Stats Final

Question 1

Parsimonious

Answer 1

Having a select amount of parameters; not including certain types of data

Question 2

What is multiple regression (compared to bivariate regression)? What is the purpose of the analysis?

Answer 2

There are at least two predictors in multiple regression and only one in bivariate regression. The purpose is to develop a predictive model based on correlations between variables.

Question 3

What does R^2 measure? Identify another name for it.

Answer 3

Percentage of variance explained by the IVs combined; Multiple correlation coefficient; indicates the correlation between the actual and expected values of the DV.

Question 4

In SPSS output, how does a B weight differ from a beta weight? How are they used?

Answer 4

B weights are the slope weights used to calculate the value of the DV; beta weights are the standardized B weights that are used to indicate the strength of the predictors.

Question 5

What is the error or residual in regression analysis?

Answer 5

The amount of variability in the outcome variable (DV) that is not explained by the predictors (IVs).

Question 6

What is the equation of the regression line used to predict values of the outcome (criterion) variable?

Answer 6

Y-prime = a + bX

Question 7

Name two reasons it’s preferable to conduct an ANOVA rather than multiple t-tests?

Answer 7

Number of comparisons
Missing info
Need one answer, not several
Inflated Type I error rate

Question 8

A mean square is the same as what type of statistic?

Answer 8

Variance

Question 9

How is the variance partitioned in ANOVA?

Answer 9

Between and within groups

Question 10

What is another word for groups in statistics-speak?

Answer 10

Levels

Question 11

Why Conduct Correlational Research? (4)

Answer 11

Ethical reasons
Financial reasons
Nature of the question
Can’t form an experimental group

Question 12

A researcher rejects the null hypothesis for a one-way groups ANOVA, where k = 4. What is the next step in the analysis?

Answer 12

Post-hoc test

Question 13

What is covariance?

Answer 13

the sum of the products of the deviations between the two sets of scores, divided by the number of pairs of scores.
∑(X – X-bar)(Y-Y-bar)/ n

Question 14

Assumptions of Linear Correlation (3)

Answer 14

1. Linearity: The data are best described by a straight line
2. Normality: Data points are normally distributed.
3. Homoscedasticity: Equal variance of data points along the line. In the case of heteroscedasticity, the Pearson r will underestimate the strength of a correlation.

Question 15

What does the correlation coefficient help us do?

Answer 15

predict the value of one variable if we know the value of another. Goal is to develop a formula for making predictions about the DV based on observed values of the IV.

Question 16

Simple bivariate regression

Answer 16

One predictor, one outcome (e.g., predicting salary from education).

Question 17

Difference between correlation and regression?

Answer 17

Correlation focuses on degree of “scatter”; regression focuses on the slope of the line.

Question 18

Error equals?

Answer 18

Y-Y’
The error of prediction is the difference between the actual and predicted values of Y. This difference is also known as the residual.

Question 19

Sources of error (3)

Answer 19

1. Measurement: Very few variables can be measured with perfect accuracy
2. Sampling – the sample will never be exactly like the population
3. Uncontrolled variation – uncontrolled variables may “disturb” the relationship between the IV and DV

Question 20

What is multiple regression?

Answer 20

combining multiple IVs (predictors) to calculate what the DV should be.

Question 21

The omnibus F

Answer 21

Overall test of the significance of the model.

Question 22

B weights

Answer 22

unstandardized regression coefficients, represent the slope weight for each variable in the model and used to create the regression equation.

Question 23

Beta weights

Answer 23

Standardized regression coefficient. They’re based on z scores with a mean of 0 and standard deviation of 1

Question 24

Multicollinearity

Answer 24

indicates that there are correlations between the predictor variables (IVs). We want them to be somewhat correlated, but if they’re too highly correlated there’s a problem

Question 25

Where on the SPSS output would you find how much variance in our DV is explained by all of our IVs?

Answer 25

R Square

Question 26

which intercept is the constant variable?

Answer 26

Y-intercept

Question 27

What does Levene’s Test tell us?

Answer 27

Whether we have homogeneity or heterogeneity of variance.

Question 28

What extra step do we take in calculating the estimated standard error of the difference between means?

Answer 28

Pooled variance estimate. Because we are dealing with two samples from two different populations, we need to combine their variances to get one value for the denominator of the t-test.

Question 29

Identify the ways in which we can get dependent or paired samples?

Answer 29

Pre-post or matched pairs

Question 30

What is making the means of different groups vary? (3)

Answer 30

Individual differences Experimental error The IV

Question 31

The most basic type of ANOVA is called the one-way between groups design. Why?

Answer 31

Because we only have one IV (one-way) and we have separate, independent groups.

Question 32

ANOVA assumptions (3)

Answer 32

Independence: there is no relationship between the observations (scores) in the different groups and between the observations in the same group. Normality: the data are normally distributed. Can be checked by looking at skewness and kurtosis data. Equality of Variance: Can be checked by asking for the homogeneity of variance option in SPSS.

Question 33

What test would you use for an experiment with 3 groups of individual scores?

Answer 33

1-way ANOVA

Question 34

The F Ratio

Answer 34

Variance between groups / variance within groups or.. F = inherent variance + treatment effect inherent variance

Question 35

Within-Groups Variance

Answer 35

The sample variance for any group is used as an estimate of the inherent variance in that specific population.

Question 36

Post-Hoc Tests

Answer 36

Helps us find out what groups have a statistically significant difference

Question 37

Two-way (Factorial) ANOVA

Answer 37

has two independent variables.

Question 38

Why would it be a paired vs independent t-test?

Answer 38

Paired-samples t tests compare scores on two different variables but for the same group of cases; independent-samples t tests compare scores on the same variable but for two different groups of cases.

Question 39

What is residual?

Answer 39

The difference between predicted values of y (dependent variable) and observed values of y

Question 40

Coeffienct of determination (how to find it, what it means)

Answer 40

The coefficient of determination (R²) measures how well a statistical model predicts an outcome.

Question 41

Assumptions of parametric tests (2)

Answer 41

1. Normal distribution 2. Homogeneity of Variance

Question 42

what influences power? (3)

Answer 42

1. difference between group means 2. inherent variability 3. sample size

Question 43

what is a z-test?

Answer 43

statistical test to determine whether two population means are different when the variances are known and the sample size is large.

Question 44

what is a multiple regression test?

Answer 44

statistical technique that can be used to analyze the relationship between a single dependent variable and several independent variables keyword: PREDICT

Question 45

what is a chi square test?

Answer 45

to compare observed results with expected results. The purpose of this test is to determine if a difference between observed data and expected data is due to chance, or if it is due to a relationship between the variables you are studying.

Question 46

what is a t-test?

Answer 46

used to compare the means of two groups