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The ability to understand and apply Business Statistics is becoming increasingly important in the industry. A good understanding of Business Statistics is a requirement to make correct and relevant interpretations of data. Lack of knowledge could lead to erroneous decisions which could potentially have negative consequences for a firm. This course is designed to introduce you to Business Statistics. We begin with the notion of descriptive statistics, which is summarizing data using a few numbers. Different categories of descriptive measures are introduced and discussed along with the Excel functions to calculate them. The notion of probability or uncertainty is introduced along with the concept of a sample and population data using relevant business examples. This leads us to various statistical distributions along with their Excel functions which are then used to model or approximate business processes. You get to apply these descriptive measures of data and various statistical distributions using easy-to-follow Excel based examples which are demonstrated throughout the course.

To successfully complete course assignments, students must have access to Microsoft Excel. ________________________________________ WEEK 1 Module 1: Basic Data Descriptors In this module you will get to understand, calculate and interpret various descriptive or summary measures of data. These descriptive measures summarize and present data using a few numbers. Appropriate Excel functions to do these calculations are introduced and demonstrated. Topics covered include: 鈥� Categories of descriptive data 鈥� Measures of central tendency, the mean, median, mode, and their interpretations and calculations 鈥� Measures of spread-in-data, the range, interquartile-range, standard deviation and variance 鈥� Box plots 鈥� Interpreting the standard deviation measure using the rule-of-thumb and Chebyshev鈥檚 theorem ________________________________________ WEEK 2 Module 2: Descriptive Measures of Association, Probability, and Statistical Distributions This module presents the covariance and correlation measures and their respective Excel functions. You get to understand the notion of causation versus correlation. The module then introduces the notion of probability and random variables and starts introducing statistical distributions. Topics covered include: 鈥� Measures of association, the covariance and correlation measures; causation versus correlation 鈥� Probability and random variables; discrete versus continuous data 鈥� Introduction to statistical distributions ________________________________________ WEEK 3 Module 3: The Normal Distribution This module introduces the Normal distribution and the Excel function to calculate probabilities and various outcomes from the distribution. Topics covered include: 鈥� Probability density function and area under the curve as a measure of probability 鈥� The Normal distribution (bell curve), NORM.DIST, NORM.INV functions in Excel ________________________________________ WEEK 4 Module 4: Working with Distributions, Normal, Binomial, Poisson In this module, you'll see various applications of the Normal distribution. You will also get introduced to the Binomial and Poisson distributions. The Central Limit Theorem is introduced and explained in the context of understanding sample data versus population data and the link between the two. Topics covered include: 鈥� Various applications of the Normal distribution 鈥� The Binomial and Poisson distributions 鈥� Sample versus population data; the Central Limit Theorem

Confidence intervals and Hypothesis tests are very important tools in the Business Statistics toolbox. A mastery over these topics will help enhance your business decision making and allow you to understand and measure the extent of 鈥榬isk鈥� or 鈥榰ncertainty鈥� in various business processes.

This is the third course in the specialization "Business Statistics and Analysis" and the course advances your knowledge about Business Statistics by introducing you to Confidence Intervals and Hypothesis Testing. We first conceptually understand these tools and their business application. We then introduce various calculations to constructing confidence intervals and to conduct different kinds of Hypothesis Tests. These are done by easy to understand applications. To successfully complete course assignments, students must have access to a Windows version of Microsoft Excel 2010 or later. Please note that earlier versions of Microsoft Excel (2007 and earlier) will not be compatible to some Excel functions covered in this course. WEEK 1 Module 1: Confidence Interval - Introduction In this module you will get to conceptually understand what a confidence interval is and how is its constructed. We will introduce the various building blocks for the confidence interval such as the t-distribution, the t-statistic, the z-statistic and their various excel formulas. We will then use these building blocks to construct confidence intervals. Topics covered include: 鈥� Introducing the t-distribution, the T.DIST and T.INV excel functions 鈥� Conceptual understanding of a Confidence Interval 鈥� The z-statistic and the t-statistic 鈥� Constructing a Confidence Interval using z-statistic and t-statistic WEEK 2 Module 2: Confidence Interval - Applications This module presents various business applications of the confidence interval including an application where we use the confidence interval to calculate an appropriate sample size. We also introduce with an application, the confidence interval for a population proportion. Towards the close of module we start introducing the concept of Hypothesis Testing. Topics covered include: 鈥� Applications of Confidence Interval 鈥� Confidence Interval for a Population Proportion 鈥� Sample Size Calculation 鈥� Hypothesis Testing, An Introduction WEEK 3 Module 3: Hypothesis Testing This module introduces Hypothesis Testing. You get to understand the logic behind hypothesis tests. The four steps for conducting a hypothesis test are introduced and you get to apply them for hypothesis tests for a population mean as well as population proportion. You will understand the difference between single tail hypothesis tests and two tail hypothesis tests and also the Type I and Type II errors associated with hypothesis tests and ways to reduce such errors. Topics covered include: 鈥� The Logic of Hypothesis Testing 鈥� The Four Steps for conducting a Hypothesis Test 鈥� Single Tail and Two Tail Hypothesis Tests 鈥� Guidelines, Formulas and an Application of Hypothesis Test 鈥� Hypothesis Test for a Population Proportion 鈥� Type I and Type II Errors in a Hypothesis WEEK 4 Module 4: Hypothesis Test - Differences in Mean In this module, you'll apply Hypothesis Tests to test the difference between two different data, such hypothesis tests are called difference in means tests. We will introduce the three kinds of difference in means test and apply them to various business applications. We will also introduce the Excel dialog box to conduct such hypothesis tests. Topics covered include: 鈥� Introducing the Difference-In-Means Hypothesis Test 鈥� Applications of the Difference-In-Means Hypothesis Test 鈥� The Equal & Unequal Variance Assumption and the Paired t-test for difference in means. 鈥� Some more applications

