Module 1: Introduction to Statistics and Inference

• Defining statistics, key terms, and data types
• Distinguishing between Descriptive Statistics and Inferential Statistics
• Understanding Population, Sample, Parameters, and Statistics
• Overview of key probability concepts and rules
• Practical session: Data Exploration and Summary Statistics in Software (e.g., calculating mean, median, standard deviation, and visualization)

Module 2: Probability Distributions and Estimation

• Exploration of the Normal Distribution and its properties
• The fundamental role and application of the Central Limit Theorem
• Point Estimation: The concept of a single best guess for a population parameter
• Interval Estimation: Constructing and interpreting Confidence Intervals (CIs)
• Practical session: Calculating Confidence Intervals for Population Means and Proportions and interpreting the margin of error

Module 3: Fundamentals of Hypothesis Testing

• Setting up the Null Hypothesis () and the Alternative Hypothesis ()
• Defining the two types of errors: Type I () and Type II ()
• The concept of the Significance Level () and the critical p-value threshold
• A step-by-step process for executing and documenting a formal hypothesis test
• Practical session: Formulating Hypotheses and Defining Error Thresholds based on real business questions

Module 4: One-Sample Hypothesis Tests

• Detailed walkthrough of the One-Sample z-test (for known population standard deviation)
• In-depth focus on the Student's t-distribution and the One-Sample t-test (for unknown population standard deviation)
• Reviewing necessary assumptions (e.g., random sampling, near-normality) for one-sample tests
• Calculating and interpreting the test statistic and p-value for decision making
• Practical session: Conducting a One-Sample t-test and Interpreting Results to test a single population claim

Module 5: Two-Sample Hypothesis Tests

• Differentiating between independent samples and dependent (paired) samples
• Performing the Two-Sample t-test for comparing the means of two independent groups (considering variance equality)
• Applying the Paired t-test for before-and-after analysis or matched subjects
• Testing for differences between two population proportions using the Two-Sample z-test
• Practical session: Comparing Two Independent Groups using Software to assess the effectiveness of a treatment or intervention

Module 6: Analysis of Variance (ANOVA)

• Understanding when ANOVA is necessary (comparing the means of more than two groups)
• Theory of One-Way ANOVA, including partitioning total variability (SS Total, SS Between, SS Within)
• Interpreting the F-statistic, the F-distribution, and the overall ANOVA p-value
• Using Post-Hoc Tests (such as Tukey's HSD or Bonferroni correction) to identify which specific pairs of means differ significantly
• Practical session: Performing a One-Way ANOVA and Post-Hoc Analysis to evaluate differences across multiple product versions or marketing campaigns

Module 7: Non-Parametric Tests

• Introduction to Non-Parametric Statistics and why they are used (when data is not normal or is ordinal/nominal)
• The Mann-Whitney U Test as the non-parametric alternative to the Independent Two-Sample t-test
• The Wilcoxon Signed-Rank Test as the non-parametric alternative to the Paired t-test
• The Kruskal-Wallis H Test as the non-parametric alternative to One-Way ANOVA
• Practical session: Applying a Non-Parametric Test (e.g., Mann-Whitney U) to data that violates the normality assumption

Module 8: Chi-Square Tests and Association

• Executing the Chi-Square Test for Goodness-of-Fit to compare observed frequencies against expected frequencies
• Executing the Chi-Square Test for Independence to determine if there is an association between two categorical variables using contingency tables
• Calculating and interpreting measures of association for categorical data (e.g., Cramer's V, Phi)
• Understanding the assumptions and potential limitations of Chi-Square () Tests
• Practical session: Testing for Association Between Categorical Variables (e.g., product preference by gender)

Module 9: Simple Linear Regression

• Reviewing Correlation analysis and its link to Simple Linear Regression
• The Ordinary Least Squares (OLS) method for estimating the best-fit line
• Interpreting the Regression Coefficients ( and ) in the context of the variables
• Hypothesis Testing for the Slope () to determine if a linear relationship exists
• Practical session: Building a Simple Regression Model and Testing Significance to predict an outcome variable from a single predictor

Module 10: Advanced Topics and Model Selection

• Introduction to the conceptual framework of Multiple Regression (using more than one predictor)
• In-depth discussion of key Model Assumptions (e.g., linearity, independence of errors, homoscedasticity)
• Strategies for dealing with assumption violations and improving model fit (e.g., variable transformations)
• A brief introduction to Logistic Regression for predicting binary outcomes
• Practical session: Review of All Tests and Choosing the Appropriate Statistical Test for various scenarios based on data type and research goal

Requirements:

·       Participants should be reasonably proficient in English.

·       Applicants must live up to Armstrong Global Institute admission criteria.

Terms and Conditions

1. Discounts: Organizations sponsoring Four Participants will have the 5th attend Free

2. What is catered for by the Course Fees: Fees cater for all requirements for the training – Learning materials, Lunches, Teas, Snacks and Certification. All participants will additionally cater for their travel and accommodation expenses, visa application, insurance, and other personal expenses.

3. Certificate Awarded: Participants are awarded Certificates of Participation at the end of the training.

4. The program content shown here is for guidance purposes only. Our continuous course improvement process may lead to changes in topics and course structure.

5. Approval of Course: Our Programs are NITA Approved. Participating organizations can therefore claim reimbursement on fees paid in accordance with NITA Rules.

Booking for Training

Simply send an email to the Training Officer on training@armstrongglobalinstitute.com and we will send you a registration form. We advise you to book early to avoid missing a seat to this training.

Or call us on +254720272325 / +254725012095 / +254724452588

Payment Options

We provide 3 payment options, choose one for your convenience, and kindly make payments at least 5 days before the Training start date to reserve your seat:

1. Groups of 5 People and Above – Cheque Payments to: Armstrong Global Training & Development Center Limited should be paid in advance, 5 days to the training.

2. Invoice: We can send a bill directly to you or your company.

3. Deposit directly into Bank Account (Account details provided upon request)

Cancellation Policy

1. Payment for all courses includes a registration fee, which is non-refundable, and equals 15% of the total sum of the course fee.

2. Participants may cancel attendance 14 days or more prior to the training commencement date.

3. No refunds will be made 14 days or less before the training commencement date. However, participants who are unable to attend may opt to attend a similar training course at a later date or send a substitute participant provided the participation criteria have been met.

Tailor Made Courses

This training course can also be customized for your institution upon request for a minimum of 5 participants. You can have it conducted at our Training Centre or at a convenient location. For further inquiries, please contact us on Tel: +254720272325 / +254725012095 / +254724452588 or Email training@armstrongglobalinstitute.com

Accommodation and Airport Transfer

Accommodation and Airport Transfer is arranged upon request and at extra cost. For reservations contact the Training Officer on Email: training@armstrongglobalinstitute.com or on Tel: +254720272325 / +254725012095 / +254724452588