Module 2/5, Descriptive Stats/Normal Prob. Distrib.

Question 1

Difference between histograms and bar graphs

Answer 1

Histograms
Bar graph, but all bars touch each other
Represent continious nume2rical data organized intoc lasses
Bar Graph
BARS DON’T TOUCH EACH OTHER

Question 2

Unimodal, bimodal, multimodal

Answer 2

Unimodal: one mode
Bimodal: two modes
Multimodal: three or more

Question 3

Floor vs. ceiling effect

Answer 3

Ceiling effect: occurs when scores pile up against some upper limit, resulting in a negative skew (EXAM IS TOO EASY)
Floor Effect: occurs when scores pile up against some lower limit, resulting in a positive skew (MOST VALUES FALL NEGATIVE, LIKE A BADLY WRITTEN EXAM)

Question 4

Why is it needed ton square all the deviations before adding to get the sum of squareS?

Answer 4

TWO REASONS FOR SQUARING THE DEVAITIONS USED IN CALCULATING THE VARIANCE: squaring “Weights” the value in favor of the larger deviations, increasing the effect of outliers in the distribution, squaring makes all the values positive

Question 5

Features of a normal distribution

Answer 5

Unimodal
Bell-curve
Follows empirical rule
Symmetric about the mean
The proportion of the areas between the standard deviations are known
Area is greatest in the center
Mean = median = mode
Total area under the curve = 1.00
Approaches but NEVER TOUCHES THE X AXIS
AS STANDARD DEVIATION INCREASES, SO DOES THE SPREAD OF THE DISTRIBUTION’S NORMAL CURVE

Question 6

Draw score at random on norm distribution?

Answer 6

If you were to draw a score at random, we would be more likely to get a score nearer to the middle/mean/ rather than out towards the tails

Question 7

Negatively vs. Positively Skewed

Answer 7

Negatively Skewed: Mean is closest to tail, then median, then mode
Positively Skewed: mean is closest to the tail, then median, then mode

Question 8

Empirical Rule

Answer 8

68% falls within 1 SD
95% falls within 2 SD
99.7% falls within 3 SD

Question 9

Central Limit Theorum

Answer 9

Only need to know the mean and SD of the population of scores the means from from, and the sample size n
nmust be bigger than 30 to be able to use it
The distribution of sample means approaches a normal curve as n increases to infinity
The mean of the distribution of sample means has the same value as the mean of the known population
The standard error of the mean is the SD of the known population divided by the square root of the sample size n
Gives the shape, central tendency, and variability