Business Statistics - Ch 1 - An Introduction to Business Statistics
Business Statistics - Ch 1 - An Introduction to Business Statistics 2283
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This 4 page Class Notes was uploaded by Michaela Francisco on Saturday January 16, 2016. The Class Notes belongs to 2283 at East Carolina University taught by Nathaniel Barreiro-Talbert in Spring 2016. Since its upload, it has received 55 views. For similar materials see Statistics for Business in Math at East Carolina University.
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Date Created: 01/16/16
Course: Business Statistics Date: 1/16/2016 Chapter 1: An Introduction to Business Statistics 1.1 Business Statistics and Their Uses Statistics: A mathematical science that deals with the collection, analysis, and presentation of data Business Statistics: Statistics that are applied to the business world in an effort to improve people’s decision making in areas like marketing and finance Business statistics are used in marketing research, advertising, operations, and finance. Marketing research relies on statistics because the statistics of a consumer test would tell the company what its consumers want. Ex: Having consumers give their ratings on 2 different products to see which product the consumers prefer Advertising companies will survey and collect data on habits of consumers in a particular area. This data will show what the consumer wants by collecting data on what the consumer views already. If the company knows the interests of a consumer, the company can better predict what products the viewers will be interested in, in the future. Ex: Television viewing habits in households can be collected and viewed by a company. The operations of a company can be improved by taking data from current products. Taking data from current products can help find ways of improving the product and this practice will help the quality control of the business. There are always ways to improve a product. Ex: Taste testing a food product and recording data such as pros and cons. The finance industry uses statistics by collecting data from customers to see if they are a good credit risk or not. This can increase the quality of the finance company’s lending practices. It shows how to best use their assets when it comes to their customers. 1.2 Data Data: values assigned to observations or measurements and are the building blocks of statistical analysis Information: data that is transformed into useful facts that can be used for specific purposes You can collect data in many ways such as recording frequency, duration, or writing observations. The difference between data and information is that data is the actual analysis and the information is derived facts from interpreting data. Say you have a table with two columns Time(seconds) Frequency 1 0 10 5 20 10 Data Point: Every one frequency is a data point 0, 5, and 10 are all data points If you are looking at the data points and you see a considerable change in the pattern between one and the next one, you could analyze what happened there. Primary Data: data that you have collected for your own use Ex: Observing and comparing 2 animals behaviors and interactions with each other Advantage: The data is your data and you have control over how it is collected Drawback: Can be expensive to collect the information Secondary Data: data collected by someone else that you are ‘borrowing’ Ex: data collected on animal populations Advantage: Cheap if not free access to the information and immediately available Drawback: You have no control over how the data was collected and could be biased Ways of Collection Primary Data Direct Observation: method of gathering data while the subjects of interest are in their natural environment, often unaware of being watched Focus Group: direct observational technique whereby individuals are often paid to discuss their attitudes toward products or services in a group setting controlled by a moderator Experiment: subjects are exposed to certain treatments and the data of interest are recorded Survey: involves directly asking people a series of questions and can be administered by e-mail, via the Web, through snail mail, face to face, or over the telephone Bias: opinion, can occur when something you think or believe affects how you ask a question or respond to one. Bias is dangerous in statistics and give you skewed information. It can occur intentionally or unintentionally. If the bias is intentional then someone could be manipulating the results of the study. Bias can also occur if the wrong population is studied. Ex: If you want to survey the entire ECU University, the population of the study needs to be widespread. There needs to be participants in all parts of the university and not just one. Quantitative Data: numerical values (measured or counted) Interval: (no true zero point) measuring differences between categories with numbers Ex: measurements on a ruler, 0 degrees Fahrenheit, a 0.0 GPA (not true zeros) Ratio: (true zero point) same as interval data, but has a true zero point Ex: absence of money, age Qualitative Data: relies on descriptive terms about something of interest (written description) Nominal: Data that is described as a category or a label Ex: gender Ordinal: same as nominal data, but can be ranked from highest to lowest Ex: in what order did people finish a street race Qualitative Data = Categorical Data The lowest level of data is nominal data. Time Series Data: values that correspond to specific measurements taken over a range of time periods Cross-Sectional Data: values collected from a number of entities during a single time period 1.3 Descriptive and Inferential Statistics Descriptive Statistics: to summarize, display, data so that we can quickly get an overview of information You see a multitude of descriptive statistics in everyday life. Ex: A graph displaying stress levels of college students. Downfall: you lose detailed information and are mostly looking at averages Inferential Statistics: allows us to make claims or conclusions about the data based on a sample of them Population: all possible subjects of interest (population can be any size) Sample: subset of a population (a set that is found within the original population) Ex: Say we have a 12 pack of soda and we measure how many ounces of soda are in each can. Then find the average of all the sodas. We find that the average amount of soda in a can is 11.9 ounces. With inferential statistics we can conclude that actual target ounces of each can is 12 ounces. Parameters: Data that describe a characteristic about a population Statistics: Data that describe a characteristic about a sample 1.4 Ethics and Statistics Biased Sample: A sample that does not represent the intended population and that can lead to distorted findings. Biased samples can happen intentionally or unintentionally. Ex: A lot of companies will try and get you to fill out surveys to see how your experience was with their business. If you actually fill out the survey, a lot of times the questions are skewed to where it is hard to even tell them you had a bad experience. That is a biased survey. Ex: If you are using graphical data, the way you represent your data can make the data look biased. If your graph is not representing detailed data, information and details are lost. Remember: If you mess up in statistics a few times, it is okay. Do not feel bad. Even large corporations can misuse statistics.
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