Contents
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Business Statistics
Business statistics is the science of good decision making in the face of uncertainty and is used in many disciplines such as financial analysis, econometrics, auditing, production and operations including services improvement, and marketing research. These sources feature regular repetitive publication of series of data. This makes the topic of time series especially important for business statistics. It is also a branch of applied statistics working mostly on data collected as a by-product of doing business or by government agencies. It provides knowledge and skills to interpret and use statistical techniques in a variety of business applications. A typical business statistics course is intended for business majors, and covers statistical study, descriptive statistics (collection, description, analysis, and summary of data), probability, and the binomial and normal distributions, test of hypotheses and confidence intervals, linear regression, and correlation.
Learning Objectives and Outcomes
This is a non-taught unit designed for self-directed study by those intending to enhance their professional or managerial competence, knowledge, understanding, and skills in business finance.
Knowledge
After completing the course, student will understand
Skills
After completing the course, student will be able to
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Teaching and Learning Resources
Statistics, Data, and Statistical Thinking
1: Statistics, Data, and Statistical Thinking
In this Session you will cover the following topics:
1.1 The Science of Statistics
1.2 Types of Statistical Applications in Business
1.3 Fundamental Elements of Statistics
1.4 Processes (Optional)
1.5 Types of Data
1.6 Collecting Data
1.7 The Role of Statistics in Managerial Decision-Making
Welcome to the collaboratory for Statistical Literacy!
“Statistical thinking will one day be as necessary for efficient citizenship as the ability to read and write”
~ HG Wells (1866-1946)
Existentially! We are ‘thrown ‘into uncertainty. Yet we must necessarily make choices by continously reducing the margin of error in our decisions and actions.
There are several reasons why I believe that this blog would be of interest to you:
Developments in the field of statistical pedagogy have come to recognize a separate domain of statistical thinking, besides the traditional notion of statistical methods. This revolution can be summarized as follows:a. The field of exploratory data analysis that gives priority to visual tools to gain insight from data.b. The availability and use of technology that has made statistical methods more accessible to all. This has made possible a preponement in the exposure to advanced techniques like regression, but not without the risk of mindless misapplication and disregard for quality of data.
The development of simpler statistical methods in statistical process control has further democratized the use of statistics in real time practice. This has implications for medicine which has traditionally seen statistics purely as a tool in medical research.
The fields of cognitive and development psychology have contributed greatly to the understanding of how we make sense of data. Though they do not always agree! Statistical thinking in a broader sense is also a favorite contender for contemporary models of general reasoning in cognitive science.
These developments offer the greatest advantages to introductory courses in statistics and yet have has been very little impact in the teaching of statistics in medical and healthcare institutions. Further more, the application of statistics in clinical practice is still not as widespread as it should be.The aim of this blog is the advocacy of statistical thinking in medicine and healthcare.
I have been a keen student of these developments for quite some time now and I wish to share this ‘basic’ knowledge with medical and other healthcare students and practitioners. The position I assume in this exercise is as a fellow student and I hope professionals in statistics, psychology and informatics would be willing to contribute to the discussion, as well as correct any errors in my understanding.
The strength I hopefully bring to this blog is a broad based interest in the psychology, history and philosophy of statistics, of science, of information and the conviction that statistical thinking is indispensable to all parts of medical and healthcare science and practice. I hope to pull together as much resources that would be invigorating to any one beginning their journey of self- learning in statistical thinking. To travel along all you need is a little enthusiasm and an open mind.….
