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Business Statistics: Overview of Descriptive Stats, Probability, Inference, and Regression, Study notes of Business Statistics

An overview of a statistics textbook, focusing on the study of data analysis using microsoft excel and stattools add-in. Descriptive statistics, probability, statistical inference, and linear regression. Descriptive statistics help make sense of large data sets through graphical and tabular summaries and numerical measures. Probability theory deals with uncertainty in business problems, and the normal and binomial distributions are discussed. Statistical inference focuses on estimating population characteristics through random sampling and analysis of sample data. Linear regression is used to study relationships between variables in various business contexts.

Typology: Study notes

2010/2011

Uploaded on 06/29/2011

mchang813
mchang813 🇺🇸

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Ch 1. Overview
Please read this NY Times article.
In a nutshell, statistics is the study of data analysis.
Software used
Microsoft Excel and StatTools Add-in
Chapters 2 & 3: Descriptive statistics
"Make sense of data"
Organizations are now able to collect huge amounts of raw data. The question then becomes,
what does it all mean?
ographical and tabular summaries
onumerical summary measures such as means, medians, and standard deviations
The material in these two chapters is elementary from a mathematical point of view, but it is extremely
important.
Chapters 4 & 5: Probability
Uncertainty is a key aspect of most business problems. To deal with uncertainty, we need a basic
understanding of probability.
Chapter 4 covers basic rules of probability and then discusses the extremely important concept of
probability distributions.
Chapter 5 follows up this discussion by focusing on two of the most important probability
distributions: the normal and binomial distributions.
Chapters 7, 8 & 9: Statistical inference
Here the basic problem is to estimate one or more characteristics of a population.
It is usually too expensive or time consuming to learn about the entire population
oTherefore, we select a random sample from the population. (Chapter 7)
pf2

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Ch 1. Overview

Please read this NY Times article. In a nutshell, statistics is the study of data analysis.

Software used

Microsoft Excel and StatTools Add-in

Chapters 2 & 3: Descriptive statistics

"Make sense of data"  Organizations are now able to collect huge amounts of raw data. The question then becomes, what does it all mean? o graphical and tabular summaries o numerical summary measures such as means, medians, and standard deviations The material in these two chapters is elementary from a mathematical point of view, but it is extremely important.

Chapters 4 & 5: Probability

Uncertainty is a key aspect of most business problems. To deal with uncertainty, we need a basic understanding of probability.  Chapter 4 covers basic rules of probability and then discusses the extremely important concept of probability distributions.  Chapter 5 follows up this discussion by focusing on two of the most important probability distributions: the normal and binomial distributions.

Chapters 7, 8 & 9: Statistical inference

Here the basic problem is to estimate one or more characteristics of a population.  It is usually too expensive or time consuming to learn about the entire population o Therefore, we select a random sample from the population. (Chapter 7)

o Then use the information in the sample to infer the characteristics of the population. (Chapter 8 & 9)  We see this continually on news shows that describe the results of various polls. o For example, the percentage of the people who support Mr. Obama or Mr. McCain  We also see it in many business contexts. o For example, auditors typically sample only a fraction of a company’s records.Then they infer the characteristics of the entire population of records from the results of the sample to conclude whether the company has been following acceptable accounting standards.

Chapters 10 & 11: Linear regression

Study of relationships between variables. Every part of a business has variables that are related to one another, and regression can often be used to estimate possible relationships between these variables. Many uses of (linear) regression analysis in business, for example:  In managerial accounting, regression is used to estimate how overhead costs depend on direct labor hours and production volume.  In marketing, regression is used to estimate how sales volume depends on advertising and other marketing variables.  In finance, regression is used to estimate how the return of a stock depends on the “market” return.  In real estate studies, regression is used to estimate how the selling price of a house depends on the assessed valuation of the house and characteristics such as the number of bedrooms and square footage