Ammar - Statistics tutor - Montréal
1st lesson free
Ammar - Statistics tutor - Montréal

One of our best tutors. Quality profile, experienced in their field, verified qualifications and a great response time. Ammar will be happy to arrange your first Statistics lesson.

Ammar

One of our best tutors. Quality profile, experienced in their field, verified qualifications and a great response time. Ammar will be happy to arrange your first Statistics lesson.

  • Rate S$23
  • Response 2h
  • Students

    Number of students accompanied by Ammar since their arrival at Superprof

    50+

    Number of students accompanied by Ammar since their arrival at Superprof

Ammar - Statistics tutor - Montréal
  • 5 (55 reviews)

S$23/h

1st lesson free

Contact

1st lesson free

1st lesson free

  • Statistics
  • Forensic Science

Master Statistics, Probability, Data Analysis, and Econometrics using SPSS, R, Stata, SAS, and Power BI with a PhD Engineer & Professor | 25+ Years’ Expertise | Students, Researchers & Professionals

  • Statistics
  • Forensic Science

Lesson location

Ambassador

One of our best tutors. Quality profile, experience in their field, verified qualifications and a great response time. Ammar will be happy to arrange your first Statistics lesson.

About Ammar

I am a PhD Engineer, university professor, researcher, and multidisciplinary educator with more than 25 years of experience in statistics, research methodology, data analysis, engineering, mathematics, programming, and applied quantitative research.

I have taught and supported university students, graduate researchers, doctoral candidates, engineers, analysts, managers, and professionals. My experience includes research design, survey analysis, experimental and observational studies, statistical modelling, thesis and dissertation support, data validation, and interpretation of complex analytical results.

My teaching philosophy is based on a clear principle: statistics should be understood as a reasoning process—not treated as a sequence of software commands.

I explain how the research question determines the analysis, why a statistical method is appropriate, what assumptions it requires, how the calculations and software output should be interpreted, and how to distinguish statistical significance from practical or scientific importance.

My expertise includes:

• Descriptive statistics, probability, sampling, estimation, and hypothesis testing
• t-tests, ANOVA, ANCOVA, repeated measures, and nonparametric methods
• Correlation, multiple regression, logistic regression, and model diagnostics
• Chi-square analysis, categorical data, risk, odds, and association measures
• Reliability analysis, exploratory factor analysis, principal component analysis, mediation, and moderation
• Data preparation, missing values, outliers, transformations, assumptions, and quality validation
• IBM SPSS Statistics, SPSS syntax, Microsoft Excel, R, Python, Jamovi, and JASP
• Research methodology, questionnaire analysis, APA-style reporting, and thesis or dissertation support

Lessons are personalized to your level, discipline, research design, dataset, and objectives. I can help you understand statistics systematically, select an appropriate analysis, correct errors in an existing procedure, interpret output, prepare for an examination, or develop a complete research-analysis workflow.

My goal is to help you become accurate, confident, and independent. You should leave each lesson understanding what was analyzed, why the method was selected, what the results mean, what limitations remain, and how to report the conclusions responsibly.

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About the lesson

  • Secondary
  • Adult education
  • Masters
  • +1
  • levels :

    Secondary

    Adult education

    Masters

    Doctorate

  • French
  • English

All languages in which the lesson is available :

French

English

Statistics becomes much easier when formulas, assumptions, software output, research questions, and real-world interpretation are connected clearly.

My lessons help you move beyond mechanically running tests in SPSS or memorizing formulas. You will learn how to define the analytical question, identify the variables and measurement levels, select an appropriate statistical method, check its assumptions, interpret the output correctly, and communicate conclusions without overstating the evidence.

Each lesson is personalized to your current level, course, assignment, thesis, dissertation, dataset, examination, or professional objective. We begin by identifying your research question, data structure, expected output, software environment, and main conceptual or technical difficulties. We then establish a focused analytical plan.

The first free lesson combines a discussion of your background, objectives and tutoring needs, an initial assessment of your current knowledge, personalized planning and scheduling, and a short trial lesson so that we can determine the most effective way to work together.

