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- Recommended Sources

Principles of Statistics for Engineers

Mathematical Sciences Librarian

Science Education Specialist

- OSU Library CatalogSearch for books and other items available at OSU - includes ebooks
- OhioLINK CatalogSearch for books statewide.

- Electronic Book Center (OhioLINK) [Full Text] This link opens in a new windowElectronic Book Center included ebooks on all subjects, including many encyclopedias, handbooks, biographical collections, and guides.

The OSU Library Catalog has subject headings. If you had found a good book on your topic, examine the subject headings for that book below the location/call number box. You can click on these to see other items with that subject ot you can search subject directly as follows.

Examples:

"History" is a subdivision of a subject that can be added after any other term. Examples: Format: branch--history. Clickable examples:

For mathematics history in a country, region, use for format mathematics--country/region--history Examples:

Citation databases allow you to see for each article the items it cited and also the items that have cited it. If you find a great article, use these databases to move both back and forward in time from that article.

- Scopus This link opens in a new windowScopus is a citation database of peer-reviewed literature and quality web sources with smart tools to track analyze and visualize research. Tools to sort, refine and quickly identify results help researchers focus on the outcome of their work.
- Web of Science Core Collection This link opens in a new windowProvides access to citation indexes which can be searched individually or as one file.
- Arts & Humanities Citation Index indexes 1,100 of the world's leading arts and humanities journals, as well as covering individually selected, relevant items from over 6,800 major science and social science journals.
- Science Citation Index Expanded indexes 5,300 major journals across 164 scientific disciplines and contains searchable, full-length, English-language author abstracts for approximately 70 percent of the articles in the database.
- The Social Sciences Citation Index indexes 1,700 journals spanning 50 disciplines, as well as covering individually selected, relevant items from over 3,300 of the world's leading scientific and technical journals.
- Book Citation Indexes- Science and Social Sciences & Humanties indexes over 30,000 editorially selected books in the sciences, social sciences and humanities, with 10,000 new books added each year. It contains searchable, full-length, English-language author abstracts for approximately 60 per cent of the articles in the database.

- Computers and Applied Sciences Complete [Selected Articles in Full Text] This link opens in a new windowComputers & Applied Sciences Complete (CASC) covers the spectrum of the applied sciences, representing knowledge on traditional engineering challenges and providing material for research concerning the business and social implications of new technology. CASC provides indexing and abstracting for more than 1,300 academic journals, professional publications, and other reference sources. Full Text is also available for more than 500 periodicals. Subject areas include the many engineering disciplines, computer theory, and new technologies. Includes Computer Source - Consumer Edition.

- JSTOR [Selected Articles in Full Text] This link opens in a new windowJSTOR offers multidisciplinary and discipline-specific collections in the humanities, sciences and social sciences. The moving wall represents the time period between the last issue available in JSTOR and the most recently published issue of a journal. It is specified by publishers in their license agreements with JSTOR, and generally ranges from 3 and 5 years. In calculating the moving wall, the current, incomplete year is not counted.
- Academic Search Complete [Selected Articles in Full Text] This link opens in a new windowAcademic Search Complete is comprehensive scholarly, multi-disciplinary full-text database, with more than 5,300 full-text periodicals, including 4,400 peer-reviewed journals. In addition to full text, this database offers indexing and abstracts for more than 9,300 journals and a total of 10,900 publications including monographs, reports, conference proceedings, etc.
- Google Scholar This link opens in a new windowConnect Google Scholar to Find It! by going to Settings > Library Links. There, search for Ohio State, choose the Ohio State option, and save.

You'll see some important buttons while using the OSU Libraries resouces. Especially important are these 4:

Where: OSU Library Catalog.

What: Have an item sent to another location for pickup or recall an item checked out.

Where: OSU Library Catalog.

What: Check for access of an individual item via OhioLINK - such as an item checked out at OSU. Also repeats searches in the OhioLINK catalog to see additional options for your need.

Where: OhioLINK Catalog

What: Click to have an item delivered to OSU from an OhioLINK library.

Where: Library Databases

What: Check for online access to journal articles, conference papers, and more. Also gives interlibrary loan option if item is not available online.

