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PSYC 480, Intermediate Statistics > Chapter 3 > Flashcards

Chapter 3 Flashcards

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1
Q

Linear Regression Predicts

A

value of a variable based on the value of another variable

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2
Q

How do you know your dealing with linear regression?

A
  • Outcome variable
  • Predictor variable
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3
Q

multiple regression

A

two or more predictor variables

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4
Q

Linear Equation

A

Y = bX+a

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5
Q

In linear equations,

A

X and y are variables
a and b are fixed constants

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6
Q

The regression analysis in a linear equation is how we get

A

a and b are fixed constants

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7
Q

In a linear equation, b is

A

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

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8
Q

In a linear equation, a is

A

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

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9
Q

Regression is

A

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

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10
Q

The best fit line for the actual data is one that

A

minimizes prediction errors

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11
Q

y-hat is

A

value of Y predicted by regression equations

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12
Q

(Y- Y hat) is

A

Error of prediction

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13
Q

(Y- Y hat) is a method called

A

the least-squared-error solution

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14
Q

Using Regression for Prediction

A

be cautious when interpreting predicted values

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15
Q

When using Regression for prediction

A

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

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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

A

legacy

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
Q

Introduction to Correlation

A

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
Q

In a Correlation, the direction can be

A

positive- both variables moving in the same direction
negative- variable move in opposite directions

27
Q

In a Correlation, the strength can be

A
  • Closer to 0, the weaker the correlation
  • Closer tp -/+ the stronger the correlation
28
Q

Examples of Correlations

A
  • 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
Q

What does the Pearson Correlation do?

A

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
Q

The Pearson Correlation equation

A

r = covariability of X and Y/ variability of X and Y separately

31
Q

Correlations used for:

A
  • Prediction
  • Validity
  • Reliability
  • Theory verification
32
Q

Interpreting Correlations

A
  • 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
Q

Correlations & Restricted Range of Scores

A

-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
Q

Correlations and Outliers

A

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
Q

Correlation and Strength of Relationships

A

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
Q

Squared correlation

A

May be interpreted as proportions of shared variability
is called the coefficient of determination

37
Q

Coefficient of determination measures

A

proportion of variability in one variable that can be determined from the relationship with the other variable (shared variability)

38
Q

Hypothesis Tests With Pearson Correlation

A

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
Q

Interpreting results for a Correlations test

A

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)

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