Statistical Analysis

CURRICULUM

Statistics – Pre requisites

  • Statistical Diagrams
  • Sample Calculation-1
  • Sample Calculation-2
  • Probability
  • Advanced Probability
  • Permutation and Combination
  • Discrete Random Variables
  • Discrete Distributions
  • Continuous Random Variables
  • The Normal Distribution
  • Correlation & Regression

Introduction to Statistics in R

  • Summarize the data
  • Understand mean, median, mode,
  • Standard deviation
  • quartiles, box plot
  • Correlation
  • Probability
  • Probability distribution
  • Normal Distribution
  • Skewness, Kurtosis
  • Poison Distribution

Random Variable and Normal Distribution

  • Random Variable
  • Normal Distribution
  • Sampling Concept
  • Use statistical methods for managerial decision making
  • Discuss applications of normal distribution

Central limit theorem and confidence interval

  • Central limit theorem
  • Confidence Interval
  • How to interpret confidence interval
  • Make statistical inferences through Confidence Intervals
  • Calculate confidence intervals for population mean with known population standard deviation
  • Calculate confidence intervals for population mean with unknown population standard deviation

Hypothesis Testing & ANOVA

  • Learn how to state null and alternative hypotheses
  • Business implications of hypothesis testing
  • Understand type-I and type-II errors
  • Conduct one-sided hypothesis test for population mean
  • t test
  • Understanding ANOVA
  • One way Analysis of Variance (ANOVA)
  • The ANOVA table in regression analysis
  • F Ratio

Regression

  • Introduction to regression methods
  • Scatter plot Covariance Correlation coefficient
  • Correlation and causality
  • Linear regression
  • Regressors
  • Scatter plot matrix
  • Ordinary Least Squares method (OLS)
  • Assumptions of Linear Regression

Advanced regression

  • Interpretation of coefficient estimates
  • Standard errors
  • t-values and pvalues and adjusted R2, R2
  • ANOVA table
  • Residuals analysis
  • Deletion diagnostics
  • Partial correlation
  • Plots – Fitted values vs Residuals, Regressors vs Residuals, Normal probability plot.
  • Collinearity; Detection – correlation matrix, VIF, variance proportion table , AIC
  • Subset selection, best subset
  • Problem of insignificance of important regressors

Logistic Regression

  • Generalized linear models
  • Likelihood profiling
  • Logistic regression on tabular data
  • Prediction
  • What are the types of Logistic Regression techniques ?
  • How does Logistic Regression work ?
  • How can you evaluate Logistic Regression’s model fit accuracy ?
  • How is it different from linear regression?
  • Why logistic regression is called regression not classification?

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