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Study these flashcards

- California
- California State University - Long Beach
- Criminal Justice And Criminology
- Criminal Justice And Criminology 325
- Becky Nash
- 325 Final

Amber F.

• 77

cards
Difference between t formula and z-score formula

T statistic uses sample variance (s2) and the z-score uses the population variance (o2)

T statistic

Used to test hypotheses about an unknown population mean, u, when the value of o is unknown. The t statistic uses the estimated standard error in the denominator. t = M - u / sM

T distribution

Complete set of t values computed for every possible random sample for a specific sample size (n) for a specific degrees of freedom (df). The t distrib. approximates the shape of a normal distrib., especially for large samples or samples from a normal pop. How well a t distribution approximates a normal distrib. depends on df. As the sample size increases, the df also increases, and better the t distrib. approximates the normal distrib.

The shape of the t distribution

Exact shape changes w/ df. T distribution tends to be flatter and more spreadout, where as normal z distrib. has more of a central peak.

Estimated standard error

(sM), used as an estimate of the real standard error, oM, when the value of o is unknown. Computed using sample variance or sa,ple standard dev & provides estimate of standard distance btwn a sample mean, M, & the population mean, u.

Estimated standard error formula

Sm = s/√n or SM = square root of s2 / n

Degrees of freedom

Degrees of freedom in a t statistic

Why do we use a t distribution table?

Assumptions of the t Test

The influence of sample size & sample variance in a t test

Estimated Cohen's d

How to interpret values of estimated Cohen's d (effects)

Measuring the percentage of variance explained, r2

Criteria for interpreting the value of r2

Reporting the results of a t-test in APA

One-tailed test vs. Two-tailed test

One-tailed test hypothesis tests

Sample variance

T test hypothesis testing (two-tailed)

ANOVA

Why do we use ANOVA over t tests?

Independent variable

quasi-independent variable

Factor

Levels of the factor

Statistical hypotheses notation for ANOVA

The test statistic for ANOVA

Testwise alpha level

Experimentwise alpha level

Between-treatments variance

Within-treatments variance

Total Sum of Squares, SStotal

Within-Treatments Sum of Squares

Between-Treatments SS

Dftotal in ANOVA

Within-Treatments Degrees of Freedom

Between treatments degrees of freedom

Calculations of Variance (MS) and the F-Ratio

The F-Ratio: The test statistic for ANOVA

Error term

The distribution of F-ratos characteristics

How to use the F distribution table

Measuring effect size for ANOVA

Reporting results of ANOVA in APA

Post hoc tests

Tukey's honestly significant difference test

The Scheffe Test

What is the relationship between ANOVA and t tests

Assumptions for ANOVA

Correlation

Main characteristics of correlation

Positive correlation

Negative correlation

Pearson correlation

Perfect correlation

Pearson correlation notation

Where and Why correlations are used

Interpreting correlations

Correlation and restricted range

Correlation and outliers

Coefficient of Determination

Interpreting strength of correlation coefficient

Scatterplot and interpretation

Goals of a regression analysis

The regression line

Linear relationship (straight line) equation

Regression

Regression line

The least-squares solution

Total squared error

Why do we use the regression equation

Standard error of the estimate (SEoE)

Relationship between SEoE and correlation coefficient

Analysis of Regression: Testing the Significance of the Regression Equation

What is r^2?

Similarities/differences btwn regression and correlation

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