- Describing data
- Box and whisker plots
- Stem and leaf plots
- Boxplots
- Fitting Lines to Data
- Stemplots
- Scatterplots
- Sampling, Surveying, and Data Analysis
- Editing graphs
- Fitting curves to data
- Pie charts, bar graphs, and pareto charts
- Other graphical methods and numerical methods
- An introduction to sample design
- Khan Academy: Introduction to sampling distributions
- Khan Academy: Techniques for random sampling and avoiding bias
- Khan Academy: Identifying a sample and population
- ·Khan Academy: Ways to represent data

- Khan Academy: Addition rule for probability

- Conditional probability
- Khan Academy: Calculating conditional probability
- Khan Academy: Conditional probability and combinations
- Khan Academy: Conditional probability tree diagram example
- Khan Academy: Marginal distribution and conditional distribution
- Khan Academy: Conditional probability and independence

- Poisson probability distribution and the urn model
- Random variables and discrete probability distributions
- Khan Academy: Variance and standard deviation of a discrete random variable
- Khan Academy: Introduction to discrete probability distributions
- Khan Academy: Discrete uniform distribution

- Expected values, means, variances, and standard deviations
- Population and sample variance
- Khan Academy: Expected Value: E(X)
- Khan Academy: Mean (expected value) of a discrete random variable

- Probability distributions for continuous random variables
- Khan Academy: Discrete and continuous random variables
- Khan Academy: Probability density functions
- Khan Academy: More on probability density functions
- Khan Academy: Random variables
- Khan Academy: Constructing a probability distribution for random variable
- Khan Academy: Worked example finding area under density curves
- Khan Academy: Probabilities from density curves
- Khan Academy: Continuous probability distribution intro

- Population mean and sample mean
- Population and sample variance
- Random sampling
- Samples and Surveys
- Sampling, Surveying, and Data Analysis
- Poisson probability distribution and the urn model
- Introduction to designing experiments
- Using samples
- Khan Academy: Sampling distribution of the sample mean
- Khan Academy: Sampling distribution of the sample mean
- Khan Academy: Sampling distribution of the sample mean 2
- Khan Academy: Inferring population mean from sample mean
- Khan academy: Difference of sample means distribution

- Binomial Distribution
- Binomial probability distribution
- Khan Academy: Visualizing a binomial distribution
- Khan Academy: Visualizing a binomial distribution

- The normal distribution
- Finding probability using a normal distribution. Part 1
- Finding probability using a normal distribution. Part 2
- Finding probability using a normal distribution. Part 3
- Finding probability using a normal distribution. Part 4
- Finding probability using a normal distribution. Part 5
- Finding Z-values with a normal distribution. Part 1
- Finding Z-values with a normal distribution. Part 2
- Finding Z-values with a normal distribution. Part 3
- Finding z-values with a normal distribution. Part 4
- Properties of the normal distribution
- The area under the normal distribution
- The normal probability distribution
- The standard normal distribution
- Khan Academy: Introduction to the normal distribution
- Khan Academy: Normal distribution problems: z-score
- Khan Academy: Normal distribution problems: Qualitative sense of normal distributions
- Khan Academy: Normal distribution problems: Empirical rule
- Khan Academy: Standard normal distribution and the empirical

- Central limit theorem. Part 1
- Central limit theorem. Part 2
- Applying central limit theorem to population means. Part 1
- Applying central limit theorem to population means. Part 2
- Applying central limit theorem to population means. Part 3
- Applying central limit theorem to population proportions. Part 1
- Applying central limit theorem to population proportions. Part 2
- Khan Academy: Central limit theorem

- Frequentist approaches sample size in adaptive clinical designs
- The study of candidate genes in drug trials sample size considerations
- ·Khan Academy: Determining sample size based on confidence and margin of error
- ·Khan Academy: Sample size for a given margin of error for a mean

- Confidence Intervals
- Confidence intervals for population means. Part 1
- Confidence intervals for population means. Part 2
- ·Khan Academy: Confidence intervals and margin of error
- ·Khan Academy: Confidence interval example
- ·Khan Academy: Confidence interval of difference of means
- ·Khan Academy: Confidence intervals for the difference between two proportions
- ·Khan Academy: Interpreting confidence level example
- ·Khan Academy: Confidence interval for the slope of a regression line
- ·Khan Academy: Small sample size confidence intervals
- ·Khan Academy: Confidence interval simulation
- ·Khan Academy: Conditions for valid confidence intervals

- Estimating population means (large samples). Part 3
- Khan Academy: Large sample proportion hypothesis testing
- Khan Academy: Large sample proportion hypothesis testing

- Khan Academy: T-statistic confidence interval
- Khan Academy: Constructing t interval for difference of means

- Khan Academy: Confidence intervals and margin of error
- Khan Academy: Determining sample size based on confidence and margin of error

- Applying central limit theorem to population means. Part 1
- Applying central limit theorem to population means. Part 2
- Applying central limit theorem to population means. Part 3
- Population mean and sample mean
- Estimating population means (large samples)
- Using samples
- Khan Academy: Sampling distribution of the sample mean
- Khan Academy: Sampling distribution of the sample mean
- Khan Academy: Sampling distribution of the sample mean 2
- Khan Academy: Inferring population mean from sample mean
- Khan academy: Difference of sample means distribution
- Khan Academy: Confidence interval of difference of means
- Khan Academy: Inferring population mean from sample mean
- Khan Academy: Small sample size confidence intervals

