What classes do you tutor?

AP Statistics

College and Graduate School Intro-level Statistics in many disciplines such as Business, Marketing, Nursing, Social Work, Psychology, Neuroscience, Communications, Education, Leadership, and other Social Sciences.

What schools do your students attend?

I have been tutoring students from all over the US and I have some international students. I have students from High Schools in Washington State (Bellevue High School, Newport, Overlake, Lakeside), Connecticut, Texas, California, Florida, North Carolina, Pennsylvania, Maryland, New York, Universities (Fordham, Yale, USC Marshal Business School, UC Irvine, Notre Dame, University of Denver Daniels College of Business, Boston College,  Texas A & M University, Rice University, and many more)

When are you available?

I tutor Monday through Friday 8:00 AM – 5:00 PM Pacific Time

Do you tutor in-person or online?

I do both.

In person can be at the student's home, my home or at a local café such as Panera or Starbucks.

Do you provide video lessons?

Yes. Sometimes when it’s difficult to find a time that works, I record a lesson and send it. We can also record our lessons and I can send them to you.

Can you help with different software and calculators?

I am most familiar with the TI-84 and Excel. I can also help with the TI-Nspire, SPSS and JASP.

How often do you meet with your students?

It's different for every student.  I meet with students weekly, twice a week, a couple times per month or as needed.  I have students that like to meet weekly and 4 or 5 times in one week before an exam.

What are your Rates?

60 minute lesson       $100

What topics do you tutor?

Exploring One variable Data

Representing quantitative and categorical variables

Bar charts, dot plots, stem & leaf plots, box plots, scatter plots, histograms

The 5 number summary, Frequency tables

Continuous vs. Discrete variables

Percentile

Exploring Two Variable Data

Correlation

Simple Linear Regression

Multiple Regression

Residuals

Analyzing departures from linearity

Representing two categorical variables

Collecting Data

Planning a study

Random Sampling

Sampling bias

Experimental design

Correlation vs causation

Probability, Random Variables, Probability Distributions

Random and non-random patterns

Simulations

Addition and multiplication rules of probability

Independent Events

Mutually Exclusive

Conditional, marginal and joint Probability

Venn diagrams, probability trees

Random Variables: means, standard deviations, combining random variables

Normal, Binomial, Geometric, Chi Square, F, Poisson, Exponential, Gamma and Uniform Distributions

Empirical rule

Permutations and Combinations

Chebyshev’s Theorem

Sampling Distributions

Normal, Binomial, Chi Square, Poisson, Exponential, Gamma

Central Limit Theorem

Biased and unbiased point estimates

Population vs sample

Parameter vs Statistic

Inference for proportions, means, slopes

Conditions

Confidence Intervals

Type I, Type II errors, P-value, Power

Hypothesis tests

ANOVA

Time Series Forecasting