Managerial decision-making is complicated, especially given the fast-paced and ever-changing business landscape. This necessitates that management graduates possess the skills to effectively navigate real-world challenges and employ robust analytical thinking. Such proficiency entails processing data, recognizing assumptions, addressing biases, and confidently making managerial judgments. This course will delve into utilization of MS Excel, demonstrating its applications through real-world case studies. Covering fundamental topics such as descriptive statistics, probability, sampling techniques, hypothesis testing, ANOVA, and basics of regression, the course aims to empower individuals to make informed, data-driven decisions and effectively communicate their findings. We begin the journey as we delve into the essentials of statistics, data types, and scales. You'll learn how to compute and interpret statistical measures in Descriptive Statistics and understand fundamental Probability laws, Bayes' theorem, and business applications.

This week, we continue with the fundamentals of probability, we introduce the concepts of random variables and probability distribution - namely - discrete and continuous. The focus for this course will be on Normal distribution. You will also learn the different types of Samples - Sample selection process and estimation of population mean using sample statistics. Further, you will get an understanding of how Sampling distribution. provides valuable insights into the variability that can be expected by repeatedly drawing samples from the same population.

The Business Statistics module concludes with a discussion on the margin of error and its relation to sample estimates. You'll learn to calculate interval estimates and apply them in business contexts. Additionally, this week we will cover hypothesis testing, which includes formulating null and alternate hypotheses and conducting significance tests to determine criteria for hypothesis rejection. We will further explore the implications of type I and type II errors in this decision-making.

This week we explore statistical inferences involving multiple populations, with a particular emphasis on ANOVA as a method for comparing means among multiple groups or populations. You'll understand ANOVA's significance in assessing group differences and gain insights into interpreting the produced F-statistic. ANOVA facilitates identifying significant differences between group means and comprehending sources of data variation.

This week we introduce Regression Analysis as we delve into the relationship between dependent and independent variables. You'll discover how Regression analysis estimates the dependent variable values based on known independent variables, aiding in identifying the best-fit line or curve representing data patterns and trends. This analysis facilitates uncovering insights crucial for informed decision-making across various fields.

The course wraps up with a unique twist: testing the association of two variables in a contingency table; testing if a variable is likely to come from a particular distribution. We will delve into concepts such as contingency tables, column and row percentages, observed and expected frequencies, and apply Chi-square statistics to test population proportion equality. Additionally, we will learn how to use Chi-square statistics to test the independence of two categorical variables and assess goodness of fit to determine if an observed dataset aligns with a specific distribution.