Descriptive Statistics

Question 1

Define central tedency

Answer 1

the tendency for the values of a random variable to cluster round its mean, mode, or median.

Question 2

Define mean, median and mode

Answer 2

mean - average
median - middle value of data set
mode - most common number

Question 3

what are the 4 measures of variability

Answer 3

• Standard deviation
  
  • Interquartile range
  • Confidence intervals
  • Z - scores

Question 4

Define standard deviation

Answer 4

The dispersion of values around the mean

Question 5

Define interquartile range

Answer 5

which is the difference between the first and third quartiles.

Question 6

Define confidence intervals

Answer 6

a range of values so defined that there is a specified probability that the value of a parameter lies within it.

Question 7

Define Z - scores

Answer 7

A z-score describes the position of a raw score in terms of its distance from the mean when measured in standard deviation units

Question 8

What is a high and low standard deviation

Answer 8

Low standard deviation means data are clustered around the mean, and

high standard deviation indicates data are more spread out.

Question 9

Define correlation

Answer 9

Correlation is a statistical measure that expresses the extent to which two variables are linearly related

Question 10

Define regression

Answer 10

a measure of the relation between the mean value of one variable (e.g. output) and corresponding values of other variables

Question 11

Defije multiple regression

Answer 11

explains the relationship between multiple independent or predictor variables and one dependent or criterion variable

Question 12

Define p value

Answer 12

• P-value is the probability that a random chance generated the data or something else that is equal or rarer
• P value is a number between 0 and 1

Question 13

What is the P value threshold for statistical significance

Answer 13

• Threshold for statistical significance is most commonly <0.05

Question 14

Lower the P value = what

Answer 14

Greater amount of statistical significance

Question 15

What does a P value of 0.05 denote

Answer 15

5% probability that the results happened by chance

Question 16

Define linear regression

Answer 16

Linear regression expresses the relationship of two variables by fitting a linear equation to observed data

Question 17

Explain what a linear regression graph will look like for each value

R = 0
R = -1.0
R = +1.0
R = +0.06

Answer 17

R = 0 will result in a circle on data plot

R = -1.0 will result in a diagonal line from top left to bottom right with dots along line

R = +1.0 will result in a diagonal line from bottom left to top right with dots along line

R = +0.6 will result in diagonal line from bottom left to top right but dots are spread a bit away from line (Same for -0.6 but different direction)

Question 18

Define pearsons correlation

Answer 18

• Pearson’s correlation coefficient is the test statistics that measures the statistical relationship, or association, between two continuous variables.

Question 19

Define high, medium and low degree of correlation for r

Answer 19

• High degree: If the coefficient value lies between ± 0.50 and ± 1, then it is said to be a strong correlation.
  
  • Moderate degree: If the value lies between ± 0.30 and ± 0.49, then it is said to be a medium correlation.
  • Low degree: When the value lies below + . 29, then it is said to be a small correlation.

Question 20

What does correlation not tell u

Answer 20

does not tell you whether one variable causes the other

Question 21

Define and explain regression equation

Answer 21

• Y = bX + C
  
  • Y is the dependent variable
  • X is the independent variable
  • B is the slope or regression coefficient
  • C is the intercept of the Y axis

Question 22

Define forced entry regression

Answer 22

Produce one R value

Question 23

Define stepwise regression

Answer 23

Produce one or more R values for variables that explain variance

Question 24

Define hierarchical regression

Answer 24

Produces R values at each step

Question 25

Define bivariate regression

Answer 25

analysing two variables to establish the strength of the relationship between them.

Question 26

Define degrees of freedom

Answer 26

• Number of individual scores that are free to vary without changing the means

Question 27

Define homogeneity of variance

Answer 27

• The spread of scores around each mean is approximately equal

Question 28

What is used to determine where the difference are

Answer 28

Post Hoc

Question 29

A significant what will tell you there is a difference between groups

Answer 29

F-ratio

Question 30

What is Benferroni correction and when to use it

Answer 30

The Bonferroni correction adjusts probability (p) values because of the increased risk of a type I error when making multiple statistical tests

• The p value is divided by the number of tests

Question 31

Define outliers

Answer 31

Observations that are distant or distinct from the other observations in a dataset

Question 32

Define skewness

Answer 32

Measure of the asymmetry of a distribution

Question 33

Describe positive and negative skew

Answer 33

• Positive skew, more data on the left side

- Negative skew, more data on the right side

Question 34

Define leptokurtic

Answer 34

When scores either side are very close together

- Resulting in sharp thin bell curve

Question 35

Define platykurtic

Answer 35

• Flat wide bell curve

- Data are far either side

Question 36

what range should skewness and kurtosis values fall

Answer 36

-1.0 to +1.0

Question 37

Define Mesokurtic

Answer 37

Distributions that are moderate in breadth and curves with a medium peaked height.

Question 38

What are the three types of kurtosis

Answer 38

leptokurtic, mesokurtic, platykurtic

Question 39

Define kurtosis

Answer 39

the sharpness of the peak of a frequency-distribution curve.

Question 40

What is done to a variable during bivariate regressio

Answer 40

Squared

Question 41

Bivariate regression can be used for what two things

Answer 41

1. Assess the shared variance between two variables

2. Predicted a value on one variable, using the value of another variable

Question 42

Multiple regression equation

Answer 42

• Y = bX1 + bX2 + bX3…… + c