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Stats Final > Stats Final > Flashcards

Stats Final Flashcards

1
Q

Parsimonious

A

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

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2
Q

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

A

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.

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3
Q

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

A

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

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4
Q

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

A

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.

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5
Q

What is the error or residual in regression analysis?

A

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

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6
Q

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

A

Y-prime = a + bX

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7
Q

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

A

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

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8
Q

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

A

Variance

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9
Q

How is the variance partitioned in ANOVA?

A

Between and within groups

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10
Q

What is another word for groups in statistics-speak?

A

Levels

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11
Q

Why Conduct Correlational Research? (4)

A

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

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12
Q

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

A

Post-hoc test

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13
Q

What is covariance?

A

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

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14
Q

Assumptions of Linear Correlation (3)

A
  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.
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15
Q

What does the correlation coefficient help us do?

A

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.

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16
Q

Simple bivariate regression

A

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

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17
Q

Difference between correlation and regression?

A

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

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18
Q

Error equals?

A

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.

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19
Q

Sources of error (3)

A
  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
20
Q

What is multiple regression?

A

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

21
Q

The omnibus F

A

Overall test of the significance of the model.

22
Q

B weights

A

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

23
Q

Beta weights

A

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

24
Q

Multicollinearity

A

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

25
Q

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

A

R Square

26
Q

which intercept is the constant variable?

A

Y-intercept

27
Q

What does Levene’s Test tell us?

A

Whether we have homogeneity or heterogeneity of variance.

28
Q

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

A

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.

29
Q

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

A

Pre-post or matched pairs

30
Q

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

A

Individual differences
Experimental error
The IV

31
Q

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

A

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

32
Q

ANOVA assumptions (3)

A

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.

33
Q

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

A

1-way ANOVA

34
Q

The F Ratio

A

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

35
Q

Within-Groups Variance

A

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

36
Q

Post-Hoc Tests

A

Helps us find out what groups have a statistically significant difference

37
Q

Two-way (Factorial) ANOVA

A

has two independent variables.

38
Q

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

A

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.

39
Q

What is residual?

A

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

40
Q

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

A

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

41
Q

Assumptions of parametric tests (2)

A
  1. Normal distribution
  2. Homogeneity of Variance
42
Q

what influences power? (3)

A
  1. difference between group means
  2. inherent variability
  3. sample size
43
Q

what is a z-test?

A

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

44
Q

what is a multiple regression test?

A

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

45
Q

what is a chi square test?

A

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.

46
Q

what is a t-test?

A

used to compare the means of two groups

Stats Final (4 decks)

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