Business Knowledge Series course

This course introduces the basic concepts in Statistics and the techniques that can be used to address research and business questions. It discusses how to compute and interpret descriptive statistics, and how to utilize statistical graphs to picture data. Participants will learn about the inferential methods of estimation and hypothesis testing. Correlation and regression analyses are introduced, and methods for investigating categorical data are presented. Hands-on examples are provided to reinforce statistical ideas and interpretation.

Learn how to

• identify and differentiate types and uses of variables and levels of measurement
• interpret statistical concepts such measures of central tendency and variability
• use statistical graphs to picture your data
• interpret results of statistical inference such as confidence intervals and hypothesis testing
• identify the appropriate statistical procedure to answer a research or business question
• apply correlation and regression analyses to address a research or business problem
• derive insights from categorical data analysis such as Chi-square tests and logistic regression

Who should attend

Anyone who wants to learn or re-learn the elementary concepts and basic techniques in Statistics.

Formats available	Duration
Classroom:	2 days

There are no prerequisites for this course.

Basic Statistical Concepts

• Descriptive and Inferential Statistics
• Populations and Samples
• Parameters and Statistics
• Uses of Variables: Independent and Dependent
• Types of Variables: Quantitative and Categorical
• Scales of Measurement: Nominal, Ordinal, Interval, Ratio
• Statistical Methods

Descriptive Statistics

• Describing your Data
• Measures of Central Tendency
• Measures of Location
• Measures of Variability

Picturing Your Data

• Histograms
• Normal Distribution
• Skewness and Kurtosis
• Normal Probability Plots
• Box Plots
• Assessing Normality

Confidence Intervals

• Point Estimators, Variability, and Standard Error
• Distribution of Sample Means
• Confidence Intervals
• Normality and the Central Limit Theorem

Hypothesis Testing

• Introduction to Hypothesis Testing
• Steps in Hypothesis Testing
• Types of Errors and the Level of Significance
• The p-value, Effect Size, and Sample Size
• Statistical Hypothesis Test

Correlation and Regression

• Correlations and Scatter Plots
• Simple Linear Regression Model
• Concepts of Multiple Regression
• Model Building

Categorical Data Analysis

• Describing Categorical Data
• Association Between Categorical Variables
• Tests of Association
• Introduction to Logistic Regression
• Logistic Regression with Categorical Predictors