Brainscape's Knowledge GenomeTM


Top Flashcards (Certified)
All Flashcards

PSYC 480, Intermediate Statistics > Chapter 3 > Flashcards

Chapter 3 Flashcards

study

1
Q

Linear Regression Predicts

A

value of a variable based on the value of another variable

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
2
Q

How do you know your dealing with linear regression?

A
  • Outcome variable
  • Predictor variable
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
3
Q

multiple regression

A

two or more predictor variables

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
4
Q

Linear Equation

A

Y = bX+a

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
5
Q

In linear equations,

A

X and y are variables
a and b are fixed constants

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
6
Q

The regression analysis in a linear equation is how we get

A

a and b are fixed constants

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
7
Q

In a linear equation, b is

A

the slope- how much Y changes when X is increased by 1 point.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
8
Q

In a linear equation, a is

A

the Y-intercept- determines the value of Y when X = 0.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
9
Q

Regression is

A

a method of finding an equation describing the best-fitting line for a set of data.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
10
Q

The best fit line for the actual data is one that

A

minimizes prediction errors

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
11
Q

y-hat is

A

value of Y predicted by regression equations

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
12
Q

(Y- Y hat) is

A

Error of prediction

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
13
Q

(Y- Y hat) is a method called

A

the least-squared-error solution

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
14
Q

Using Regression for Prediction

A

be cautious when interpreting predicted values

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
15
Q

When using Regression for prediction

A

do not use the regression equation to make predictions outside the existing range of X values

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
16
Q

When it comes to using regression for prediction, you can only

A

predict within existing range of X values. The regression equation may change outside the existing range of X values

17
Q

To test the regression significance, you use

A

Analysis of regression

18
Q

Analysis of Regression

A

H0: the slope of the regression line (b) is zero

H1: at least one predictor has a slope (b) significantly different from zero.

Anova table tells you if regression equation (model) is significant.

19
Q

Multiple Regression Assumptions

A
  • Must be a linear relationship between two variables
  • Homoscedasticity
  • Residuals (errors) of the regression line approximately normally distributed
  • No multicollinearity
20
Q

To check for linear relationship with

A

scatter plot matrix

21
Q

Graphs with scatter plot matrix

22
Q

Dialogs with scatter plot matrix

A

scatter/dot…

23
Q

Interpreting results for a multiple linear regression is to report

A
  • Type of test (multiple linear regression)
  • Predictor & Outcome variables
    regression line equation
  • whether the model was ststistically significant (report F-test/ANOVA)
  • Which predictors were significant (slope/beta/B)
  • R^2
24
Q

Linear Regression is the setup after correlation that

A

Predict value of a variable based on the value of another variable
- Outcome variable
- Predictor variable
Uses the equation
Multiple Regression

25
Introduction to Correlation
Measures and describes the relationship between two variables - no manipulation - must be measured on interval/ratio scale Characteristics of relationship - Direction (negative or positive; indicated by the sign, + or – of the correlation coefficient) - Form (linear is most common) - Strength or consistency (varies from 0 to 1)
26
In a Correlation, the direction can be
positive- both variables moving in the same direction negative- variable move in opposite directions
27
In a Correlation, the strength can be
- Closer to 0, the weaker the correlation - Closer tp -/+ the stronger the correlation
28
Examples of Correlations
- The relationship between income and happiness. - The relationship between stress levels and hours worked per week, - The relationship between family income and student GPA.
29
What does the Pearson Correlation do?
Measures the degree and the direction of the linear relationship between two variables - Not appropriate for curvilinear relationships Perfect linear relationship - Every change in X has a corresponding change in Y - Correlation will be –1.00 or +1.00
30
The Pearson Correlation equation
r = covariability of X and Y/ variability of X and Y separately
31
Correlations used for:
- Prediction - Validity - Reliability - Theory verification
32
Interpreting Correlations
- Correlation does NOT equal causation - Establishing causation requires an experiment where one variable is manipulated and others carefully controlled. - The dangers of interpreting correlation.
33
Correlations & Restricted Range of Scores
-Correlation value is affected by range of scores in the data Severely restricted range may provide a very different correlation than a broader range of scores - Floor & Ceiling Effects Never generalize a correlation beyond the sample range of data
34
Correlations and Outliers
Characterized by much larger (or smaller) score than others. in sample outliers have disproportionately large impact on correlation coefficient Clearly recognizable in a scatter plot
35
Correlation and Strength of Relationships
A correlation coefficient measures the degree of relationship on a scale from 0 to +1.00 - 100% predictability It is easy to mistakenly interpret this decimal number as a percent or proportion - Correlation is not a proportion
36
Squared correlation
May be interpreted as proportions of shared variability is called the coefficient of determination
37
Coefficient of determination measures
proportion of variability in one variable that can be determined from the relationship with the other variable (shared variability)
38
Hypothesis Tests With Pearson Correlation
Pearson’s r can be used with hypothesis testing, to determine whether the r is statistically significant. Ho: There is no relationship between the two variables. H1 : There is a relationship between the two variables. (nondirectional/two-tailed) H1 : There is a negative relationship between the two variables. (directional/one-tailed)
39
Interpreting results for a Correlations test
Report - Statistical significance - Effect size (weak, moderate, strong) - Practical Significance (discussion) Test results - Type of test and variables - Value of correlation (sign & value) - df (n-2) - p-value/significance level

PSYC 480, Intermediate Statistics (1 decks)

Brainscape helps you reach your goals faster, through stronger study habits.
© 2025 Bold Learning Solutions. Terms and Conditions