98 P value: 0.41 Since the test statistics of -0.84 is less than the critical value of 1.98 we fail to reject the Ho. The data supports Ho the fact that the outcome is...
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...Introduction In a quest to determine if there exist a disparity in wages and earnings between men and women examining data for those specific variables are critical. Data collection will assist the research team in determining if there is a substantial difference in the wages and earnings between these two groups of individuals in their respective occupational fields. The data collection may display a number of possible outcomes. One of the possible outcomes from the data may show on average men do earn considerably more than women. Another outcome may show on average there are no substantial disparities in earnings and wages between men and women. Earlier research shows that there were factors that contribute to wage disparities such as age, experience, education, and job location. Although those factors are important to research so are the data that analyzes average wages and earnings of men and women. According to Orris, ‘measures of central tendency are the methods we use to summarize data by trying to find one number that best represents all the numbers in a sample or population. There are three primary measures of central tendency: the mode, the median, and the mean,’(page 12). The mode is the most frequently occurring data value. It may be similar to the mean and median, if data values near the center of the sorted array tend to occur often. But it may also be quite different from the mean and median. The median is especially useful when there are extreme values, the...
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...Probability and Distributions Abstract This paper will discuss the trends and data values and how they relate to statistical terms. Also will describe the probability of different actions to the same group of data. The data will be broke down accordingly to qualitative and quantitative data, and will be grouped and manipulated to show how the data in each group can prove to be useful in the workplace. Memo To: Head of American Intellectual Union From: Abby Price Date: 3/05/2014 Subject: Data analysis from within the union’s surveys Dear Dr. Common: I will be analyzing data given to me which was taken from a survey within the union from 186 employees. I will discuss probability and how its information is important in the workplace. Overview of the Data Set The data group I was given to analyze has 9 categories: gender, age, department, position, tenure, job satisfaction, intrinsic, extrinsic, and benefits. The employees were asked to rate on a scale of 1-7 on how satisfied they were with the company. Gender, age, department, position and tenure are all qualitative data. This data is acknowledged by a code on the given data but cannot measured unlike the quantitative data: job satisfaction, intrinsic, extrinsic, and benefits. Use of Statistics and Probability in the Real World Statistics are just about everywhere in the business world, from the upper management to the lower line of employees, statistics are very useful and are a huge part of our...
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