
Explore predictive modeling fundamentals using Minitab and Excel, covering regression modelling, correlation, descriptive statistics, analysis of variance, and multiple regression, with applications in market research and retail analytics.
Explore nonlinear regression and how multiple regression uses different slopes for each independent variable, assess variable significance, and build regression models with dummy variables.
Explore one-way anova and discriminant analysis within predictive modeling, and learn to generate scatterplots and basic statistics in Minitab through dataset import and graphical summaries.
Explore descriptive statistics in Minitab for mutual fund returns, including means, standard deviations, skewness, kurtosis, and t-tests, with data import, basic statistics, and histograms for interpretation.
Explore how to generate descriptive statistics in Minitab for predictive modeling and time series analysis, interpret standard deviation and variance, and assess graphical summaries and confidence levels to inform decisions.
Explore how standard deviation measures volatility to match investment choices with an investor’s risk appetite, using Minitab for observations and descriptive statistics of asset prices and returns.
Analyze the NAV price results and energy-price data, comparing mean, standard deviation, and range across funds, noting higher volatility in ICICI Prudential Tech Fund and HDFC Equity Fund.
Interpret standard deviation and range to assess price volatility; the data show high volatility for IBF and HDFC Equity, with lower volatility for Excel and HD Cap.
This lecture extends descriptive statistics with finance-focused examples, linking high standard deviation to higher risk, and demonstrates calculating descriptive statistics on daily complaints data, including mean, standard deviation, and skewness.
Analyze descriptive statistics for customer complaints and resting heart rate using Minitab; interpret mean around 19, median 19.5, and skewness before and after rest to assess variation.
We analyze resting heart rate before and after testing using mean, standard deviation, and median, noting little average change but median shift, with emphasis on data quality for predictive modeling.
Explore loan applicant MTW data to reveal income levels, education, age, savings, and debt, with insights on skewness and variability.
The data show high income with notable variability, savings high but dependent on spending, low debt, and most applicants hold at least one credit card (up to six).
Learn how to perform t tests for single and two-sample comparisons in predictive modeling, using a heart rate example in Minitab, and interpret p-values, confidence intervals, and significance levels.
Use Minitab to test whether loan approval depends on income using a t-test with income, savings, and debt. Interpret the p-value to decide if income predicts loans in this sample.
Use the paired t-test in Minitab to test hypotheses about savings, age, and other determinants, interpret t-values and p-values for predictive modeling in linear and logistic regressions.
The objective of this training program is to help trainees to master all the skills that are required to work with Minitab. The training program will help the trainee to perform all the statistical analysis with Minitab. It is also intended to make the trainees cover all the topics that fall under the domain of Minitab. Topics like Minitab GUI and Descriptive Statistics, Statistical Analysis using Minitab, Correlation Techniques in Minitab and Predictive Modeling using Excel will be covered in this training module and Project on Data Analytics using Minitab and Project on Minitab – Regression Modeling will be covered in the project module. The goal of this course is to help an individual to achieve knowledge of working with Minitab to perform time series analysis and forecasting of data in all sorts of statistics based problems. It will help all the interested trainees who are willing to extend their knowledge base concerned with Data analytics. This training will also assist the trainees to understand the sort of problems that could be resolved using this. After the finishing of the course, the trainee will advance with the skills needed for time series analysis and forecasting of data.
To understand the meaning of the “name” of the course, let us break down the curse and understand them word by word so that the intent of the course would be clear to our learners and we would set an expectation for our learners who would be pursuing the course. Let us start to understand what Minitab is. Minitab is a software used in use cases of statistics. Minitab, as the software is capable of data analysis, pattern detection through statistics, data crunching. Most of the calculation is automated, and graphs are generated as per the functionality required by the analyst working on the use cases. For this course specifically, we would be exploiting the use of Minitab for Time Series Analysis and Forecasting.
Now, let us understand what time series analysis is. Time series data is a collection of data over some time or interval. The data when captured over time typically might have some internal structure, for example, autocorrelation, trends, seasonality, stationarity, and many such fundamentals. For example, let us think about the sales of umbrellas or raincoats. We can see that in the selling of these items we might encounter a seasonality aspect, that they might be sold high during the onset of the rainy season. Also, we can see that the data itself might auto-correlate itself to the promos that might have been set by some stores, online or offline, and many more such intricate detailing through the understanding of time series analysis fundamentals.
Now when we know how to analyze the time series it becomes pretty evident that what exactly to do with the studies or hypothesis we developed as a part of the study. The answer lies in prediction! We would understand the fundamentals through historical time series data and then utilize that deep insight to predict or forecast for the future. In this way, we would be able to capture the historical trends and seasonality and utilize them to have a better insight into what’s in store in the future.
This course is a collection of well-crafted tutorials or topics we would naturally go about if we must fully understand the topic of time series analysis and forecasting. This course will allow you to get a sense of what is lying in the basket of time series analysis in the professional project which you would encounter as soon as you take up the project in your organization. This course makes you reach that level of being the “handful” of people who are capable of cracking even the toughest nut in the professional world.
This confidence you build in the course of this training will go a long way in not only analyzing the sense of time series data but also give you the capability to mentor people who would have just started their track on time series analysis. This course doesn’t differentiate between a beginner or an advanced learner. We have to learn for everyone who is here to expand their horizon of knowledge. Lastly, we would like to mention that this course will lay a strong foundation in time series analysis so that you would be able to build a strong mansion on this foundation layer.