business analytics statistical analytics syllabus by skillogic
TRANSCRIPT
1.1.Motivate the use of statistical methods for
managerial decision making
1.2.Discuss the concepts of probability distributions
and random variables
1.3.Review methods of representing data,
pictorially and through summary statistics
3.1. Introduce the concept of statistical inference
3.2. Recognize the existence of sample-to-sample
variations
3.3. Understand central limit theorem and its
implications for statistical inference
4.1. Introduce the concept of confidence intervals
as a way to make statistical inferences
4.2. Calculate confidence intervals for population
mean with known and unknown population
standard deviations
5.1. Calculate confidence intervals for population
proportions
5.2. Calculate confidence intervals for population
variance
5.3. Quantify minimum sample sizes to achieve
certain margin of error in predictions
6.1. Learn how to state null and alternative
hypotheses
6.2. Understand type-I and type-II errors
6.3. Conduct one-sided hypothesis test for
population proportion / mean
8.1. Compare the means using paired observations
8.2. Test for the difference of two population means
using independent samples
8.3. Test for the difference of two population
proportions
10.1. Introduce the notion of statistical tests on
ordinal data
10.2. Test for the difference between mean ranks
using paired observations
10.3. Compare mean ranks in two independent
samples
Bivariate data; Scatter plot; Covariance; Correlation
coefficient; Uses and issues; Correlation and
causality; Linear regression; Assumptions
Scatter plot matrix; Multiple linear regression;
Assumptions; Ordinary Least Squares method
(OLS); Basic regression summary; Interpretation of
coefficient estimates, standard errors, t-values and
p-values, and adjusted ; ANOVA table; Basic tests.
Need for deeper analysis; Residuals; Deletion
diagnostics; Added variable plots; Partial
correlation; Model adequacy checks; Plots – Fitted
values vs Residuals, Regressors vs Residuals,
Normal probability plot.
Detection – correlation matrix, VIF, variance
proportion s table; Remedies; subset selection,
best subset; Criteria – R2, Adjusted R2, AIC, BIC,
Mallows Cp
Possible causes; Detection – graphical methods,
formal tests; Remedies – Transformations,
Adjustment to standard errors of OLS estimates,
Generalized least squares
Possible causes; Detection – graphical methods,
formal tests; Remedies – First differences,
Adjustment to standard errors of OLS estimates,
Generalized least squares, Dummy variables and
autocorrelation, forecasting in the presence of
autocorrelation.
20.1. Generalized Linear Models
20.2. Binary and multinomial logistic regressions
20.3. Poisson regression
20.4. Zero-inflated Poisson regression
20.5. Negative Binomial regression
21.1. Missing value patterns: Missing completely at
random (MCAR). Missing at random (MAR).
Missing not at random (MNAR)
21.2. Listwise deletion. Pairwise deletion
21.3. Various imputation methods: Hot deck
imputation. Mean substitution. Regression
imputation. EM imputation
22.1. Censoring and truncation. Characteristics of survival a
22.2. Time-to-event data. Hazard and survival functions
22.3. Kaplan-Meier estimate of survival function
22.4. Cox proportional hazards model (ph), estimation and its
analysis. Extensions
22.5. Stratified ph; ph with time-varying covariates
22.6. Parametric survival analysis with standard distributions
22.7. Accelerated failure time models
23.1. Basic concepts: randomization, replication and control
23.2. Experimental design for testing differences in several means:
Completely randomized and randomized complete block designs.
Cross-over designs
23.3. Two-level factorial experiments---full and fractional. Plackett-
Burman designs
23.4. Designs for three or more levels. Taguchi designs. Response
surface designs
23.5. Case-Control designs for campaign evaluation
23.6. Designs for conjoint analysis
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