Regression Analysis is perhaps the single most important Business Statistics tool used in the industry. Regression is the engine behind a multitude of data analytics applications used for many forms of forecasting and prediction.

This is the fourth course in the specialization, "Business Statistics and Analysis". The course introduces you to the very important tool known as Linear Regression. You will learn to apply various procedures such as dummy variable regressions, transforming variables, and interaction effects. All these are introduced and explained using easy to understand examples in Microsoft Excel. The focus of the course is on understanding and application, rather than detailed mathematical derivations. Note: This course uses the 鈥楧ata Analysis鈥� tool box which is standard with the Windows version of Microsoft Excel. It is also standard with the 2016 or later Mac version of Excel. However, it is not standard with earlier versions of Excel for Mac. WEEK 1 Module 1: Regression Analysis: An Introduction In this module you will get introduced to the Linear Regression Model. We will build a regression model and estimate it using Excel. We will use the estimated model to infer relationships between various variables and use the model to make predictions. The module also introduces the notion of errors, residuals and R-square in a regression model. Topics covered include: 鈥� Introducing the Linear Regression 鈥� Building a Regression Model and estimating it using Excel 鈥� Making inferences using the estimated model 鈥� Using the Regression model to make predictions 鈥� Errors, Residuals and R-square WEEK 2 Module 2: Regression Analysis: Hypothesis Testing and Goodness of Fit This module presents different hypothesis tests you could do using the Regression output. These tests are an important part of inference and the module introduces them using Excel based examples. The p-values are introduced along with goodness of fit measures R-square and the adjusted R-square. Towards the end of module we introduce the 鈥楧ummy variable regression鈥� which is used to incorporate categorical variables in a regression. Topics covered include: 鈥� Hypothesis testing in a Linear Regression 鈥� 鈥楪oodness of Fit鈥� measures (R-square, adjusted R-square) 鈥� Dummy variable Regression (using Categorical variables in a Regression) WEEK 3 Module 3: Regression Analysis: Dummy Variables, Multicollinearity This module continues with the application of Dummy variable Regression. You get to understand the interpretation of Regression output in the presence of categorical variables. Examples are worked out to re-inforce various concepts introduced. The module also explains what is Multicollinearity and how to deal with it. Topics covered include: 鈥� Dummy variable Regression (using Categorical variables in a Regression) 鈥� Interpretation of coefficients and p-values in the presence of Dummy variables 鈥� Multicollinearity in Regression Models WEEK 4 Module 4: Regression Analysis: Various Extensions The module extends your understanding of the Linear Regression, introducing techniques such as mean-centering of variables and building confidence bounds for predictions using the Regression model. A powerful regression extension known as 鈥業nteraction variables鈥� is introduced and explained using examples. We also study the transformation of variables in a regression and in that context introduce the log-log and the semi-log regression models. Topics covered include: 鈥� Mean centering of variables in a Regression model 鈥� Building confidence bounds for predictions using a Regression model 鈥� Interaction effects in a Regression 鈥� Transformation of variables 鈥� The log-log and semi-log regression models

The Business Statistics and Analysis Capstone is an opportunity to apply various skills developed across the four courses in the specialization to a real life data. The Capstone, in collaboration with an industry partner uses publicly available 鈥楬ousing Data鈥� to pose various questions typically a client would pose to a data analyst.

Your job is to do the relevant statistical analysis and report your findings in response to the questions in a way that anyone can understand. Please remember that this is a Capstone, and has a degree of difficulty/ambiguity higher than the previous four courses. The aim being to mimic a real life application as close as possible.