End of Semester Exam Revision
Chapter 7: Predicting a Quantitative Variable from a Categorical Variable
7.1 Comparing means from two independent samples
End of Semester Exam Revision
End of Semester E
Lecture 10
PSYC 2009
The Normal Distribution and Sampling Distributions
Reading: Ch. 4, pp. 122-127, Ch. 5, pp. 131-135
Last lecture, we used probability to introduce the concept of a
theoretical dist
Lecture 9
PSYC 2009
Introduction to Distributions: Binomial Distribution
Reading: Ch. 4, 116-122
A probability distribution presents the categories of a variable (or
scores, for a quantitative variabl
Lecture 6
PSYC 2009
Experimental Design and Statistical Inference
Reading: Ch. 4, 103-106
Introduction to Experimental Design
An experiment controls the influence of at least one variable whereas
a no
Lecture 12
PSYC 2009
Statistical Models and Significance Testing
Reading: Ch. 6, pp. 175-185
Confidence intervals convey how precise our estimates are. But we can
also use them to make inferences abou
Lecture 20
PSYC 2009
Effect-Size in Analysis of Variance
Reading: Ch.7, pp. 241-249
How big is the effect?
Even when ANOVA tells us there is a significant difference among
treatment means, all we know
Lecture 13
PSYC 2009
Type I and Type II Error
Reading: Ch. 6, pp. 185-192
Last time we learned about the procedure for a significance test:
1. Decide on the confidence level that you wish to use for
d
Lecture 16
PSYC 2009
Comparing Two Means
Reading: Ch.7, pp. 211-223
Basis of the comparison
The key to understanding statistical inference in between-subjects
experiments is randomized assignment.
It
Lecture 5
PSYC 2009
Introduction To Inferential Statistics
Reading: Ch. 4, 87-102
Motives for using inferential statistics:
Sampling
Suppose you wanted to find out the percentage of unemployed in
Aust
Lecture 22
PSYC 2009
Evaluating Linear Regression Models
Reading: Ch.8, pp. 222-269
Evaluating Regression Models with R2
Evaluating how well a regression model predicts the dependent
variable raises i
Lecture 7
PSYC 2009
Introduction to Probability
Reading: Ch. 4, 106-112
Why do we need to know about probability?
1. Anything random involves probabilities.
Inferential statistics are founded on proba
Lecture 4
PSYC 2009
Summarizing Distributions and More Lies
Reading: Ch.3, 72-83
Measures of central tendency
Measures of central tendency are ways of describing a "typical" case in
the data.
The simp
Lecture 21
PSYC 2009
Introduction to Linear Regression
Reading: Ch.8, pp. 252-262
When prediction is the main goal of a study, researchers often turn to
a regression analysis.
A regression model is a
Lecture 2
PSYC 2009
Measurement, Variables and Data
Reading: Ch. 2, 26-49
Basic concepts
A construct is a concept, usually a characteristic or property, that
underlies measurement.
A construct is an
Lecture 1
PSYC 2009
Uncertainty and psychological research
Reading: Ch.1, 5-24
Why do we need to learn about this stuff?
Why do we need measurement?
Asking people questions or interviewing them is "m
Lecture 19
PSYC 2009
Analysis of Variance (ANOVA)
Reading: Ch.7, pp. 234-241
Rationale for ANOVA
We have already seen the need to compare means for more than two
groups in an experiment, and we have l
Lecture 24
PSYC 2009
Correlation, Causation, and the General Linear Model
Reading: Ch.8, pp. 283-289
Correlation and causation
Most often, researchers are interested in relationships between two
varia
Lecture 17
PSYC 2009
Comparing More Than Two Means
Reading: Ch.7, pp. pp. 223-230
Problem of multiple confidence intervals
In many situations we may want to compare means from more than
two independen
Lecture 23
PSYC 2009
Predictive Accuracy and Correlation
Reading: Ch.8, pp. 269-276
Confidence interval for the regression coefficient
Because SSe measures the amount of variation in Y away from Y, it
Lecture14
PSYC 2009
Confidence Intervals and the t-test
Reading: Ch. 6, pp. 192-198
In the most recent lectures we have learned how to use a confidence
interval to perform a significance test, and how
Lecture 25
PSYC 2009
Review of Confidence Intervals and Models
Reading: Ch.11, pp. 368-381
Confidence intervals
How do we realize the goals of statistical inference? We use
confidence intervals to ref
Lecture 18
PSYC 2009
Comparing Two Variances
Reading: Ch.7, pp. pp. 230-234
Variances from Two Independent Samples
Recall that means aren't the only thing we can compare between
experimental condition
Lecture 3
PSYC 2009
Using Graphs to Describe Variables
Reading: Ch. 3, 52-72
So far, weve covered basic ideas about variables and measurement.
Now we're going to examine techniques for describing and
Lecture 8
PSYC 2009
Conditional Probabilities and Risk
Reading: Ch. 4, 112-116
Health psychologists and epidemiologists refer to a characteristic (be it
age, gender, whether a person smokes, etc.) as
Lecture 15
PSYC 2009
Effect Size and Power
Reading: Ch. 6, pp. 198-208
In the most recent lectures we have learned about the t-test and how
Type I and Type II errors, confidence levels, and power are
Lecture 11
PSYC 2009
Introduction to Confidence Intervals
Reading: Ch. 5, pp. 148-162, 169-172
Last lecture, we introduced the idea of a sampling distribution and saw
how it provided a model of how a