Business Analytics Statistical Analytics Syllabus by Skillogic

Skillogic Business Analytics Course

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

2.1.Introduce standard normal distribution

2.2. Discuss applications of normal distribution

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

7.1. Conduct two-sided hypothesis tests 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

9.1. Introduce Design of Experiments

9.2. Conduct one way Analysis of Variance

(ANOVA)

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

Dummy variables; Transformations – Power

transformation, Box-Cox transformation.

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.

Linear Probability Model; Advantages and issues;

Guidelines for Linear Regression Modeling

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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