Applied business statistics: methods and Excel-based by Trevor Wegner · Applied business statistics: methods and Excel-based applications. by Trevor. Applied business statistics: Methods and Excel-based applications. This text aims to differentiate itself from other business statistics texts in two important ways. This further edition continues the theme of using Excel as a computational tool to perform statistical analysis. Applied business statistics: methods and Excel-based applications: solutions manual / Trevor Wegner. Bookmark: rattribillvordo.tk
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10 records Download as PDF, TXT or read online from Scribd. Flag for TREVOR WEGNER . Applied Business rattribillvordo.tk 3 12/18/ AM. Applied. About the Author. Trevor Wegner is a statistical consultant in business analytics. He facilitates statistical training programmes for managers, and consults in the. Applied Business Statistics: Methods and Excel-Based Applications 2nd Edition Trevor Wegner is a statistical consultant and a former lecturer of marketing research and Get your Kindle here, or download a FREE Kindle Reading App.
The incorrect choice of statistical method for a given data type can again produce invalid statistical findings. Whether your application is business, how-to, education, medicine, school, church, sales, marketing, online training or just for fun, PowerShow. And, best of all, most of its cool features are free and easy to use. You can use PowerShow.
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You can choose whether to allow people to download your original PowerPoint presentations and photo slideshows for a fee or free or not at all. Check out PowerShow. For example, the following two statistical conclusions could be drawn for all employees. In addition, there are many other off-the-shelf software packages for business use on laptops. Some work as Excel add-ins. A search of the internet will identify many other statistical packages and list their capabilities.
All offer the techniques of descriptive statistics, inferential analysis and statistical modelling covered in this text. A few examples follow for illustrative purposes.
Finance Stock market analysts use statistical methods to predict share price movements; financial analysts use statistical findings to guide their investment decisions in bonds, cash, equities, property, etc. At a company level, statistics is used to assess the viability of different investment projects, to project cash flows and to analyse patterns of payment by debtors.
Marketing Marketing research uses statistical methods to sample and analyse a wide range of consumer behaviour and purchasing patterns. Market segmentation studies use statistical techniques Applied Business Statistics. Statistics is used to analyse human resources issues, such as training effectiveness, patterns of absenteeism and employee turnover, compensation planning and manpower planning.
Surveys of employee attitudes to employment issues use similar statistical methods to those in market research.
In the area of production planning, managers use statistical forecasts of future demand to determine machine and labour utilisation over the planning period.
It enables a user i to assess data quality and ii to select the most appropriate statistical method to apply to the data. Both factors affect the validity and reliability of statistical findings.
Data Quality Data is the raw material of statistical analysis.
If the quality of data is poor, the quality of information derived from statistical analysis of this data will also be poor. Consequently, user confidence in the statistical findings will be low.
A useful acronym to keep in mind is GIGO, which stands for garbage in, garbage out. It is therefore necessary to understand what influences the quality of data needed to produce meaningful and reliable statistical results. Data quality is influenced by four factors: the data type, data source, the method of data collection and appropriate data preparation. Selection of Statistical Method The choice of the most appropriate statistical method to use depends firstly on the management problem to be addressed and secondly on the type of data available.
Certain statistical methods are valid for certain data types only. The incorrect choice of statistical method for a given data type can again produce invalid statistical findings.
A random variable is either qualitative categorical or quantitative numeric in nature.
Applied Business Statistics. The data is represented by categories only. The following are examples of qualitative random variables with categories as data: The gender of a consumer is either male or female.
An employees highest qualification is either a matric, a diploma or a degree. A company operates in either the financial, retail, mining or industrial sector.
Numbers are often assigned to represent the categories e. Such categorical data can therefore only be counted to determine how many responses belong to each category. Quantitative random variables generate numeric response data. These are real numbers that can be manipulated using arithmetic operations add, subtract, multiply and divide. The following are examples of quantitative random variables with real numbers as data: the age of an employee e.
Numeric data can be further classified as either discrete or continuous.