Elpidia Juli SPSS
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From Primary School Math to PhD Data Analysis: Meet Your New Partner in Academic Success

Struggling with numbers? Meet Dr. Elpidia Juli, a passionate educator with a PhD from Universiti Malaysia Sabah and over 4 years of proven teaching experience. Whether you are a primary student tackling fractions or a PhD candidate drowning in data, Dr. Elpidia makes the complex feel simple.

Why Choose Dr. Elpidia?

For University Students: Mastery in SPSS and complex data interpretation. If you need help with Structural Equation Modelling (SEM), ANOVA, or Coefficients, she doesn't just run the numbers—she helps you read them.

For School Students: Specialist in Mathematics and Additional Mathematics. She turns "math-phobia" into "math-confidence" for both primary and secondary levels.

Flexible Learning: Expert in both interactive Online sessions and focused Physical tutoring.

With an easy-going personality and a PhD-level understanding of logic, Dr. Elpidia is more than a tutor—she’s a mentor who truly cares about your breakthrough.

Subjects

  • English Beginner-Expert

  • Mathematics and Statistics Beginner-Expert

  • Additional Math Beginner-Intermediate

  • Additional Mathematics Beginner-Expert

  • Malay Language Expert

  • Analysis of variance (ANOVA) in SPSS Beginner-Expert

  • Data analysis using SPSS and Excel for a project Beginner-Expert

  • Structural equation modeling (SEM) through SPSS, AMOS, SmartPLS Beginner-Expert

Experience

  • Tutor (Jan, 2021–Feb, 2023) at My Tuition A+

Education

  • Social Sciences (Sociology and Social Anthropology) (Sep, 2021–Sep, 2025) from Universiti Malaysia Sabah
  • Master of Science Health and Social Care (Aug, 2020–Aug, 2021) from University of Edinburgh Napier
  • Bachelor of Science (Hons). Mathematics (Mar, 2014–Mar, 2016) from UNIVERSITI TEKNOLOGI MARA SHAH ALAM

Fee details

    RM80–100/hour (US$20.15–25.19/hour)

Courses offered

  • Introduction to Statistical Analysis

    • US$700
    • Duration: 1 Month
    • Delivery mode: Online
    • Group size: Individual
    • Instruction language: English
    • Certificate provided: Yes
    Chapter 1: Introduction to Statistics

    1.1 Definition and Scope of Statistics
    1.2 Importance of Statistical Analysis
    1.3 Applications in Various Fields
    1.4 Types of Statistics (Descriptive vs Inferential)
    1.5 Role of Statistics in Research

    Chapter 2: Types and Sources of Data

    2.1 Qualitative vs Quantitative Data
    2.2 Discrete vs Continuous Data
    2.3 Levels of Measurement (Nominal, Ordinal, Interval, Ratio)
    2.4 Primary vs Secondary Data
    2.5 Methods of Data Collection

    Chapter 3: Data Organization and Presentation

    3.1 Classification and Tabulation
    3.2 Frequency Distributions
    3.3 Graphical Representation:

    Bar Charts
    Histograms
    Pie Charts
    Line Graphs
    3.4 Data Visualization Principles
    Chapter 4: Descriptive Statistics

    4.1 Measures of Central Tendency:

    Mean
    Median
    Mode

    4.2 Measures of Dispersion:

    Range
    Variance
    Standard Deviation

    4.3 Shape of Distribution:

    Skewness
    Kurtosis
    Chapter 5: Probability Theory

    5.1 Basic Concepts of Probability
    5.2 Rules of Probability
    5.3 Conditional Probability
    5.4 Probability Distributions:

    Binomial Distribution
    Normal Distribution
    Poisson Distribution
    Chapter 6: Sampling Techniques

    6.1 Population vs Sample
    6.2 Sampling Methods:

    Random Sampling
    Stratified Sampling
    Systematic Sampling
    Cluster Sampling

    6.3 Sampling Errors and Bias

    Chapter 7: Inferential Statistics

    7.1 Estimation Techniques
    7.2 Confidence Intervals
    7.3 Hypothesis Testing:

    Null and Alternative Hypotheses
    Type I and Type II Errors
    p-values
    Chapter 8: Statistical Tests

    8.1 t-test
    8.2 Chi-square test
    8.3 ANOVA (Analysis of Variance)
    8.4 Non-parametric Tests

    Chapter 9: Correlation and Regression

    9.1 Correlation Analysis
    9.2 Pearson vs Spearman Correlation
    9.3 Simple Linear Regression
    9.4 Multiple Regression
    9.5 Interpretation of Results

    Chapter 10: Data Analysis Using Software

    10.1 Introduction to Statistical Software:

    SPSS
    R
    Microsoft Excel

    10.2 Data Entry and Cleaning
    10.3 Running Basic Analyses
    10.4 Interpreting Output

    Chapter 11: Reporting Statistical Results

    11.1 Writing Statistical Findings
    11.2 Tables and Figures in Reports
    11.3 APA/Academic Reporting Standards
    11.4 Common Errors in Interpretation

    Chapter 12: Ethical Considerations in Statistics

    12.1 Data Integrity
    12.2 Misuse of Statistics
    12.3 Transparency and Reproducibility

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