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PHTH 7262 Practice Issues II > Statistics Lecture - Dr Wofford > Flashcards

Statistics Lecture - Dr Wofford Flashcards

1
Q

Linear Regression, Assumptions

A

Assumptions:

  • Predictor variable(s) (IV) must be quantitative (and continuous?)
  • Outcome variable (DV) must be quantitative, continuous
  • No perfect multicollinarity: predictor variables should not correlate highly (should not be the same?)
  • Outliers
    • Big problem with regression- must examine causal reasons

There are more assumptions, but these are outside the scope of this class

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1
Q

Predictor variable(s) is the same as

A

IV

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2
Q

What test can you do for multivariate? (more than one DV)

A

MANOVA (or MANCOVA if there is covariates)

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4
Q

Draw a comparison of parametric and nonparametric statistics chart.

A
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4
Q

what is a really common reason for an outlier?

A

mistake in data entry

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5
Q

What is a coefficient table?

A

Used in Linear Regression for prediciton statistics

Coefficient table shows how good each predictor value was. Usually there will be a relationship between strength of individual predictor variables and all of them as a whole (a model).

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6
Q

Do you want r2 to be high or low?

What value is acceptable?

A

High

0.5 is clinically ok

Great statistician will say 0.8 and above is good

Dr. Lake will probalby say 0.3 and above

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7
Q

Three types of Regression

A

•Types:

  1. •Linear regression
    • 1 continuous DV and 1 continuous IV
  2. •Multiple linear regression
    • 1 continuous DV and 1+ continuous IVs
  3. •Logistic regression
    • 1 categorical DV and 2+ IVs on any scale
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7
Q

Correlation coeeficients:

A

Correlation Coefficients are used to quantitatively describe relationship between the two variables in terms of strength and direction

  • Range from -1.00-0.00-+1.00
  • Are sensitive to sample size
  • With a large enough sample size, two variables will be statistically correlated, but may not be meaningful
  • (r) denotes a correlation coefficient
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8
Q

Correlations

A

BIGGEST THING IS THEY MUST BE BIVARIATIE: Can only ook at the correlation between two variables, ONLY TWO!!

  • •“What is the relationship between A and B?” is research question. Magnitude and strength of relationship.
    • •How do (x) and (y) relate to each other- bivariate correlation?
    • •Can be graphically displayed with scatter plot
  • •Can be applied to paired observations on two different occasions or to one variable measured on two different occasions
  • •Is not causal in nature- can state a relationship exists, but is not cause and effect
  • How strong is the relationship?
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10
Q

Linear Regression: Basics except assumptions

A
  • Examination of two variables, X and Y, that are linearly related
    • X= IV; Y= DV
  • Uses a scatter plot to assess for the linear regression line
    • Line which best describes the orientation of all data points in the scatter plot
    • If the data was perfectly correlated, all data points would fall along a straight line
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10
Q

What is a moderate correlation level?

A

Will give us an r and a p-value. Correlation and significance (likelihood it could have occurred by chance)

0.5 is a moderate correlation

0.01 p-value, means it is statistically significant and is more than could have happened by chance.

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12
Q

What is Coefficient of Determination (r2):

A

Used in Linear regression for correlations

percentage of the total variance in the Y scores which can be explained by the X scores

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13
Q

Parametric correlation coefficient

A

pearson’s r

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14
Q

How does the amount of predictor variables affect the required sample size?

A

The more predictor variables, the more people you need

Usually need about 30 subjects per predictor variable

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15
Q

MANOVA (and MANCOVA)

A

•MANOVA: Comparison of means between >2 groups when there is >1 DV

  • •Works best when DVs are moderately correlated (2 DVs on the same measurement scale)
  • •ie: 180 and 360 speeds for isokinetic ER- 2 DVs which are similar because they are on the same measure (isokinetic ER)

•Types:

  • •One way MANOVA: 1 IV, 1+ DV
  • •Factorial MANOVA: 1+ Ivs, 1+ DVs
  • •MANCOVA: 1 IV, 1+ DV, covariate
  • •Factorial MANCOVA: 1+ Ivs, 1+ DVs, covariate
  • •Doubly Multivariate: Repeated measures MANOVA
16
Q

Prediction statistics uses

A

regression tests

17
Q

What will correlation tests give us?

A

Will give us an r and a p-value. Correlation and significance (likelihood it could have occurred by chance)

For example:

  1. 5 is a moderate correlation
  2. 01 p-value, means it is statistically significant and is more than could have happened by chance.
19
Q

Outcome variable is the same as

A

DV

20
Q

Two most common types of correlations

A

Most common types of correlations (correlation coefficients):

  • Pearson product-moment coefficient of correlation (r)
  • Used when both variables (x and y) are continuus variables with underlying normal distributions on the interval or ratio scales
  • Can be subject to a test of significance to determine if the observed value could have occurred by chance
    • Will yield a p value which will be compared to the alpha level

•Spearman rank correlation coefficient (rs) or Spearman’s rho

  • •Nonparametric version of Pearson product-moment coefficient
  • •Used with ordinal data
  • •Also is subject to a test of significance and yields a p value
21
Q

Regression

A

Used for prediction statistics

  • Prediction of outcomes and characteristics
  • •Explains and predicts quantifiable clinical outcomes
  • •Types:
    • •Linear regression
      • •1 continuous DV and 1 continuous IV
    • •Multiple linear regression
      • •1 continuous DV and 1+ continuous IVs
    • •Logistic regression
      • •1 categorical DV and 2+ IVs on any scale
22
Q

Linear Regression, what you get out of it:

A
  • Coefficient of Determination (r2): percentage of the total variance in the Y scores which can be explained by the X scores
  • Measure of proportion (ranges from 0-1.00)
  • Regression output: (page 558)
    • Model Summary table: provides r2
    • ANOVA table: assess the model as a whole to see if the regression model is good (how good is that regression model)
    • Coefficent table: individual predictors have beta weights (B)- how important of a predictor variable they are
      • T stat in the coefficient table assesses the significance of each predictor variable
      • T stat and F stat will be similar (both non significant or significant) if there is only one IV
23
Q

nonparametric correlation coefficient

A

spearman’s rho

PHTH 7262 Practice Issues II (10 decks)

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