...Tell when to use a Pearson’s correlation, the possible research hypotheses for this statistical model, and when correlation can be used to test each type of Research Hypothesis (attributive, associative and causal). 1. A Pearson's correlation is used when you want to find a linear relationship between two variables. It can be used in a causal as well as a associativeresearch hypothesis but it can't be used with a attributive RH because it is univariate. 2. Pearson's correlation should be used only when there is a linear relationship between variables. It can be a positive or negative relationship, as long as it is significant. Correlation is used for testing in Within Groups studies. A possible research hypothesis for this statistical model would be that there is a positive linear relationship between variables. Another possible research hypothesis would be that there is a negative linear relationship. If there is no linear relationship between the variables, then we would retain the null hypothesis. 3. Pearson's correlation should be used when there is a significant effect. (p > .05) When there is a relationship between two variables. There can be a positive or negative correlation. It cannot be used when we retain the null hypothesis because then there is no relationship. It can be used if the null is rejected. 4. A Pearson's correlation is used when two quantitative variables are being tested in the RH. This cannot test attributive RH, but can associative and causal. The...
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...Huang (354902) Renan Lesaffre (355688) Prestige Statistics – ABM Case Report 2013 Table of Contents 1. Introduction ........................................................................................................................................... 3 1.1. 1.2. 1.3. 2. 2.1. 2.2. Causal relationship scheme ........................................................................................................... 4 Quantitative variables ................................................................................................................... 4 Qualitative variables ..................................................................................................................... 5 Dependent/central variable: Price ................................................................................................. 7 Independent variables ................................................................................................................... 7 Univariate analysis ................................................................................................................................ 6 Quantitative variables ........................................................................................................................... 7 Qualitative variables ............................................................................................................................. 8 3. Bivariate analysis ..........................................
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...Chapter 10: Comparing Two Groups Bivariate Analysis: Methods for comparing two groups are special cases of bivariate statistical methods – Two variables exist: Response variable – outcome variable on which comparisons are made Explanatory variable – binary variable that specifies the groups Statistical methods analyze how the outcome on the response variable depends on or is explained by the value of the explanatory variable Independent Samples: Most comparisons of groups use independent samples from the groups, The observations in one sample are independent of those in the other sample Example: Randomized experiments that randomly allocate subjects to two treatments Example: An observational study that separates subjects into groups according to their value for an explanatory variable Dependent samples: Dependent samples result when the data are matched pairs – each subject in one sample is matched with a subject in the other sample Example: set of married couples, the men being in one sample and the women in the other. Example: Each subject is observed at two times, so the two samples have the same subject Categorical response variable: For a categorical response variable - Inferences compare groups in terms of their population proportions in a particular category - We can compare the groups by the difference in their population proportions: (p1 – p2) Example: Experiment: Subjects were 22,071 male physicians Every other day for five years, study participants...
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...companies, government, education researchers, marketing organizations, data miners, and others. The original SPSS manual (Nie, Bent & Hull, 1970) has been described as one of "sociology's most influential books" for allowing ordinary researchers to do their own statistical analysis.[4] In addition to statistical analysis, data management (case selection, file reshaping, creating derived data) and data documentation (a metadata dictionary was stored in the data file) are features of the base software. Statistics included in the base software: * Descriptive statistics: Cross tabulation, Frequencies, Descriptive, Explore, Descriptive Ratio Statistics * Bivariate statistics: Means, t-test, ANOVA, Correlation (bivariate, partial, distances), Nonparametric tests * Prediction for numerical outcomes: Linear regression * Prediction for identifying groups: Factor analysis, cluster analysis (two-step, K-means, hierarchical), Discriminant It is one of the most commonly used statistics software in the world. It is a very powerful tool that has so many uses, among them: * predict...
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...Interpretation of SPSS output for Car Care Inc. Jacquelynn Patterson Liberty University Online Professor Lingley Busi 331-B01 October 7, 2013 1.0 Introduction Statistics in social sciences are an important aspect in making the understanding of social behaviors plausible in organizations, governments, marketers and other cohorts with same interest. Initially, statistical manipulations were conducted manually and obliged researchers to have formulas at their fingertips. This strenuous exercise was susceptible to shortcomings in case large volumes of data were to be analyzed. In addition, manual calculations depend on human nature that is vulnerable to ill health, emotional exhaustion and fatigue. As a result, there are many chances of making errors when dealing with manual calculations. This would finally affect the end results obtained. The above mentioned problems are likely to be amplified especially when dealing with a huge number of research subjects. If this is the case, it implies that marketing research data analysis would be the most vulnerable if manual statistical manipulations were embraced. This is because marketing research depends heavily on many respondents in order for the results to valid and reliable for making inferences to the whole population. Inevitably, marketers are bludgeoned into using statistical software that can handle large volumes of cases in a single command. This does not only reduce...