A- STATISTICAL FOUNDATIONS
• Populations, samples, parameters, statistics, variables, observations, data types, and measurement levels
• Descriptive versus inferential statistics, sampling variability, probability, distributions, and statistical reasoning
• Understanding formulas conceptually rather than applying them mechanically

B- DATA PREPARATION AND QUALITY
• Coding variables, defining labels and values, importing data, restructuring files, and creating derived variables
• Missing values, duplicates, inconsistent entries, outliers, data-entry errors, and logical validation
• Data screening, distribution assessment, transformations, and documentation of analytical decisions

C- DESCRIPTIVE STATISTICS
• Frequencies, percentages, ratios, means, medians, modes, ranges, variance, standard deviation, quartiles, and percentiles
• Tables, cross-tabulations, distributions, box plots, histograms, bar charts, and other statistical graphics
• Describing patterns accurately and selecting summaries appropriate to the data type

D- PROBABILITY AND SAMPLING
• Probability rules, conditional probability, counting methods, random variables, and expected values
• Binomial, normal, and other relevant probability distributions
• Sampling methods, sampling distributions, the central limit theorem, standard errors, and margins of error

E- ESTIMATION AND HYPOTHESIS TESTING
• Confidence intervals, null and alternative hypotheses, significance levels, p-values, effect sizes, and statistical power
• Type I and Type II errors, one-tailed and two-tailed tests, and practical versus statistical significance
• Selecting, conducting, interpreting, and reporting appropriate statistical tests

F- COMPARING GROUPS
• One-sample, independent-samples, and paired-samples t-tests
• One-way and factorial ANOVA, repeated-measures ANOVA, ANCOVA, and post-hoc comparisons
• Nonparametric alternatives, including Mann–Whitney, Wilcoxon, Kruskal–Wallis, and Friedman tests

G- ASSOCIATION AND REGRESSION
• Correlation, covariance, partial correlation, and interpretation of relationships
• Simple and multiple linear regression, model specification, coefficients, confidence intervals, and prediction
• Logistic regression, categorical outcomes, odds ratios, and introductory generalized models when appropriate
• Assumptions, multicollinearity, residuals, influential cases, model fit, and diagnostic analysis

H- CATEGORICAL DATA ANALYSIS
• Cross-tabulations, chi-square tests, expected frequencies, standardized residuals, and measures of association
• Binary and multinomial outcomes, contingency tables, risk, odds, and interpretation
• Choosing methods appropriate to categorical variables and sample conditions

I- SCALE DEVELOPMENT AND MULTIVARIATE METHODS
• Reliability analysis, Cronbach’s alpha, item–total statistics, and scale evaluation
• Exploratory factor analysis, principal component analysis, factor retention, rotation, and interpretation
• Mediation, moderation, interaction effects, path analysis foundations, and other agreed multivariate methods

J- SPSS AND COMPLEMENTARY TOOLS
• IBM SPSS Statistics: Data View, Variable View, menus, syntax, output, charts, and export
• SPSS data management, transformations, recoding, Compute Variable, Select Cases, Split File, Weight Cases, and Merge Files
• Microsoft Excel, R, Python, Jamovi, JASP, or other tools when they support the learner’s project
• Syntax is encouraged when reproducibility, accuracy, or repeated analysis is important

K- RESEARCH ANALYSIS AND REPORTING
• Translating research questions and hypotheses into a defensible analytical strategy
• Selecting analyses according to the study design, variable types, assumptions, and sample size
• Interpreting tables, coefficients, p-values, confidence intervals, effect sizes, and diagnostic results
• Preparing APA-style or institution-specific tables, figures, results sections, and methodological explanations

A typical lesson may include theoretical explanation, review of the research design, data screening, live software analysis, output interpretation, assumption checking, error diagnosis, and preparation of a clear results summary.

You may work with your own assignment, survey, thesis, dissertation, research dataset, professional study, or examination material, provided that confidential information is handled appropriately.

My objective is not merely to tell you which SPSS menu to select or whether a p-value is below .05. It is to help you understand why the analysis is appropriate, what its assumptions mean, how to verify the result, and how to draw conclusions that are statistically and scientifically defensible.

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Rates

Rate

  • S$23

Pack rates

  • 5h: S$115
  • 10h: S$230

online

  • S$23/h

Travel

  • + S$10

free lessons

This first lesson is free to allow you to get to know your teacher so that they can best meet your needs.

  • 1hr

Details

The first free lesson is a structured introductory and trial session. We will briefly introduce ourselves—including your academic or professional background and my relevant expertise—clarify your objectives, deadlines and tutoring needs, and assess your current knowledge through discussion and a short diagnostic activity. We will then establish a focused learning plan and schedule for future lessons. The remaining time will be used for a short trial lesson on a representative concept or problem, allowing you to experience my teaching approach before deciding whether to continue.

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