MMPC 005 - Quantitative Analysis for Managerial Applications | Rapid Roundup | All Units

An overview of quantitative analysis, discussing the use of mathematical and statistical techniques to make decisions based on data. The importance of understanding and interpreting data in various fields is highlighted.

Covering the basics of data collection and analysis in quantitative methods. Exploring topics related to probability distributions and decision theory.

Explaining the concepts of primary and secondary data, focusing on the significance of questionnaires and observation methods in data collection.

Discussing the importance of measures of central tendency such as mean, median, and mode in summarizing numerical data and making informed decisions.

Exploring measures of variation like range, standard deviation, and coefficient of variation, to understand the spread and reliability of data sets.

Introducing probability theory and its application in quantifying uncertainty, assessing likelihoods, and making informed decisions based on sample spaces and events.

Exploring decision theory and its methods for structuring complex choices, including decision trees, marginal analysis, and preference theory for informed decision-making under uncertainty.

Discussing various sampling methods like simple random sampling, systematic sampling, and cluster sampling for measuring characteristics of large groups effectively and efficiently.

Exploration of probability and non-probability sampling, including stratified and multi-stage sampling, sample size considerations, sampling errors, and potential biases in sampling.

Discussion on sampling distributions, hypothesis testing, population comparisons, probability distribution, sample mean, central limit theorem, and sampling variance.

Explanation of null hypothesis, alternate hypothesis, test statistics, critical values, rejection regions, P-values, population mean testing, and population proportion testing.

Overview of forecasting techniques such as moving averages and exponential smoothing, control charts, importance of forecasting in decision-making, and forecasting applications in business.

Explanation of correlation coefficients, interpreting R values, correlation significance, correlation in real-world applications, and understanding auto-correlation.

Introduction to regression analysis, model building, estimating parameters, error analysis, confidence intervals, forecasting values, and significance testing in regression models.

Explanation of time series components (trend, seasonality, cycle, randomness), autocorrelations, autoaggressive and moving average models, arima model, and forecast method selection.