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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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...How do employment, age, and income influence the possibilities of a greater purchasing power? What contributes to a higher income level? Fahmeda Sultana ID: 4864712 Executive Summary ‘The Creative Life’, a company based on Huizhou, China is a multinational electronic company. The Creative Life (TCL) has been trying to capture the market overseas through merger and acquisitions. This research report is based on the study of how TCL can improvise demographic variables and use the data to extract results on the level of income and their buying power. Considering the scenario that the company is trying to launch a hi-end tablet in India or United States the data is collected. In order to examine the two markets the research problem has been identified: What are the attributes that will lead to higher purchasing power of the consumers for hi-end tablets? Research questions and hypothesis are constructed to find the relationship between age, gender income, type of employment, and level of education. The study involve the used exploratory research design to examine the objectivity, nature and the dependability. Income is used to assess the buying power of the individuals. How can income affect a greater buying power, how the type of employment caters to greater income level and how education can lead to high income is all discussed in details in the report. Questionnaire is completed...
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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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...Chapter 1: Introduction – Defining the Role of Statistics in Business • Statistical Analysis: helps extract information from data and provides an indication of the quality of that information • Data mining: combines statistical methods with computer science & optimization in order to help businesses make the best use of the information contained in large data sets • Probability: helps you understand risky and random events and provides a way of evaluating the likelihood of various potential outcomes 1.1 - Why should you learn statistics? o Advertising. Effective? Which Commercial? Which markets? o Quality control. Defect rate? Cost? Are improvements working? o Finance. Risk – how high? How to control? At what cost. o Accounting. Audit to check financial statements. Is error material? o Other – economic forecasting, measuring and controlling productivity 1.2 – What is statistics? • Statistics: the art and science of collecting and understanding data o A complete and careful statistical analysis will summarize the general facts that apply to everyone and will also alert you to any exceptions. 1.3 – The Five Basic Activities of Statistics 1. Design Phase: will resolve these issues so that useful data will result a. Designing the Study involves planning the details of data gathering. Can avoid the costs & disappointment of find out – too late – that the data collected are not adequate to answer the important questions...
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...Statistics in Business Lula Mae Blake QNT/351 May 4, 2015 Sara Skowronski Statistics in Business According to dictionary definition it is “the science that deals with the collection, classification, analysis, and interpretation of numerical facts or data, and that, by use of mathematical theories of probability, imposes order and regularity on aggregates of more or less disparate elements (2015).” Identify different types and levels of statistics Types and levels of statistics include: Descriptive statistics: numerical and graphical methods that observe patterns in data set to give a summary of the information so it can be presented in an understandable format. Inferential statistics: is sample data that comes up with estimates, decisions, predictions, or other general particulars about a larger set of data. Experimental unit: is a fundamental element – an object (person, thing, transaction, or event). Another fundamental is Population: which is a set of units including people, objects, transactions, or events used in a study. A sample is the subject of the units of a population. Variable: describes the character or property of an individual experimental unit. It is known as univariate data which is a study the looks at only one variable, and bivariate data that deals with the study or relationship of two variables. Measurement: is an important factor of studying variables, as is qualitative and quantitative variables. Describe...
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...v) Bivariate Analysis (up to 300 words) Using appropriate statistical and/or graphical techniques examine how each of your IVs is associated with the DV. Use appropriate tests of statistical significance to see whether it is reasonable to infer these associations to the population represented by the survey. Obtain and consider strength of association statistics and reflect on what these show / mean. Succinctly summarise these two analyses in one paragraph. When looking at this crosstabulation of data from the British Crime Survey regarding the relationship between gender and fear of being raped (see Appendix 1) it seems clear that women are much more worried than men. This is exemplified by the fact that over 90% of women stated that they are worried about being raped compared to the 9.8% that admitted to being worried. In fact, almost 70% of men said they are not worried at all whereas only 30.1% on women claimed to have no concerns. When evaluating the crosstabulation of data from the British Crime Survey regarding the relationship between social class and fear of being raped (see Appendix 2) it seems a lot more evenly spread than that of the one concerning gender. In terms of being the most worried, it seems that people in higher technical occupations have a higher fear of being raped. What is interesting however is that this category also holds the highest number of people who are not worried about being raped. In order to analyse this effectively, it...
