Data Analytics

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Module 1: Descriptive statistics and Inferential
statistics
 Using Statistics
 Percentiles and Quartiles
 Measures of Central Tendency
 Measures of Variability
 Grouped Data and the Histogram
 Skewness and Kurtosis
 Relations between the Mean and Standard Deviation
 Methods of Displaying Data
 Exploratory Data Analysis
 Using the Computer


Inferential statistics
Random variables
 Expected Values of Discrete Random Variables
 Sum and Linear Composite of Random Variables
 Bernoulli Random Variable
 The Binomial Random Variable
 The Geometric Distribution
 The Hyper geometric Distribution
 The Poisson Distribution
 Continuous Random Variables
 Uniform Distribution
 The Exponential Distribution
 Normal distribution


Sampling and Sampling Distributions
 Sample Statistics as Estimators of Population Parameters
 Sampling Distributions
 Estimators and Their Properties
 Degrees of Freedom
 The Template

Confidence Intervals
 Confidence Interval for the Population Mean When the Population Standard Deviation is Known
 Confidence Intervals for m When s is Unknown - The t Distribution
 Large-Sample Confidence Intervals for the Population Proportion p
 Confidence Intervals for the Population Variance
 Sample Size Determination
 The Templates

Hypothesis Testing
 The Concept of Hypothesis Testing
 Computing the p-value
 The Hypothesis Test
 Pre-Test Decisions

The Comparison of Two Populations
 Paired-Observation Comparisons
 A Test for the Difference between Two Population Means UsingIndependent Random Samples
 A Large-Sample Test for the Difference between Two Population Proportions
 The F Distribution and a Test for the Equality of Two Population Variances


Analysis of Variance
 The Hypothesis Test of Analysis of Variance
 The Theory and Computations of ANOVA
 The ANOVA Table and Examples
 Further Analysis
 Models, Factors, and Designs
 Two-Way Analysis of Variance
 Blocking Designs


Probability
 Basic Definitions: Events, Sample Space, and Probabilities
 Basic Rules for Probability
 Conditional Probability
 Independence of Events
 Combinatorial Concepts
 The Law of Total Probability and Bayes’ Theorem
 Joint Probability Table
 Using the Computer


Module2: Regression and Predictive Analysis

 The Simple Linear Regression Model
 Estimation: The Method of Least Squares
 Error Variance and the Standard Errors of Regression Estimators
 Correlation
 Hypothesis Tests about the Regression Relationship
 How Good is the Regression?
 Analysis of Variance Table and an F Test of the Regression Model
 Residual Analysis and Checking for Model Inadequacies
 Use of the Regression Model for Prediction
 The Solver method of Regression.


Multiple Regression
 Using Statistics
 The k-Variable Multiple Regression  Model
 The F Test of a Multiple Regression Model
 How Good is the Regression
 Tests of the Significance of Individual Regression Parameters
 Testing the Validity of the Regression Model
 Using the Multiple Regression Model for Prediction