2: Methods for Describing Sets of Data
In this Session you will cover the following topics:
2.1 Describing Qualitative Data
2.2 Graphical Methods for Describing Quantitative Data
2.3 Summation Notation
2.4 Numerical Measures of Central Tendency
2.5 Numerical Measures of Variability
2.6 Interpreting the Standard Deviation
2.7 Numerical Measures of Relative Standing
2.8 Methods for Detecting Outliers (Optional)
2.9 Graphing Bivariate Relationships (Optional)
2.10 The Time Series Plot (Optional)
2.11 Distorting the Truth with Descriptive Techniques
In this Session you will cover the following topics:
3.1 Events, Sample Spaces, and Probability
3.2 Unions and Intersections
3.3 Complementary Events
3.4 The Additive Rule and Mutually Exclusive Events
3.5 Conditional Probability
3.6 The Multiplicative Rule and Independent Events
3.7 Random Sampling
4: Random Variables and Probability Distributions
In this Session you will cover the following topics:
4.1 Two Types of Random Variables
4.2 Probability Distributions for Discrete Random Variables
4.3 The Binomial Distribution
4.4 The Poisson Distribution (Optional)
4.5 Probability Distributions for Continuous Random Variables
4.6 The Uniform Distribution (Optional)
4.7 The Normal Distribution
4.8 Descriptive Methods for Assessing Normality
4.9 Approximating a Binomial Distribution with a Normal Distribution
4.10 The Exponential Distribution (Optional)
4.11 Sampling Distributions
4.12 The Central Limit Theorem
5: Inferences Based on a Single Sample: Estimation with Confidence Intervals
In this Session you will cover the following topics:
5.1 Large-Sample Confidence Interval for a Population Mean
5.2 Small-Sample Confidence Interval for a Population Mean
5.3 Large-Sample Confidence Interval for a Population Proportion
5.4 Determining the Sample Size
6: Inferences Based on a Single Sample: Tests of Hypothesis
In this Session you will cover the following topics:
6.1 The Elements of a Test of Hypothesis
6.2 Large-Sample Test of Hypothesis About a Population Mean
6.3 Observed Significance Levels: p-Values
6.4 Small-Sample Test of Hypothesis About a Population Mean
6.5 Large-Sample Test of Hypothesis About a Population Proportion
6.6 A Nonparametric Test About a Population Median (Optional)
7: Inferences Based on a Two Samples: Confidence Intervals and Tests of Hypotheses
In this Session you will cover the following topics:
7.1 Comparing Two Population Means: Independent Sampling
7.2 Comparing Two Population Means: Paired Difference Experiments
7.3 Determining the Sample Size
7.4 Testing the Assumption of Equal Proportion Variances (Optional)
7.5 A Nonparametric Test for Comparing Two Populations: Independent
Sampling (Optional)
7.6 A Nonparametric Test for Comparing Two Populations: Paired
Difference Experiment (Optional)
7.7 Comparing Three or More Population Means: Analysis of
Variance (Optional)
8: Comparing Population Proportions
In this Session you will cover the following topics:
8.1 Comparing Two Population Proportions: Independent Sampling
8.2 Determining the Sample Size
8.3 Comparing Population Proportions: Multinomial Experiment
8.4 Contingency Table Analysis
In this Session you will cover the following topics:
9.1 Probabilistic Models
9.2 Fitting the Model: The Least Squares Approach
9.3 Model Assumptions
9.4 An Estimator of s2
9.5 Assessing the Utility of the Model: Making Inferences
About the Slope ß1
9.6 The Coefficient of Correlation
9.7 The Coefficient of Determination
9.8 Using the Model for Estimation and Prediction
9.9 Simple Linear Regression: A Complete Example
9.10 A Nonparametric Test for Correlation (Optional)
10: Introduction to Multiple Regression
In this Session you will cover the following topics:
10.1 Multiple Regression Models
10.2 The First-Order Model: Estimating and Interpreting the
ß-Parameters
10.3 Model Assumptions
10.4 Inferences About the ß Parameters
10.5 Checking the Overall Utility of a Model
10.6 Using the Model for Estimation and Prediction
10.7 Residual Analysis: Checking the Regression Assumptions
10.8 Some Pitfalls: Estimability, Multicollinearity, and Extrapolation
11: Methods for Quality Improvement
In this Session you will cover the following topics:
11.1 Quality, Processes, and Systems
11.2 Statistical Control
11.3 The Logic of Control Charts
11.4 A Control Chart for Monitoring the Mean of a Process:
The x-Chart
11.5 A Control Chart for Monitoring the Variation of a Process:
The R-Chart
11.6 A Control Chart for Monitoring the Proportion of Defectives
Generated by a Process: The p-Chart
Recommended Text
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A
First Course In Business Statistics Eighth Edition by Check the availability and buy your books from our Bookshop. |
Resources
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