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...SPSS Instruction Manual University of Waterloo Department of Statistics and Actuarial Science September 1, 1998 Table Of Contents Page Before Using This Manual……………………………………………………………………………….3 Introduction to SPSS……………………………………………………………………………………..4 SPSS Basics……………………………………………………………………………………………... 5 Tutorial 1: SPSS Windows.…………………………………………………………………………5 Tutorial 2: Starting A SPSS Session.……………………………………………………………...6 Tutorial 3: Getting Help on SPSS.………………………………………………………………… 6 Tutorial 4: Ending A SPSS Session.……………………………………………………………… 6 Creating and Manipulating Data in SPSS.……………………………………………………………. 7 Tutorial 1: Creating a New Data Set.……………………………………………………………... 7 Tutorial 2: Creating a New Data Set From Other File Formats.……………………………….10 Tutorial 3: Opening an Existing SPSS Data Set.………………………………………………. 16 Tutorial 4: Printing a Data Set.…………………………………………………………………… 16 Generating Descriptive Statistics in SPSS…………………………………………………………...17 Tutorial 1: Mean, Sum, Standard Deviation, Variance, Minimum Value, Maximum Value, and Range.……………………………………………………….. 17 Tutorial 2: Correlation.…………………………………………………………………………….. 18 Generating Graphical Statistics in SPSS……………………………………………………………..20 Tutorial 1: How to Generate Scatter Plots.………………………………………………………20 Tutorial 2: How to Generate A Histogram.………………….…………………………………... 22 Tutorial 3: How to Generate A Stem and Leaf Plot……………………………………………..23 Tutorial 4: How to Generate A Box Plot………………………………………………………….26 Statistical Models in SPSS……………………………………………………………………………...
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...SPSS Guide Crosstabs Analyze Descriptive Statistics Crosstabs Choose the a nominal variable from the left and move it to the “Row(s)” box on the right Choose another nominal variable from the left and move it to the “Columns(s)” box on the right Click on the “Statistics” button and check the “Chi-square” box Click on the “Cells” button and check the “Observed”, “Expected” and “Adjusted standardized” boxes Click “OK” Output: Check to see that no more than 20% of cells have an expected count of less than 5. Then check that the Pearson Chi-Square is significant. Lastly look at the “Adjusted Residual” to see if it is greater than 1.96 or less than -1.96. One-Sample T-test Analyze Compare Means One-Sample T-test Choose the scale (interval or ratio) variable on the left to be tested and move it to the right window In the “Test Value” box enter the value you to which you want to compare the mean Click “OK” Output: SPSS gives you a 2-tailed significance value. For a one tailed test you double the significance value. Independent Samples T-test Analyze Compare Means Independent Samples T-test Choose the scale (interval or ratio) variable(s) on the left to be tested and move it to the upper “Test Variable(s)” right window Choose the nominal variable on the left and move it to the “Grouping Variable” box at the bottom right. Click on the “Define Groups” button to tell SPSS which groups to compare. Click “OK” Output: You must examine the Levene’s test to determine...
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...consistency or stability of the measuring device over time (Gay, 1996). A questionnaire is a series of questions ask to the subject to get responsive directly from the subjects. Questionnaires are widely used especially in descriptive survey studies. Surveying collects factual data from participants and gain opinions. The first of two research articles I picked was “A descriptive study of workplace conflict management styles,” The study has a non-experimental, descriptive, and quantitative research design, a sample size of 103 employees working in financial, IT, academic and marketing sectors were analyzed (stratified random sampling was used for the purpose). Statistical measures like Cranach’s alpha, independent samples t test, bivariate correlation, robust tests for equality of means, multiple comparisons (Turkey HSD) and chi square tests were employed to analyze the data. Research revealed that employees of fairer sex in Indian organizations used integrating, compromising and avoiding styles of interpersonal conflict handling. Dominating and obliging styles were followed by males and same was true for all the employees as their age increased. Additionally, employees...
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...General Lectures 1-4 What is statistics? Discipline: collecting, classifying, summarizing, organizing, analyzing and interpreting numerical information. -A means to answer research questions (and to test hypothesis). -No scientific proof: make plausible through empirical data with an empirical observed result. Shape and skewness: Negatively skewed: mean < median Symmetry: mean = median Positively skewed: mean > median Mode = the score that occurs most frequently. Median = middle score when scores are ranked in order. Mean = the average score, the media. Interpreting the standard deviation: -the empirical rule (frequency distribution is bell shaped and symmetric) 68% values: -1 - +1 95% values: -1 - +1 99.7 % values: -3 - +3 -Chebyshev’s rule (applies to all distributions, regardless their shape) ?% values: -1 - +1 75% values: -2 - +2 88.9% values: -3 - +3 o Choice of numerical measures depends on the measurement level of the variable. o Measurement levels are hierarchical and so are the numerical measures. o ‘Higher’ level measures are generally preferred. o Some measures are sensitive to extreme values. Less sensitive measures are preferred when a distribution is too skewed. o Use the Z-score to determine the relative position of an observation. ( Z = X - / ) Central Limit Theorem: When the sample is sufficiently large (n30) the sampling distribution will be approximately a normal distribution. Confidence interval: Probability that...
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