Predictive Modeling| Statistical Analysis: Minitab and Excel

Compute column statistics in Minitab and Excel by selecting input variables such as open price data, and generate descriptive statistics and plots.

Navigate Minitab and Excel for predictive modeling, using the help and assistant to access descriptive statistics, regression, and graphical analyses like scatter plots and time series.

Apply descriptive statistics—mean, standard deviation, and t tests—using Minitab 19 on airlines shares data; explore histograms, skewness, and kurtosis for close price and delivery versus traded quantity.

Compute descriptive statistics for financial data in Minitab, focusing on average price, total traded quantity, and turnover, with median and skewness insights.

Analyze Infosys data with descriptive statistics on high-low and close-open spreads, including mean, standard deviation, skewness, and kurtosis, plus weighted average price and delivery-to-traded quantity.

Learn to perform chi-square and g-square goodness-of-fit tests in Minitab, using price and trade data to compare observed and expected counts in categorical analysis.

Perform chi-square tests in Minitab to assess open versus close prices, report Pearson chi-square and likelihood ratio values, and review descriptive statistics for turnover and delivered quantity.

Learn to run a one-way anova in Minitab to compare open price and close price means, interpret p-values and 95% confidence intervals under equal variances.

Conduct a one-way analysis of variance in Minitab and Excel to test whether group means differ, interpret the f-statistic and p-value, and review confidence intervals and r-squared results.

Correlations part 1: explore how to compute and interpret correlations using Minitab 19, including Pearson and Spearman methods, matrix plots, and data preparation for linear relationships.

Analyze correlations between Reliance and Infosys across turnover, delivered quantity, and traded quantity to assess diversification and portfolio risk, using correlation and covariance in a predictive modeling context.

Explore correlations within same sectors, examining delivered quantity to traded quantity and turnover, and assess high-low spreads for intraday insights and sector diversification.

Learn regression modeling in predictive modeling, starting with simple linear regression. Explore independent and dependent variables and the equation y = mx + c in Minitab and Excel.

Master regression analysis by building a fit regression model to predict Tech Mahindra close from BSE close, interpreting the regression equation, goodness of fit, ANOVA, p-values, and r-squared.

Analyze Tech Mahindra and BSE close prices using regression in Minitab and Excel, including fitted models, scatter plots with regression lines, and computing returns to explore correlations.

Perform regression analysis in Minitab 19 to compute Pearson correlations, generate close price returns, and interpret R, slope, and R-squared for Tech Mahindra versus BSE.

Apply regression modeling and descriptive statistics in Minitab to analyze Tech Mahindra and BSE data, computing mean, standard deviation, skewness, kurtosis, and median to illuminate price movements.

Explore regression modeling in Minitab by regressing Colgate-Palmolive returns on BSE Sensex close returns, importing data, computing close returns, and interpreting a regression with an R-squared around 90%.

Analyze Colgate-Palmolive close price using a Minitab regression model with BSE close returns, assess significance, and visualize with scatter plots, descriptive statistics, and correlation.

Master single-variable linear regression in MS Excel using the Data Analysis Toolpak. Learn to set dependent and independent variables, run regression, and interpret outputs like R-squared, F-test, and t-tests.

Derive regression model with y as Tech Mahindra close returns and x as BSE close returns, using m = 0.1354 and c = -0.0020, and note t-statistics indicate insignificance.

Install and enable Excel's Analysis Toolpak and related add-ins, then use data analysis tools to perform regression modeling with specified x and y ranges.