- Tests of Significance
- Hypothesis testing, types of error, and small samples
- Khan Academy: P-values and significance tests
- Khan Academy: Hypothesis testing and p-values
- Khan Academy: Using a table to estimate P-value from t statistic
- Khan Academy: Calculating a P-value given a z statistic
- Khan Academy: Comparing P-values to different significance levels
- Khan Academy: Estimating a P-value from a simulation
- Khan Academy: Using a P-value to make conclusions in a test about slope
- Khan Academy: Statistical significance of experiment
- Khan Academy: Comparing P-value from t statistic to significance level
- Khan Academy: Comparing P-values to different significance levels
- Khan Academy: P-values and significance tests
- Khan Academy: Hypothesis testing and p-values
- Khan Academy: Comparing P-values to different significance levels

- The student t-distribution
- Using a student t-distribution statistical table
- T-Tests : Lecture 7
- Independent T-Tests : Lecture 8
- Khan Academy: Using a table to estimate P-value from t statistic
- Hypothesis testing, types of error, and small samples

- Comparing Two Means
- Khan Academy: Introduction to t statistics
- Khan Academy: T-statistic confidence interval
- Khan Academy: Example of hypotheses for paired and two-sample t tests
- Khan Academy: Two-sample t test for difference of means
- Khan Academy: Hypotheses for a two-sample t test
- Khan Academy: Conclusion for a two-sample t test using a P-value

- Khan Academy: Introduction to power in significance tests
- Khan Academy: Examples thinking about power in significance tests

- T-tests
- T-test
- T-test I : the z-test and the single sample t-test
- T-test II : independent samples t-test
- T-test III : repeated measures t-test
- T test
- T-Tests : Lecture 7
- Independent T-Tests : Lecture 8
- Khan Academy: Two-sample t test for difference of means
- Khan Academy: Example of hypotheses for paired and two-sample t tests
- Khan Academy: Hypotheses for a two-sample t test
- Khan Academy: Conclusion for a two-sample t test using a confidence interval

- ·Khan Academy: Two-sample t test for difference of means
- ·Khan Academy: Example of hypotheses for paired and two-sample t tests
- ·Khan Academy: Hypotheses for a two-sample t test

- Correlation & simple regression
- Scatter Plots and Gym Training
- Scatterplots
- Call center forecasting : linear regression models
- Khan Academy: Constructing a scatter plot
- Khan Academy: Studying, shoe size, and test scores scatter plots
- Khan Academy: People smoking less over time scatter plot
- Khan Academy: Constructing scatter plot exercise example
- Khan Academy: Introduction to inference about slope in linear regression

- An introduction to descriptive & inferential statistics
- An introduction to inferential statistical tests
- Descriptive verses inferential statistics
- Inferential statistics
- Khan Academy: Introduction to inference about slope in linear regression
- Khan Academy: Conditions for inference on slope
- Khan Academy: Confidence interval for the slope of a regression line
- Khan Academy: Calculating t statistic for slope of regression line
- Khan Academy: Using a P-value to make conclusions in a test about slope
- Khan Academy: Using a confidence interval to test slope

- Khan Academy: Impact of transforming (scaling and shifting) random variables
- Khan Academy: Transforming nonlinear data
- Khan Academy: Worked example of linear regression using transformed data
- Khan Academy: Example: Transforming a discrete random variable

- Khan Academy: Comparing models to fit data
- Khan Academy: Describing subsets of sample spaces exercise

- Adaptive clinical trial design randomization
- An introduction to randomized controlled trials
- Design and conduct of non-inferiority trials
- Assumptions in ANOVA and tests
- ANOVA tables
- Mechanisms of ANOVA
- Mathematics of ANOVA. Part 1
- Mathematics of ANOVA. Part 2
- Remedial measures for assumption violation in ANOVA
- Statistical inference in ANOVA
- Two-way ANOVA
- Two-way ANOVA tables
- ANOVA
- An introduction to ANOVA
- ANOVA repeated measures
- One-way ANOVA. Part I
- One-way ANOVA. Part II
- One-way ANOVA. Part III
- One-Way ANOVA
- ANOVA : Lecture 9
- Khan Academy: ANOVA 1: Calculating SST (total sum of squares)
- Khan Academy: ANOVA 2: Calculating SSW and SSB (total sum of squares within and between)
- Khan Academy: ANOVA 3: Hypothesis test with F-statistic

- Khan Academy: Introduction to experiment design

- Adaptive clinical trial design randomization
- Khan Academy: Matched pairs experiment design
- Khan Academy: Introduction to experiment design

- Choosing & Usng SourcesTutorials on finding and using info.
- Article Express / Interlibrary LoanRequest articles, conference papers, and even books (check OhioLINK first) when unavailable at OSU. Request scans of print-only journal articles available at OSU.